Thinking about the Future 3 - Principles PDF

Thinking about the Future
WIP Draft version 3.0
THINKING ABOUT THE FUTURE. The way that we think about the
future must mirror how the future actually unfolds. As we have all learned
from recent experience, the future is not a simple extrapolation of linear,
single-domain trends. We now have to consider ways in which the
possibility of random, chaotic and radically disruptive events may be
factored into enterprise strategy development, threat assessment and risk
management frameworks and incorporated into enterprise decision-
making structures and processes.

Abiliti: Future Systems
• Abiliti: Origin Automation is part of a global consortium of Digital Technologies Service
Providers and Future Management Strategy Consulting firms for Digital Marketing and
Multi-channel Retail / Cloud Services / Mobile Devices / Big Data / Social Media
• Graham Harris Founder and MD @ Abiliti: Future Systems
– Email: (Office)
– Telephone: (Mobile)
• Nigel Tebbutt 奈杰尔泰巴德
– Future Business Models & Emerging Technologies @ Abiliti: Future Systems
– Telephone: +44 (0) 7832 182595 (Mobile)
– +44 (0) 121 445 5689 (Office)
– Email: Nigel-Tebbutt@hotmail.com (Private)
• Ifor Ffowcs-Williams CEO, Cluster Navigators Ltd & Author, “Cluster Development”
– Address : Nelson 7010, New Zealand (Office)
– Email : e4@clusternavigators.com
Abiliti: Origin Automation Strategic Enterprise Management (SEM) Framework ©
Cluster Theory - Expert Commentary: -

Slow is smooth, smooth is fast.....
.....advances in “Big Data” have lead to a revolution in
futures studies, forecasting and predictive modelling – but
it takes both human ingenuity, and time, for long-range
Models of the Future to develop and mature.....

At the very Periphery of Corporate
Vision and Awareness…..
• The Cosmology Revolution – new and exciting advances in Astrophysics and
Cosmology (String Theory and Wave Mechanics) is leading Physicists towards new
questions and answers concerning the make-up of stellar clusters and galaxies, stellar
populations in different types of galaxy, and the relationships between high-stellar
populations and local clusters. What are the implications for galactic star-formation
histories and relative stellar formation times – overall, resolved and unresolved – and
their consequent impact on the evolution of life itself ?.
• The Quantum Revolution – The quantum revolution could turn many ideas of science
fiction into science fact - from meta-materials with mind-boggling properties such as
invisibility, limitless quantum energy via room temperature superconductors an
onwards and upwards to Arthur C Clarke's space elevator. Some scientists even
forecast that in the latter half of the century everybody will have a personal fabricator
that re-arranges molecules to produce everything from almost anything. How
ultimately will we use this gift? Will we have the wisdom to match our mastery of
matter like Solomon? Or will we abuse our technology strength and finally bring down
the temple around our ears like Samson?
• The Nano-Revolution – To meet the challenges in an ever more resource-limited
world, innovation and technology must play an increasing role. Nanotechnology, the
engineering of matter at the atomic scale to create materials with unique properties
and capabilities, will play a significant part in ensuring that risks to critical water
resources for future cities are addressed. Nanotechnology “has the potential to be a
key element in providing effective, environmentally sustainable solutions for supplying
potable water for human use and clean water for agricultural and industrial uses.”

• The Energy Revolution • Oil Shale • Kerogen • Tar Sands • Methane Hydrate • The
Hydrogen Economy • Nuclear Fusion • Every year we consume the quantity of Fossil
Fuel energy which took nature 3 million tears to create. Unsustainable fossil fuel energy
dependency based on Carbon will eventually be replaced by the Hydrogen Economy
and Nuclear Fusion. The conquest of hydrogen technology, the science required to
support a Hydrogen Economy (to free up humanity from energy dependency) and
Nuclear Fusion (to free up explorers from gravity dependency) is the final frontier which,
when crossed, will enable inter-stellar voyages of exploitation across our Galaxy.
• Nuclear Fusion requires the creation and sustained maintenance of the enormous
pressures and temperatures to be found at the Sun’s core This is a most challenging
technology that scientists here on Earth are only now just beginning to explore and
evaluate its extraordinary opportunities. To initiate Nuclear Fusion requires creating the
same conditions right here on Earth that are found the very centre of the Sun. This
means replicating the environment needed to support quantum nuclear processes which
take place at huger temperatures and immense pressures in the Solar core – conditions
extreme enough to overcome the immense nuclear forces which resist the collision and
fusion of two deuterium atoms (heavy hydrogen – one proton and one neutron) to form a
single Helium atom – accompanied by the release of a vast amount of Nuclear energy.

• Renewable Resources • Solar Power • Tidal Power • Hydro-electricity • Wind
Power • The Hydrogen Economy • Nuclear Fusion • Any natural resource is a
renewable resource if it is replenished by natural processes at a rate compatible
with or faster than its rate of consumption by human activity or other natural uses
or attrition. Some renewable resources - solar radiation, tides, hydroelectricity,
wind – can also classified as perpetual resources, in that they can never be
consumed at a rate which is in excess of their long-term availability due to natural
processes of perpetual renewal. The term renewable resource also carries the
implication of prolonged or perpetual sustainability for the absorption, processing or
re-cycling of waste products via natural ecological and environmental processes.
• For the purposes of Nuclear Fission, Thorium may in future replaced enriched
Uranium-235. Thorium is much more abundant, far easier to mine, extract and
process and far less dangerous than Uranium. Thorium is used extensively in
Biomedical procedures, and its radioactive decay products are much more benign.
• Sustainability is a characteristic of a process or mechanism that can be
maintained indefinitely at a certain constant level or state – without showing any
long-term degradation, decline or collapse.. This concept, in its environmental
usage, refers to the potential longevity of vital human ecological support systems -
such as the biosphere, ecology, the environment the and man-made systems of
industry, agronomy, agriculture, forestry, fisheries - and the planet's climate and
natural processes and cycles upon which they all depend.

• Trans-humanism – advocates the ethical use of technology to extend current
human form and function - supporting the use of future science and technology to
enhance the human genome capabilities and capacities in order to overcome
undesirable and unnecessary aspects of the present human condition.
• The Intelligence Revolution – Artificial Intelligence will revolutionise homes,
workplaces and lifestyles. Augmented Reality will create new virtual worlds –
such as the interior of Volcanoes or Nuclear Reactors, the bottom of the Ocean or
the surface of the Moon, Venus or Mars - so realistic they will rival the physical
world. Robots with human-level intelligence may finally become a reality, and at
the ultimate stage of mastery, we'll even be able to merge human capacities with
machine intelligence and attributes – via the man-machine interface.
• The Biotech Revolution – Genome mapping and Genetic Engineering is now
bringing doctors and scientists towards first discovery, and then understanding,
control, and finally mastery of human health and wellbeing. Digital Healthcare
and Genetic Medicine will allow doctors and scientists to positively manage
successful patient outcomes – even over diseases previously considered fatal.
Genetics and biotechnology promise a future of unprecedented health, wellbeing
and longevity. DNA screening could diagnose and gene therapy prevent or cure
many diseases. Thanks to laboratory-grown tissues and organs, the human body
could be repaired as easily as a car, with spare parts readily available to order.
Ultimately, the ageing process itself could ultimately be slowed or even halted.

• Global Massive Change is an evaluation of global capacities and limitations. It
includes both utopian and dystopian views of the emerging world future state, in
which climate, the environment, ecology and even geology are dominated by the
indirect impact of human activity and the direct impact of human manipulation: –
1. Human Impact is now the major factor in climate change, environmental and
ecological degradation.
2. Environmental Degradation - man now moves more rock and earth than do all
of the natural geological processes
3. Ecological Degradation – biological extinction rate - is currently greater than
that of the Permian-Triassic boundary (PTB) extinction event
4. Food, Energy, Water (FEW) Crisis – increasing scarcity of Natural Resources
• Society’s growth-associated impacts on its own ecological and environmental
support systems, for example intensive agriculture causing exhaustion of natural
resources by the Mayan and Khmer cultures, de-forestation and over-grazing
causing catastrophic ecological damage and resulting in climatic change – for
example, the Easter Island culture, the de-population of upland moors and
highlands in Britain from the Iron Age onwards – including the Iron Age retreat
from northern and southern English uplands, the Scottish Highland Clearances
and replacement of subsistence crofting by deer and grouse for hunting and
sheep for wool on major Scottish Highland Estates and the current sub-Saharan
de-forestation and subsequent desertification by semi-nomadic pastoralists

The Management of Uncertainty
A Brief History of Chaos…..
Mechanical Processes –
Thermodynamics (Complexity and Chaos Theory) – governs the behaviour of Systems
Classical Mechanics (Newtonian Physics) – governs the behaviour of all everyday objects
Quantum Mechanics – governs the behaviour of unimaginably small sub-atomic particles
Relativity Theory – governs the behaviour of impossibly super-massive cosmic structures
Wave Mechanics (String Theory) – integrates the behaviour of every size and type of object

Executive Summary - The
Management of Uncertainty
• It has long been recognized that one of the most important competitive factors for any
organization to master is the management of uncertainty. Uncertainty is the major
intangible factor contributing towards the risk of failure in every process, at every level,
in every type of business. The way that we think about the future must mirror how the
future actually unfolds. As we have learned from recent experience, the future is not a
straightforward extrapolation of simple, single-domain trends. We now have to consider
ways in which the possibility of random, chaotic and radically disruptive events may be
factored into enterprise threat assessment and risk management frameworks and
incorporated into decision-making structures and processes.
• Managers and organisations often aim to “stay focused” and maintain a narrow
perspective in dealing with key business issues, challenges and targets. A
concentration of focus may risk overlooking Weak Signals indicating potential issues
and events, agents and catalysts of change. Such Weak Signals – along with their
resultant Wild Card and Black Swan Events - represent early warning of radically
disruptive future global transformations – which are even now taking shape at the very
periphery of corporate awareness, perception and vision – or just beyond.

• There are many kinds of Stochastic or Random processes that impact on every area
of Nature and Human Activity. Randomness can be found in Science and Technology
and in Humanities and the Arts. Random events are taking place almost everywhere
we look – for example from Complex Systems and Chaos Theory to Cosmology and
the distribution and flow of energy and matter in the Universe, from Brownian motion
and quantum theory to fractal branching and linear transformations. There are further
examples – atmospheric turbulence in Weather Systems and Climatology, and system
dependence influencing complex orbital and solar cycles. Other examples include
sequences of Random Events, Weak Signals, Wild Cards and Black Swan Events
occurring in every aspect of Nature and Human Activity – from the Environment and
Ecology - to Politics, Economics and Human Behaviour and in the outcomes of current
and historic wars, campaigns, battles and skirmishes - and much, much more.
• These Stochastic or Random processes are agents of change that may precipitate
global impact-level events which either threaten the very survival of the organisation -
or present novel and unexpected opportunities for expansion and growth. The ability to
include Weak Signals and peripheral vision into the strategy and planning process may
therefore be critical in contributing towards the continued growth, success, wellbeing
and survival of both individuals and organisations at the micro-level – as well as cities,
states and federations at the macro-level - as witnessed in the rise and fall of empires.

Random Processes
• Random Processes may influence any natural and human phenomena, such as: -
– the history of an object
– the outcome of an event
– the execution of a process
• Randomness may be somewhat difficult to demonstrate, as true Randomness in chaotic
system behaviour is not always readily or easily distinguishable from any of the “noise”
that we may find in Complex Systems – such as foreground and background wave
harmonics, resonance and interference. Complex Systems may be influenced by both
internal and external factors which remain hidden – either unrecognised or unknown.
These hidden and unknown factors may exist far beyond our ability to detect them – but
nevertheless, still exert influence. The existence of weak internal or external forces acting
on systems may not be visible to the observer – these subliminal temporal forces can
influence Complex System behaviour in such a way that the presence of imperceptibly tiny
inputs, acting on a system, amplified in effect over many system cycles - are ultimately
able to create massive observable changes to outcomes in complex system behaviour.

• Uncertainty is the outcome of the disruptive effect that chaos and randomness
introduces into our daily lives. Research into stochastic (random) processes looks
towards how we might anticipate, prepare for and manage the chaos and uncertainty
which acts on complex systems – including natural systems such as Cosmology and
Climate, as well as human systems such as Politics and the Economy – so that we may
anticipate future change and prepare for it…..
1. Classical Mechanics - Any apparent randomness is as a result of Unknown Forces
2. Thermodynamics - Randomness, chaos and uncertainty is directly a result of Entropy
3. Biology - Any apparent randomness is as a result of Unknown Forces
4. Chemistry - Any apparent randomness is as a result of Unknown Forces
5. Atomic Theory - All events are utterly and unerringly predictable (Dirac Equation)
6. Quantum Mechanics - Every event is both symmetrical and random (Hawking Paradox)
7. Geology - Any randomness or asymmetry is a result of Unknown Forces
8. Astronomy - Any randomness or asymmetry is a result of Unknown Forces
9. Cosmology - Any randomness or asymmetry is as a result of Dark Matter, Energy, Flow
10. Relativity Theory - Randomness or asymmetry may be a result of Quantum effects
11. Wave Mechanics - Any randomness and asymmetry is as a result of Unknown Forces

Domain Scope / Scale Randomness Pioneers
Classical Mechanics
(Newtonian Physics)
Everyday objects Any apparent randomness is as
a result of Unknown Forces
Sir Isaac Newton
Thermodynamics Entropy, Enthalpy Newcomen, Trevithick,
Watt, Stephenson
Biology Evolution Darwin, Banks, Huxley,
Krebs, Crick, Watson
Chemistry Molecules Lavoisier, Priestley
Atomic Theory Atoms Each and every Quantum event
is truly and intrinsically fully
symmetrical and random
Max Plank, Niels Bohr
Quantum Mechanics Sub-atomic particles Erwin Schrodinger ,
Werner Heisenberg,
Paul Dirac,
Richard Feynman

Geology The Earth, Planets,
Planetoids, Asteroids,
Meteors / Meteorites
Any apparent randomness is as
Hutton, Lyell, Wagner
Astronomy Common, Observable
Celestial Objects
Any apparent randomness or
asymmetry may be as a result
of Quantum effects or other
Unknown Forces acting early in
the history of Space-Time
Galileo, Copernicus,
Kepler, Lovell, Hubble
Cosmology Super-massive
Celestial Objects
Hoyle, Ryall, Rees,
Penrose, Bell-Burnell
Relativity Theory The Universe Any apparent randomness or
asymmetry is as a result of
Unknown Forces / Dimensions
Albert Einstein,
Hermann Minkowski,
Stephen Hawking
Wave Mechanics
(String Theory or
Quantum Dynamics)
The Universe,
Membranes and
Hyperspace
Michael Green,
Michio Kaku

• Classical Mechanics (Newtonian Physics)
– Classical Mechanics (Newtonian Physics) governs the behaviour of everyday objects
– any apparent randomness is as a result of unimaginably small, unobservable and
unmeasurable Unknown Forces - either internal or external - acting upon a System.
• Thermodynamics
– governs the flow of energy and the transformation (change in state) of systems
– randomness, chaos and uncertainty is the result of the effects of Enthalpy and Entropy
• Chemistry
– Chemistry (Transformation) governs the change in state of atoms and molecules
• Biology
– Biology (Ecology ) governs Evolution - the life and death of all living Organisms

• Atomic Theory
– governs the behaviour of unimaginably small objects (atoms and sub-atomic particles)
– all events are truly and intrinsically, utterly and unerringly predictable (Dirac Equation).
• Quantum Mechanics
– governs the behaviour of unimaginably tiny objects (fundamental sub-atomic particles)
– all events are truly and intrinsically both symmetrical and random (Hawking Paradox).
• Geology
– Geology governs the behaviour of local Solar System Objects (such as The Earth, Planets,
Planetoids, Asteroids, Meteors / Meteorites) which populate the Solar System
unmeasurable Unknown Forces - either internal or external - acting upon a System
• Astronomy
– Astronomy governs the behaviour of Common, Observable Celestial Objects (such as
Asteroids, Planets, Stars and Stellar Clusters) which populate and structure Galaxies
– any apparent randomness or asymmetry is as a result of Quantum Effects, Unknown
Forces or Unknown Dimensions acting very early in the history of Universal Space-Time

• Cosmology
– Cosmology governs the behaviour of impossibly super-massive cosmic building blocks
(such as Galaxies and Galactic Clusters) which populate and structure the Universe
– any apparent randomness or asymmetry is due to the influence of Quantum Effects,
Unknown Forces (Dark Matter, Dark Flow and Dark Energy) or Unknown Dimensions
• Relativity Theory
– Relativity Theory governs the behaviour of impossibly super-massive cosmic structures
• Wave Mechanics (String Theory or Quantum Dynamics)
– Wave Mechanics integrates the behaviour of every size and type of physical object
Forces or Unknown Dimensions acting on the Universe, Membranes or in Hyperspace

• The Temporal Wave is a novel and innovative method for Visual Modelling and Exploration of
Geospatial “Big Data” - simultaneously within a Time (history) and Space (geographic) context.
The problems encountered in exploring and analysing vast volumes of spatial–temporal
information in today's data-rich landscape – are becoming increasingly difficult to manage
effectively. In order to overcome the problem of data volume and scale in a Time (history) and
Space (location) context requires not only traditional location–space and attribute–space
analysis common in GIS Mapping and Spatial Analysis - but now with the additional dimension
of time–space analysis. The Temporal Wave supports a new method of Visual Exploration for
Geospatial (location) data within a Temporal (timeline) context.
• This time-visualisation approach integrates Geospatial (location) data within a Temporal
(timeline) data along with data visualisation techniques - thus improving accessibility,
exploration and analysis of the huge amounts of geo-spatial data used to support geo-visual
“Big Data” analytics. The temporal wave combines the strengths of both linear timeline and
cyclical wave-form analysis – and is able to represent data both within a Time (history) and
Space (geographic) context simultaneously – and even at different levels of granularity. Linear
and cyclic trends in space-time data may be represented in combination with other graphic
representations typical for location–space and attribute–space data-types. The Temporal Wave
can be used in roles as a time–space data reference system, as a time–space continuum
representation tool, and as time–space interaction tool.

• Randomness. Neither data-driven nor model-driven macro-economic or micro-economic
models currently available to us today - seem able to deal with the concept or impact of
Random Events (uncertainty). We therefore need to consider and factor in further novel
and disruptive (systemic) approaches which offer us the possibility to manage uncertainty.
We can do this by searching for, detecting and identifying Weak Signals – which are tiny,
unexpected variations or disturbances in system outputs – surprises – predicating the
possible existence of hidden data relationships which are masked or concealed within the
general background system “noise”. Weak Signals are caused by the presence of small
unrecognised or unknown forces acting on the system. Weak Signals in turn may indicate
the possible future appearance of emerging chaotic, and radically disruptive Wild Card or
Black Swan events beginning to form on the detectable Horizon – or even just beyond.
• Random Events must then be factored into Complex Systems Modelling. Complex
Systems interact with unseen forces – which in turn act to inject disorder, randomness,
uncertainty, chaos and disruption. The Global Economy, and other Complex Adaptive
Systems, may in future be considered and modelled successfully as a very large set of
multiple interacting Ordered (Constrained) Complex Systems - each individual System
loosely coupled with all of the others, and every System with its own clear set of rules and
an ordered (restricted) number of elements and classes, relationships and types.

Future Management
Research Philosophy
“Research philosophy is an over-arching term relating to the
development of knowledge - and understanding the nature of that
knowledge which is under development.....”
• Adapted from Saunders et al, (2009) •
Epistemology concerns the scope of what constitutes acceptable knowledge in a field of study.
Ontology is concerned with the nature of reality - and raises questions about assumptions

Research Philosophies
• This section aims to discuss Risk Research Philosophies in detail, in order to develop
a general awareness and understanding of the options - and to describe a rigorous
approach to Research Methods and Scope as a mandatory precursor to the full Risk
Research Design. Kvale (1996) and Denzin and Lincoln (2003) highlight how different
Research Philosophies can result in much tension amongst research stakeholders.
• When undertaking any research of either a Scientific or Humanistic nature, it is most
important to consider, compare and contrast all of the varied and diverse Research
Philosophies and Paradigms that are available to the researcher and supervisor -
along with their respective treatments of ontology and epistemology issues.
• Since Research Philosophies and paradigms often describe dogma, perceptions,
beliefs and assumptions about the nature of reality and truth (and knowledge of that
reality) - they can radically influence the way in which the research is undertaken,
from design through to outcomes and conclusions. It is important to understand and
discuss these contrasting aspects in order that approaches congruent to the nature
and aims of the particular study or inquiry in question, are adopted - and to ensure
that researcher and supervisor biases are understood, exposed, and mitigated.

Research Philosophies
• James and Vinnicombe (2002) caution that we all have our own inherent preferences
that are likely to shape our research designs and conclusions, Blaikie (2000) describes
these aspects as part of a series of choices that the researcher has to consider, and
demonstrates that this alignment that must connect choices made back to the original
Research Problem. If this is not achieved, then certain research methods may be
adopted which turn out to be incompatible with the researcher’s stance, and result in
the final work being undermined through lack of coherence and consistency.
• Blaikie (1993) argues that Research Methods aligned to the original Research Problem
are highly relevant to Social Science since the humanistic element introduces a
component of “free will”’ that adds a complexity beyond those usually encountered in
the natural sciences – whilst others, such as Hatch and Cunliffe (2006) draw attention
to the fact that different paradigms ‘encourage researchers to study phenomena in
different ways’, going on to describe a number of organisational phenomena from three
different perspectives, thus highlighting how different kinds of knowledge may be
derived through observing the same phenomena from different philosophical
viewpoints and perspectives.

Aspects of Research Philosophy
• Rationalism – “blue-sky” pure research - the stance of the natural scientist
– Rationalism can be defined as “probabilistic research approaches that employ forensic and
analytical methods, make extensive use of both qualitative and quantitative analysis - free
from any pre-determined behavioral models - in order to discover hidden or unknown truths”
• Positivism – goal seeking - the stance of the applied scientist
– Positivism can be defined as “deterministic research approaches that employ empirical
methods, and make extensive use of quantitative analysis, or develop logical calculi in order to
develop hypotheses and build conceptual models in support of formal explanatory theory”
• Realism – direct and critical realism
– The essence of realism is that what the senses show us as reality is the truth; that objects
have an existence independent of the human mind.
• Interpretation – researchers as ‘social actors’
– Interpretation advocates the necessity for researchers to understand differences between
humans in our role as social actors.
• Pragmatism – studies judgements about value
– Pragmatism holds that the most important determinant of the epistemology, ontology,
axiology adopted is the research question

Probabilistic v. Deterministic Domains
Deterministic
Probabilistic
Rationalism
Positivism Gnosticism, Sophism
Scepticism
Dogma
Enlightenment
Pragmatism
Realism
Social Sciences
Sociology
Economics
Business Studies / Administration / Strategy
Psychology / Psychiatry / Medicine / Surgery
Behavioural Research Domains
Arts and the Humanities
Life Sciences
HistoryArts LiteratureReligion
Law PhilosophyPolitics
Biological basis of Behaviour
Biology Ecology Anthropology and Pre-history
Clinical Trials / Morbidity / Actuarial Science
“Goal-seeking” Empirical Research Domains
Applied (Experimental) Science
Earth Sciences
Economic Analysis
Classical Mechanics (Newtonian Physics)
Applied mathematics
Geography
Geology
Chemistry
Engineering
Geo-physics Environmental Sciences
Archaeology
Palaeontology
“Blue Sky” – Pure Research Domains
Future Management
Pure (Theoretical) Science
Quantitative Analysis
Computational Theory / Information Theory
Astronomy
Cosmology
Relativity
Astrophysics
Astrology
Taxonomy and Classification
Climate Change
Complex and Chaotic Research Domains
Narrative (Interpretive) Science
Statistics
Strategic Foresight
Data Mining“Big Data” Analytics
Cluster Theory
Pure mathematics
Particle Physics
String Theory
Quantum Mechanics
Complex Systems – Chaos Theory
Futures Studies
Weather ForecastingPredictive Analytics
Reaction
Stoicism

DETERMINISTIC PHILOSOPHIC PARADIGMS
GOVERNING AUTHORITIES tend to be Deterministic in nature - 12 Deterministic Paradigms.....
DETERMINISTIC PHILOSOPHIC PARADIGMS
• Utopian (Idealistic) Paradigm - Strategic Positivism
• Humanist (Instructional) Paradigm - Sceptic Paradigm
• Dogmatic (Theosophical) Paradigm - Reactionary Paradigm
• Utilitarian (Consequential) Paradigm – Egalitarian Paradigm
• Extrapolative (Projectionist) Paradigm – Wave, Cycle, Pattern and Trend Analysis
• Steady State (La meme chose - same as it ever was) Paradigm – Constant Paradigm
• Hellenistic (Classical) Paradigm – Human Ethics, Morals, Values and Beliefs
• Pre-ordained (Pre-disposed, Stoic) Paradigm - Cognitive Analysis / Intuitive Assimilation
• Elitism (New World Order) - Goal Seeking, Leadership Studies and Stakeholder Analysis
• Existentialist Paradigm (Personal Futures) - Trans-humanism, The Singularity, NLP / EHT
• Empirical (Scientific Determinism, Theoretical Positivism) Paradigm – Hypothetical Paradigm
• Predictive (Ordered, Systemic, Mechanistic, Enthalpy) Paradigm – Deconstructive Paradigm

PROBABILISTIC PHILOSOPHIC PARADIGMS
NATURAL PHILOSOPHERS tend to be Probabilistic in nature - 12 Probabilistic Paradigms.....
PROBABILISTIC PHILOSOPHIC PARADIGMS
• Polemic (Rational) Paradigm - Enlightenment
• Dystopian (Fatalistic) Paradigm – Probabilistic Negativism
• Postmodernism (Reactionary) Paradigm - Structural Philosophy
• Complexity (Constructionist) Paradigm - Complex Systems and Chaos Theory
• Metaphysical (Naturalistic, Evolutionary, Adaptive) Paradigm - Gaia Hypothesis
• Mystic (Gnostic, Sophistic, Esoteric, Cathartic) Paradigm – Contemplative Paradigm
• Uncertainty (Random, Chaotic, Disorderly, Enthalpy) Paradigm – Disruptive Paradigm
• Experiential (Forensic, Deductive, Realist, “Blue Sky”) Paradigm – Pragmatism
• Qualitative (Narrative, Reasoned) Paradigm - Scenario Forecasting and Impact Analysis
• Simplexity (Reductionist) Paradigm – Loosely-coupled Linear Systems and Game Theory
• Interpretive (Ordered, Systemic, Mechanistic, Entropic) Paradigm – Constructive Paradigm
• Quantitative (Logical, Technical) Paradigm - Mathematical Modelling & Statistical Analysis

Ancient Philosophy
• 1 Ancient Chinese philosophy
– 2.1 Schools of thought
• 2.1.1 Hundred Schools of Thought
• 2.1.2 Early Imperial China
– 2.2 Philosophers
• 2 Ancient Greek and Roman philosophy
– 3.1 Philosophers
• 3.1.1 Presocratic philosophers
• 3.1.2 Classical Greek philosophers
• 3.1.3 Hellenistic philosophy
– 3.2 Hellenistic schools of thought
– 3.3 Early Roman and Christian philosophy
– 3.4 Philosophers during Roman times

Ancient Philosophy
• 4 Ancient Indian philosophy
– 4.1 Vedic philosophy
– 4.2 Sramana philosophy
– 4.3 Classical Indian philosophy
– 4.4 Ancient Indian philosophers
• 4.4.1 Philosophers of Vedic Age (2000–600 BCE)
• 4.4.2 Philosophers of Axial Age (600–185 BCE)
• 4.4.3 Philosophers of Golden Age (184 BCE – 600 CE)
• 5 Ancient Iranian philosophy
– 5.1 Schools of thought
– 5.2 Philosophy and the empire
– 5.3 Literature

Ancient Philosophy
• 6 Ancient Jewish philosophy
– 6.1 First Temple (c. 900 BCE to 587 BCE)
– 6.2 Assyrian exile (587 BCE to 516 BCE)
– 6.3 Second Temple (516 BCE to 70 CE)
– 6.4 Early Roman exile (70 CE to c. 600 CE)

Hellenistic Schools of Thought
• 1 Pre-Socratic philosophy
– 1.1 Milesian school
– 1.2 Xenophanes
– 1.3 Pythagoreanism
– 1.4 Heraclitus
– 1.5 Eleatic philosophy
– 1.6 Pluralism and atomism
– 1.7 Sophistry
• 2 Classical Greek philosophy
– 2.1 Socrates
– 2.2 Plato
– 2.3 Aristotle
• 3 Hellenistic philosophy
• 4 Transmission of Greek philosophy under Islam

Hellenistic Schools of Thought
1. Pythagoreanism
2. Sophism
3. Cynicism
4. Cyrenaicism
5. Platonism
6. Peripateticism
7. Pyrrhonism
8. Epicureanism
9. Stoicism
10. Eclecticism
11. Hellenistic Judaism
12. Neopythagoreanism
13. Hellenistic Christianity
14. Neoplatonism
Hellenistic schools of Philosophy and Thought

Philosophical Paradigms
Mystic (Hermetic)
Philosophy
Description Leading figures
mystic (hermetic)
philosophy
Related: -
Transcendentalism;
Perennial Philosophy;
Unitarian Universalism;
Gnosticism and the Kabala.
Mystic (Hermetic) Philosophy in which “secret”
or “hidden” knowledge is revealed to initiates in a
series of steps or “degrees”, in ritual practices or
religious rites based on Perennial Philosophy;
Transcendentalism and Unitarian Universalism
– are found in Jewish and Christian Gnosticism,
the Kabala and Moslem Sufism. The essence of
divination in mystic (hermetic) philosophy is the
belief that wisdom, knowledge or “spiritual truth”
can be gained through contemplative meditation –
trance-like deep thinking or prayer – in a state of
mystical union with the universal force, or in direct
communion with the supreme spiritual being – as
is described by mystic (hermetic) philosophers.
The Kybalion Hermetic Philosophy was published
anonymously in 1908 by an individual or group of
individuals under the pseudonym of "the Three
Initiates" – which they claimed to be the essential
distillation of the mystic philosophical teachings of
Hermes Trismegistus the “Thrice-Great” - which
is interpreted today as associated with learning or
wisdom transmitted to man from divine sources.
Hermes Trismegistus, Philo of
Alexandria, Flavius Josephus,
Michel Nostradamus.
Hermes Trismegistus. the “Thrice-
Great”. Hermes is interpreted as a
figure associated with learning or
wisdom transmitted to man from
divine sources. Hermes is the Greek
name for the Egyptian god Thoth or
Tehuti, the god of wisdom, learning
Philo of Alexandria, also called
Philo Judaeus, was a Hellenistic
Jewish philosopher who lived in
Alexandria, in the Roman province of
Egypt. Philo used philosophical
allegory to attempt to fuse and
harmonize Greek Stoic philosophy
with Jewish Kabala philosophy.

Mystic (Hermetic)
Philosophy
mystic (hermetic)
philosophy
Related: -
Transcendentalism;
Perennial Philosophy;
Unitarian Universalism;
Gnosticism and the Kabala.
Josephus was a priest, soldier, and scholar. He is
famous for his prophecies – and also for being the
most credible secular historian outside of the New
Testament to record the existence of Jesus Christ.
Prophetic visions of Nostradamus are contained
in 942 cryptic poems called The Centuries.
Nostradamus wrote four-line verses (quatrains) in
groups of 100 (centuries). They have enthralled
generation after generation of readers. He was
often referred to as the prophet of doom because
many of his visions involved conflict and death.
Titus Flavius Josephus, born
Joseph ben Matityahu, was a first-
century Romano-Jewish scholar,
historian and philosopher, born in
Jerusalem - part of Roman Judea -
to a father of priestly descent and a
mother who claimed royal ancestry
Michel de Nostredame, usually
Latinised as Nostradamus (14 or 21
December 1503 – 2 July 1566) was
from a Sephardic Jewish family who
had converted to Christianity. Born
in South-west France, the French
apothecary and reputed seer who
published “The Centuries” – a
collection of prophecies that have
since become famous worldwide.

Stoic
Philosophy
Stoic philosophy
Related: -
Stoicism and
Rationalism
“Throughout eternity,
all that is of like form
will come around
again – everything
that is the same must
always return in its
own everlasting
cycle.....”
“Look back over time,
with past empires
that in their turn rise
and fall – through
changing history you
may also see the
future.....”
Stoicism is a school of Hellenistic philosophy
founded in Athens by Zeno of Citium in the early 3rd
century BC. Stoic Philosophers taught that errors in
judgment resulted from destructive emotions, and that
a sage, or person of "moral and intellectual
perfection", would be entirely detached from any such
emotional impediments – and would therefore view
the world from an entirely Rational perspective..
Marcus Aurelius followed • Stoic Philosophy •
The Roman emperor Marcus Aurelius was perhaps
the only true philosopher-king in world history.
His Stoic tome Meditations, written in Greek while on
campaign between 170 and 180, is still revered as a
literary monument to a Stoic philosophy of service
and duty, describing how to find and maintain a
rational equanimity in the midst of conflict by studying
and understanding nature as a source of instruction,
guidance and inspiration.
Lucius Anna of accelerationeus
Seneca (c. 4 BC-65 AD), Statesman,
Stoic philosopher, and playwright
Rational Futurists view the world
from an entirely objective and
Rational perspective – devoid of any
subjective or emotional influences
• Marcus Aurelius • Emperor of Rome
Marcus Aurelius Latin: Marcus
Aurelius Antoninus Augustus; 26 April
121 AD –17 March 180 AD) was
chosen by Hadrian to be his eventual
successor. He was Roman Emperor
from 161 to 180, he ruled jointly with
Lucius Verus as his co-emperor from
161 until Verus' death in 169. He was
the last of the Five Good Emperors,
and is also considered one of the
most important Stoic philosophers.

Dystopian
Philosophy
Dystopian
philosophy
Dystopia: A futuristic, imaginary society, world or
universe that exerts oppressive citizen control - in
which a figurehead or concept is worshipped by
the unfortunate citizens of that dystopian society.
The prologue to Book I of Milton Paradise Lost
begins with Milton stating that his subject will be
Adam and Eve’s disobedience and fall from grace
in order to discourse about the fall of man and to
justify God's harsh ways of dealing with mankind.
Brave New World is a novel written in 1931 by
Aldous Huxley and published in 1932. Set in a
future London of AD 2540, the novel anticipated
the rise of Fascism in Europe – developments in
state-sponsored propaganda and terrorism, the
technology of warfare, centralised control of
sexual and reproductive functions, euthanasia,
psychological profiling and sleep-learning
The Dystopian view or viewpoint
In Futures Studies is based on
the upward trend in developing
global crises which threaten the
future of mankind – war, terrorism,
rebellion, insecurity and civil
disobedience, Climate Change,
Population Growth and the rapid
exhaustion of natural resources -
Food Energy and Water
John Milton. (1608–1674)
Paradise Lost - where demons
released to live in Pandemonium
wander – to prey on lost souls.
Brave New World is an unsettling,
loveless and even sinister place.
This is because Huxley endows his
"ideal" society with features and
characteristics calculated to
alienate his readers from this so-
called “Utopia”.

Utopian
Philosophy
Utopian
philosophy
Utopian philosophy – Utopia is an imagined
place or state of affairs in which everything is
perfect. The word Utopia is constructed from two
Greek words - TOPOS meaning PLACE and OU
meaning NO. Thus Utopia is "nowhere" or an
imaginary place. It is also a pun on the word EU
meaning good or perfect. So Utopia can also be a
seen as a perfect place - which is non-existent.
Sir Thomas Moore published Utopia in 1516 and
since then the word Utopia has become the
generic name for a whole genre of speculative
writing, philosophy and ideology - also being
retrospectively applied to works like Plato's
Republic, upon which Utopia is largely based.
Milton's last two poems were published in one
volume in 1671. Paradise Regained, a brief epic
in four books, was followed by Samson Agonistes
In Futures Studies, a Utopian
view or idealistic viewpoint is
founded on the false hope or
promise that future of mankind will
sort itself out, and everything is
going to be all right…..
Sir Thomas More (later canonized
St. Thomas a’ More) is famous for
both his book Utopia (1515) and
for his Catholic martyrdom when
he refused to acknowledge King
Henry VIII as head of the
Protestant Church, then failed to
sanction Henrys’ divorce of Queen
Catherine of Aragon.
John Milton. (1608–1674)
Paradise Regained

Philosophy Description Leading figures
Polemic philosophy Polemic comes from the Greek polemikos meaning
"warlike” or “belligerent” - a philosophy based on
confrontational argument or adversarial debate –
particularly when attacking an opposing doctrine or
refuting an opposite view, viewpoint or opinion - often
accompanied by controversial, misleading,
contentious or spurious statements.
Nietzsche and Voltaire are examples
of Polemic Philosophers.
Polemic Futures Studies is based
on the forceful arguments put forward
by the opponents and detractors of
Darwinian Evolution, Climate Change
Romantic
Philosophy –
Related: -
Epistemology
Romanticism
Natural Philosophy
Romantic Movement
The Romantic Movement emerged out of the
Romantic challenge to both the static, materialistic
views of the Enlightenment and its diametrically
opposed Idealistic Paradigm – so developing as a
reaction against their contrary methods, validity, and
scope – in respect of both their ethical values, and the
distinction between “opinion” and “justified belief”.
Romantic Philosophy, along with its new
epistemology of nature (natural history – the study of
life) came to be the prevailing mood of the 18th
century – a popular movement which also sparked
the religious revival to which both the Evangelical and
High Church movements bear stark witness
(Epistemology – the theory of knowledge)
Romantic movement – an artistic,
literary, and intellectual movement
emerging in Western Europe during
the second half of the 18th century: -

Metaphysical
Philosophy
metaphysical
philosophy
In Western philosophy,
metaphysics has become
the study of the
fundamental nature of
reality - what it is, why it
is, and how we are to able
to understand its true
properties.....
Metaphysics is a broad area of philosophical
enquiry marked out by two types of question.
The first question aims to be the most general
investigation possible into the nature of reality: -
are there principles which apply to all that is real,
to everything that exists? Through abstraction
from the particular (specific) nature of existing
things - what distinguishes them from each other,
what can we learn generically about objects
merely by virtue of the fact that they exist?
The second question or type of inquiry seeks to
uncover what is ultimately real - frequently
offering answers in sharp contrast to our
everyday experience of the world. Through an
understanding of the terms of these two
paradigms, metaphysics is very closely related to
ontology - which is usually taken to involve both
‘what is existence (being)?’ and ‘what types of
(fundamentally distinct) objects exist?’
Emmanuel Kant, Carl Linaeus,
Charles Darwin, Thomas Huxley
Thomas Henry Huxley was one of
the first adherents to Darwin's theory
of evolution by natural selection, and
did more than anyone else to
advance its claims – including a
famous debate in 1860 with Bishop
Samuel Wilberforce – this was a key
moment in his own career and in the
wider acceptance of evolution.

Metaphysical
Philosophy
metaphysical
philosophy
Related: -
Alchemy
Ontology
Taxonomy
Classification
Natural History
Natural Philosophy
Systemic
Methodology
Rationalism can be defined as “probabilistic research
approaches that employ forensic and analytical
methods, make extensive use of both qualitative and
quantitative analysis - free from any pre-determined
behavioural models - in order to discover the secrets
of hidden or “unknown” truths
Positivism can be defined as “deterministic research
approaches that employ empirical methods, and
make extensive use of quantitative analysis, or
develop logical calculi in order to develop hypotheses
and build conceptual models in support of formal
explanatory theory”
The essence of Realism is that what the senses
show us as reality is the truth; that objects have an
existence independent of the human mind.
Interpretation advocates the necessity for
researchers to understand differences between
Pragmatism holds that the most important
determinant of the epistemology, ontology, axiology
adopted is the question posed by the research
Rationalism – “blue-sky” research -
the natural stance of the free and
unencumbered “pure” scientist
Positivism – goal seeking - the
natural stance of the restricted and
constrained “applied” scientist
Realism – the direct, critical and
objective science of realism
Interpretation – scientific
researchers as “social actors”
Pragmatism – studies subjective
judgements about questions of
ethics, values and beliefs

Metaphysical
Philosophy
metaphysical
philosophy
In Western philosophy,
metaphysics has
become the study of the
fundamental nature of
reality - what it is, why it
is, and how we are to
understand its
properties.....
Related: -
Alchemy
Ontology
Taxonomy
Classification
Natural History
Natural Philosophy
Systemic Methodology
Darwinism is a theory of biological evolution
which was developed by Charles Darwin and
others, stating that all species of organisms arise
and develop over time through the mechanism of
natural selection – in which only those organisms
best adapted to their environment survive long
enough to reproduce – and therefore pass on their
genetic information to the next generation.
Relativity or the theory of relativity in physics,
encompasses two theories by Albert Einstein: the
case of special relativity and general relativity –
each describe the properties of Mass and Energy
(Mass-Energy) interacting with Space and Time.
The Minkowski Space-Time continuum is a four-
dimensional manifold or construct, described by
Hermann Minkowski to better understand the case
of special relativity. It has four dimensions - three
dimensions of space inextricably linked with time.
Hubble's Law is named after astronomer Edwin
Hubble whose studies of distant galaxies gave us
an understanding of the expanding Universe.
Edwin Hubble was hired to work at
Mount Wilson Observatory in 1919
(part of the Observatories of the
Carnegie Institution of Washington)
as a junior astronomer. During the
1920's and 30's, Edwin Hubble
discovered that the Universe is
expanding, with galaxies moving
away from each other at a rapidly
increasing rate of acceleration

Metaphysical
Philosophy
metaphysical
philosophy
In Western
philosophy,
metaphysics has
become the study of
the fundamental
nature of reality -
what it is, why it is,
and how we are to
understand its
properties.....
Related: -
Alchemy
Ontology
Taxonomy
Classification
Natural History
Natural Philosophy
Systemic
Methodology
String theory is a set of mathematical attempts to
model the four known fundamental interactions -
gravitation, electromagnetism, strong nuclear force,
weak nuclear interaction – clustered into a single,
universal Theory of Wave Dynamics.
In theoretical physics, M-theory is an extension of
string theory in which the 11 dimensions of space-
time are identified as 7 higher-dimensions plus the 4
common dimensions (11D st = 7 hd + 4D).
Proponents believe that the 11-dimensional theory
unites all five 10-dimensional string theories (10D st =
6 hd + 4D) and supersedes them. Though a full
description of the theory is not known, the low-entropy
dynamics are thought to be supergravity interacting
with 2- and 5-dimensional membranes in a single,
unified Theory of Wave Dynamics.
Gabriele Veneziano – is an Italian
theoretical physicist and string
theorist. His 1968 dual resonance
model of the strong interaction was
the first component of string theory to
be described - he is regarded as a
founder of this field of String Theory.
Brian Greene, author of the book
about string theory, The Elegant
Universe, was educated at Harvard
and Oxford, graduating in 1987. After
working at Harvard and Cornell, he is
currently a Professor of Physics and
Mathematics at Columbia.

Metaphysical
Philosophy
Pre-Raphaelite
Philosophy
Related: -
Gothic Revival
Pre-Raphaelite
Movement
Pre-Raphaelite
Brotherhood
The Pre-Raphaelite Movement (also known as the
Pre-Raphaelite Brotherhood or Pre-Raphaelites)
were a group of English painters, poets, and critics,
founded in 1848 by William Holman. Pre-
Raphaelitism sprang from a new mood in English
painting, reflecting the great moral and material
changes of the age which, by the middle of the 19th
century, saw the arrival of a new generation of artists
– who were forerunners of the Modern Art movement.
Major influences included the industrial revolution,
which brought far-reaching social changes - not least
important, a new wealthy Middle Class whose taste
and outlook were formed under new influences -
along with a renewed interest in nature and the arts
and culture of the Middle Ages - the Gothic revival....
The Pre-Raphaelite Brotherhood,
founded in 1848 by William Holman,
rejected post-Enlightenment arts and
society - looking back to the Middle
Ages for artistic / cultural inspiration.
Dante Gabriel Rossetti - John Everett
Millais - Quattrocento - William
Holman Hunt, Ford Madox Brown
Postmodern
philosophy
Postmodernism is a philosophical movement or
tendency which is critical of the fundamental
principles, assumptions and general direction which
underpin classical western philosophy.
Postmodernism emphasises the importance of
discourse, power relationships, personalisation and
individualism as key in the "construction" or
“rebuilding” of the Post-modernist world viewpoint.
Architecture features strongly in the
post-modernistic movement – Aldo
Rossi, Frank Gehry and Terry Farrell

Complexity
Paradigm
From the Paradigm (concept) of Complexity to
System Complexity - philosophical, scientific and
professional disciplines addressing complexity in
their fields all view the Complexity Paradigm as based
on the behaviour of complex systems and the rich
conceptual world of non-linear equations – centred on
the science of turbulence and chaos, strange
attractors, emergence and fractals, self-organisation
and critical system complexity.
Edward Lorenz, John Henry Holland,
Edgar Morin
Disruptive Futurism Disruptive Futurism is a Futurist Framework for
Digital Technology Disruption – Digital Platform and
Service convergence – which, since the year 2000,
has severely impacted on the performance of 52% of
the Fortune 500 listed companies.
Joseph Schumpeter – Austrian
School Economist
Ian Neild – BT Laboratories
Experiential
Philosophy (versus
Argumentative
Philosophy)
The New Philosophy:
Cognitive Science &
Experiential Realism
Experiential Philosophy maintains that real-world
experience has priority over theoretical
argumentation; as Lewis Carroll so brilliantly
demonstrated, even the most logical of arguments
cannot persuade someone who refuses to experience
the rationality of the move from some set of premises
to their logical conclusion
John Dewey - experiential training,
education and learning (1938)
Experiential education and training
refers to a pedagogical philosophy
and methodology concerned with
practical, hands-on learning activities

Metaphysical
Philosophy
Interpretive
Paradigm
Interpretive research operates within a research
paradigm that functions differently from traditional
research in the humanities or social sciences - as it
operates without any prior assumptions or pre-
determined views about findings or outcomes.
Interpretive views have different originations in
different disciplines. Positivism (Deterministic) and
Phenomenological (Interpretive) research and the
growing consensus of a mixed methods approach to
research studies – explains a rapidly increasing
popularity in interpretative research methods.
Schultz, Cicourel and Garfinkel
Interpretive Research is used In
pure research studies, within a
research paradigm that functions
somewhat differently from traditional
research in applied science, the
humanities or social sciences - as it
proceeds without any fixed model,
prior assumptions or pre-determined
views about any of the findings or
outcomes of the research study.
Deterministic
Paradigms –
Positivism (and
Post-positivism)
Positivism is sometimes referred to as the 'scientific
method' or 'science-based research', and in
Management Studies, as ‘strategic positivism’.
Positivism as such is founded on a deterministic,
rationalistic, empiricist philosophy that originated with
Aristotle, Francis Bacon, John Locke, August Comte
and Emmanuel Kant" (Mertens, 2005, p.8) and
"reflects a deterministic philosophy in which causes
probabilistic pre-determined outcomes and effects"
(Creswell, 2003, p.7).
Circa Trova – seek and ye shall find.
Aristotle, Francis Bacon, John Locke,
August Comte and Emmanuel Kant
The scientific researcher observes
the behaviour of a system, formulates
a hypothesis to explain the observed
behaviour, and then designs and
executes an experiment to test how
well his hypothesis predicts the actual
and real observations and outcomes.

Qualitative
Paradigm
Qualitative Methods
- tend to be
deterministic,
interpretive and
subjective in
nature.
When we wish to design a research project to
investigate large volumes of unstructured data
producing and analysing graphical image and text
data sets with a very large sample or set of
information – “Big Data” – then the quantitative
method is preferred. As soon as subjectivity - what
people think or feel about the world - enters into the
scope (e.g. discovering Market Sentiment via Social
Media postings), then the adoption of a qualitative
research method is vital. If your aim is to understand
and interpret people’s subjective experience and the
broad range of meanings that attach to it, then
interviewing, observation and surveying a range of
non-numerical data (which may be textual, visual,
aural) are key strategies you will consider. Research
approaches such as using focus groups, producing
case studies, undertaking narrative or content
analysis, participant observation and ethnographic
research are all important qualitative methods. You
will also want to understand the relationship of
qualitative data to numerical research. Any qualitative
methods pose their own problems with ensuring the
research produces valid and reliable results (see also:
Data Science and working with “Big Data” Analytics.
Qualitative Paradigm. Most
qualitative research texts identify
three primary types of research:-
1. Exploratory – research on a
concept, people, or situation that
the researcher knows little about.
2. Descriptive (Narrative) –
research on a concept, people,
process or situation that the
researcher knows something
about, but just wants to describe
the narrative findings that he/she
has found or observed.
3. Explanatory – involves deriving
a hypothesis from existing
theories and available models,
then testing that hypothesis
through a process of
experimental observation and
data collection.

Quantitative
Paradigm
Quantitative
Methods - tend to
be probabilistic,
analytic and
objective in nature.
When we want to design a research project to test a
hypothesis objectively by capturing and analysing
numerical data sets with a large sample or set of
information – then the quantitative method is
preferred. There are many key issues to consider
when you are designing an experiment , predictive
model, system or some other research project using
quantitative methods - such as randomisation,
selection and sampling.. Also, quantitative research
uses mathematical and statistical means extensively
to produce reliable analysis of its results (see also:
Cluster Analysis and Wave-form Analysis methods).
Quantitative Research refers to the
systematic empirical investigation of
social and scientific phenomena
through system modelling and
statistical analysis - via direct
observation and careful collection of
mathematical, numerical or biometric
datasets, and thorough analysis and
interpretation of the data.
Scientific Research observes and
collects data on the behaviour of a
system, formulates a hypothesis to
explain the observed behaviour, and
then designs and executes an
experiment to test how well his
hypothesis predicts the actual and
real observations and outcomes.

Future Taxonomy
There are some 10-20 Primary Futures Disciplines, 20-30
Futures Paradigms and over 250 Secondary Futures
Specialities documented in various sources – covering
Futures Studies, Strategic Foresight, Military and Business
Strategy, Economic Modelling and Long-range Forecasting,
Business Planning and Financial Analysis –

Future Taxonomy
• The main objective of any Futures Taxonomy is to identify, capture, analyse and
classify the mainstream Futures Studies, Strategic Foresight and Strategy Analysis
Primary Future Disciplines (20-30) Futures Studies Subjects (20-30) – Regimes,
Frameworks and Paradigms, and then to document the Secondary Future
Specialties (over 250) – Models, Methods, Tools and Techniques – and to order,
group, define and describe both the Primary and Secondary subjects in a
comprehensive, consistent, coherent, complete and logical manner.
• This is the first step towards creating a Futures Body of Knowledge (BOK)
• There are some 10-20 Primary Futures Disciplines, 20-30 Futures Paradigms and
over 250 Secondary Specialities documented in various sources – covering Futures
Studies, Strategic Foresight, Military and Business Strategy, Economic Modelling
and Long-range Forecasting, Business Planning and Financial Analysis
• Primary Future Disciplines – 10-20
• Futures Studies Regimes, Frameworks and Paradigms – 20-30
• Secondary Future Specialties – up to 250

Probabilistic Future Viewpoints
• Polemic (Rational) Paradigm - Enlightened Futurism
• Postmodernism (Reactionary) Paradigm - Structural Futurism
• Mystic (Gnostic, Sophistic, Esoteric, Cathartic) Paradigm – Contemplative Futurism
• Uncertainty (Random, Chaotic, Disorderly, Enthalpy) Paradigm - Disruptive Futurism
• Experiential (Forensic, Deductive, Realist, “Blue Sky”) Paradigm – Pragmatic Futurism
• Interpretive (Ordered, Systemic, Mechanistic, Entropic) Paradigm – Constructive Futurism

Deterministic Future Viewpoints
• Humanist (Instructional) Paradigm - Sceptic Futurism
• Dogmatic (Theosophical) Paradigm - Reactionary Futurism
• Utilitarian (Consequential) Paradigm – Egalitarian Futurism
• Steady State (La meme chose - same as it ever was) Paradigm – Constant Futurism
• Hellenistic (Classical) Paradigm – Future of Human Ethics, Morals, Values and Beliefs
• Empirical (Scientific Determinism, Theoretical Positivism) Paradigm – Hypothetical Futurism
• Predictive (Ordered, Systemic, Mechanistic, Enthalpy) Paradigm – Deconstructionist Futurism

Primary Futures Research Disciplines
• Futures Studies
– History and Analysis of Prediction
– Future Studies – Classification and Taxonomy
– Future Management Primary Disciplines
– Future Management Secondary Specialisations
• Strategic Foresight
– Foresight Regimes, Frameworks and Paradigms
– Foresight Models, Methods, Tools and Techniques
• Qualitative Techniques
• Quantitative Techniques
• Systems Theory - Complexity
• Chaos Theory – Random Events, Uncertainty and Disruption
• Political and Economic Futures
• Science and Technology Futures
• Entrepreneurship and Innovation Futures
• Personal Futures – Trans-humanism, NLP / EHT
• The Future of Philosophy, Knowledge and Values
• Future Beliefs – Moral, Ethical and Religious Futures
• Massive Change – Human Impact and Global Transformation
• Human Futures – Sociology, Anthropology and Cultural Studies
• The Future of Information, Knowledge Management and Decision Support

Primary Futures Disciplines
Primary
Futures
Disciplines
9.
Future of Philosophy,
Knowledge & Values
7 .
Future of Information &
Knowledge Management
10. Future Beliefs –
Moral, Ethical
& Religious Futures
1. Futures Studies
4.
Science and
Technology Futures
12. Human Futures –
Sociology, Anthropology
and Cultural Studies
3.
Political & Economic
Futures
6.
Entrepreneurship &
Innovation Futures
2. Strategic Foresight
5.
Environment, Climate &
Ecology Futures
8.
Personal Futures –
Trans-humanism
11. Massive Change –
Human Impact and
Global Transformation

Primary Futures Disciplines
• Futures Studies
– History and Analysis of Prediction
– Future Studies – Classification and Taxonomy
– Future Management Primary Disciplines
– Future Management Secondary Specialisations
• Strategic Foresight
– Foresight Regimes, Frameworks and Paradigms
– Foresight Models, Methods, Tools and Techniques
• Quantitative Techniques
• Qualitative Techniques
• Chaos Theory – Random Events, Uncertainty and Disruption
• Political and Economic Futures
• Entrepreneurship and Innovation Futures
• Personal Futures – Trans-humanism, NLP / EHT
• The Future of Philosophy, Knowledge and Values
• Future Beliefs – Moral, Ethical and Religious Futures
• Massive Change – Human Impact and Global Transformation
• Human Futures – Sociology, Anthropology and Cultural Studies
• The Future of Information, Knowledge Management and Decision Support

Secondary Future Specialties
• Monte Carlo Simulation
• Forecasting and Foresight
• Back-casting and Back-sight
• Causal Layered Analysis (CLA)
• Complex Adaptive Systems (CAS)
• Political Science and Policy Studies
• Linear Systems and Game Theory
• War-gaming and Lanchester Theory
• Complex Systems and Chaos Theory
• Integral Studies and Future Thinking
• Critical and Evidence-Based Thinking
• Predictive Surveys and Delphi Oracle
• Visioning, Spontaneity and Creativity
• Foresight, Intuition and Pre-cognition
• Developmental & Accelerative Studies
• Systems & Technology Trends Analysis
• Scenario Planning and Impact Analysis
• Collaboration, Facilitation & Mentoring
• Black Swan Events - Weak Signals, Wild
Cards, Chaos, Uncertainty & Disruption
• Economic Modelling & Planning
• Financial Planning and Analysis
• Ethics of Emerging Technology Studies
• Horizon Scanning, Tracking & Monitoring
• Intellectual Property and Knowledge
• Critical Futures and Creative Thinking
• Emerging Issues and Technology Trends
• Patterns, Trends & Extrapolation Analysis
• Linear Systems & Random Interactions
• Cross Impact Analysis and Factors of
Global Transformation and Change
• Preferential Surveys / Polls and Market
Research, Analysis and Prediction
• The Future of Religious Beliefs - Theology,
Divinity, Ritual, Ethics and Value Studies
• Divination – Hermetic, Mystic, Esoteric
and Enlightened Spiritual Practices

• The Cosmology Revolution
– Dark Energy, Dark Mass
– String Theory and the Nature of Matter
• SETI – The Search for Extra-Terrestrial
Planetary Systems, Life and Intelligence
• Nano-Technology, Nuclear Physics and
Quantum Mechanics
• The Energy Revolution - Nuclear Fusion
Hydrolysis and Clean Energy
• Science and Society Futures – the Social
Impact of Technology
• Smart Cities of the Future
• The Information Revolution – Internet
Connectivity and the Future of the
Always-on Digitally Connected Society
• Digital Connectivity, Smart Devices, the
Smart Grid & Cloud Computing Futures
• Content Analysis (“Big Data”) – Data Set
“mashing”, Data Mining & Analytics
• Earth and Life Sciences – the Future of
Biology, Geology & Geographic Science
• Environmental Sustainability Studies –
Climatology, Ecology and Geography
• Human Activity – Climate Change and
Future Environmental Degradation –
Desertification and De-forestation
• Human Populations - Profiling, Analysis,
Streaming and Segmentation
• Human Futures - Population Drift and
Urbanisation - Human Population Curves
and Growth Limit Analysis
• The Future of Agriculture, Forestry,
Fisheries, Agronomy & Food Production
• Terrain Mapping and Land Use – Future
of Topology, Topography & Cartography
• Future Natural Landscape Planning,
Environmental Modelling and Mapping
• Future Geographic Information Systems,
Spatial Analysis & Sub-surface Modelling

• Macro-Economic and Financial Futures
• Micro-Economic and Business Futures
• Strategic Visioning – Possible, Probable &
Alternative Futures
• Strategy Design – Vision, Mission and
Strategy Themes
• Strategy Development – Outcomes, Goals
and Objectives
• Performance Management – Target Setting
and Action Planning
• Critical Success Factors (CSF’s) and Key
Performance indicators (KPI’s)
• Business Process Management (BPM)
• Balanced Scorecard Method
• Planning and Strategy
– (foundation, intermediate & advanced)
• Modelling and Forecasting
• Threat Assessment & Risk Management
• Layers of Power, Trust and Reputation
• Leadership Studies, Goal-seeking and
Stakeholder Analysis
• Military Science, Peace and Conflict
Studies – War, Terrorism and Insecurity
• Corporate Finance and Strategic
Investment Planning Futures
• Management Science and Business
Administration Futures
• Future Management and Analysis of Global
Exploitation of Natural Resources
• Social Networks and Connectivity
• Consumerism and the rise of the new
Middle Classes
• The BRICs and emerging powers
– • Brazil • Russia • India • China •
• The Seven Waves of Globalisation
– • Goods • People • Capital • Services
– • Ideology • Economic Control •
– • Geo-Political Domination •

• Human Values, Ethics and Beliefs
• History, Culture and Human Identity
• Human Geography & Industrial Futures
• Human Factors and Behavioural Theory
• Anthropology, Sociology and Factors of
Cultural Change
• Human Rites, Rituals and Customs - the
Future of Cults, Sects and Tribalism
• Ethnographic and Demographic Futures
• Epidemiology, Morbidity and Actuarial
Science Futures
• Infrastructure Strategy, Regional Master
Planning and Urban Renewal
• Future Townscape Envisioning. Planning
Modelling and Virtual Terrain Mapping
• The Future of Urban and Infrastructure
Master Planning, Zoning and Control
• Architecture and Design Futures - living
in the Built Environment of the Future
• Trans-humanism – The Future Human
State – Qualities, Capabilities, Capacities
• The Future of Medical Science, Bio-
Technology and Genetic Engineering
• The Future of the Human Condition -
Health, Wealth and Wellbeing
• The Future of Biomechanics, Elite Sports
and Professional Athletics
• Personal Futures – Motivational Studies,
Life Coaching and Personal Training
• Positive Thinking – Self-Awareness, Self-
Improvement & Personal Development
• Positive Behavioural Psychology and
Cognitive Therapy - NLP and EHT
• Intuitive Assimilation and Cognitive
Analysis
• Predictive Envisioning and Foresight
Development
• Contemplative Mediation and Psychic
Methods

• Business Strategy, Transformation and
Programme Management Futures
• Next Generation Enterprises (NGE) –
Envisioning, Planning and Modelling
• Multi-tier Collaborative Future Business
Target Operating Models (eTOM)
• Corporate Responsibility / Triple Bottom
Line Management
• Regulatory Compliance - Enterprise
Governance, Reporting and Controls
• Future Economic Modelling, Long-range
Forecasting and Financial Analysis
• The Future of Organisational Theory
and Operational Analysis
• Business Innovation and Product
Planning Futures
• Technology Innovation and Product
Design Futures
• Product Engineering and Production
Planning Futures
• Enterprise Resource Planning and
Production Management Futures
• Marketing Needs Analysis, Propositions
and Product Life-cycle Management
• The Future of Marketing Services,
Communications and Advertising
• The Future of Media, Entertainment and
Multi-channel Communications
• The Future of Leisure, Travel & Tourism –
Culture, Restaurants and Entertainment
• The Future of Spectator Events - Elite
Team Sports and Professional Athletics
• The Future of Art, Literature and Music
• The Future of Performance Arts, Theatre
and the Moving Image
• Science Fiction & Images of the Future
• Interpreting Folklore, Legends & Myths –
Theology, Numerology & Lexicography
• Utopian and Dystopian Literature, Film
and Arts

Strategic Foresight as
Knowledge Management

Thinking about the Future…..
• The way that we think about the future must mirror how the future actually unfolds.
As we have learned from recent experience, the future is not a straightforward
extrapolation of simple, single-domain trends. We now have to consider ways in
which the possibility of random, chaotic and radically disruptive events may be
factored into enterprise threat assessment and risk management frameworks and
incorporated into enterprise decision-making structures and processes.
• Managers and organisations often aim to “stay focused” and maintain a narrow
perspective in dealing with key business issues, challenges and targets. A
concentration of focus may risk overlooking those Weak Signals indicating potential
issues and events, agents and catalysts of change. These Weak Signals – along with
their resultant Wild Cards, Black Swan Events and global transformations - are even
now taking shape at the very periphery of corporate awareness, perception and
vision – or even just beyond.
• These agents of change may precipitate global impact-level events which either
threaten the very survival of the organisation - or present novel and unexpected
opportunities for expansion and growth. The ability to include weak signals and
peripheral vision into the strategy and planning process may therefore be critical in
contributing towards the organisation's continued growth, success, well being and
survival.

THINKING ABOUT THE FUTURE -
• It has long been recognized that one of the most important competitive
factors for any organization to master is the management of uncertainty.
Uncertainty is the major intangible factor contributing towards the risk of
failure in every process, at every level, in every type of business.
• The way that we think about the future must mirror how the future actually
unfolds. As we have all learned from recent experience, the future is not a
simple extrapolation of linear, single-domain trends. We now have to
consider ways in which the possibility of random, chaotic and radically
disruptive events may be factored into enterprise strategy development,
threat assessment and risk management frameworks and incorporated into
enterprise decision-making structures and processes.

THINKING ABOUT THE FUTURE -
• Managers and organisations often aim to “stay focused” and maintain a
narrow perspective in dealing with key business issues, challenges and
targets. A concentration of focus may risk overlooking those Weak Signals
indicating potential issues and events, agents and catalysts of change. These
Weak Signals – along with their resultant Wild Cards, Black Swan Events and
global transformations - are even now taking shape at the very periphery of
corporate awareness, perception and vision – or even just beyond.
• These agents of change may precipitate global impact-level events which
either threaten the very survival of the organisation - or present novel and
unexpected opportunities for expansion and growth. The ability to include
weak signals and peripheral vision into the strategy and planning process
may therefore be critical in contributing towards the organisation's continued
growth, success, well being and survival.

Futures Studies
• Futures Studies, Foresight, or Futurology is the science, practice and art
of postulating possible, probable, and preferable futures. Futures studies
(colloquially called "Futures" by many of the field's practitioners) seeks to
understand what is likely to continue, what is likely to change, and what is
a novel, emerging pattern or trend. Part of the discipline thus seeks a
systematic and extrapolation-based understanding of both past and
present events - in order to determine the probability and impact of
future events, patterns and trends.
• Futures is an interdisciplinary curriculum, studying yesterday's and today's
changes, and aggregating and analyzing both lay and professional content
and strategies, beliefs and opinions, forecasts and predictions with respect
to shaping tomorrow. It includes analysing the sources, agents and causes,
patterns and trends of both change and stability in an attempt to develop
foresight and to map possible, probable and alternative futures.

Foresight
• Foresight draws on traditions of work in long-range forecasting and strategic planning
horizontal policymaking and democratic planning, horizon scanning and futures
studies (Aguillar-Milan, Ansoff, Feather, van der Hijden, Slaughter et all) - but was also
highly influenced by systemic approaches to innovation studies, global design,
massive change, science and technology futures, economic, social and demographic
policy, fashion and design - and the analysis of "weak signals" and "wild cards",
"future trends“ "critical technologies“ and “cultural evolution".
– The longer-term - futures that are usually at least 10 years away (though there are some
exceptions to this, especially in its use in private business). Since Foresight is an action-
oriented discipline (via the planning link) it will rarely be applied to perspectives beyond a
few decades out. Where major infrastructure decisions such as petrology reservoir
exploitation, aircraft design, power station construction, transport hubs and town master
planning decisions are concerned - then the planning horizon may well be half a century.
– Alternative futures: it is helpful to examine alternative paths of development, not just what
is currently believed to be most likely or business as usual. Often Foresight will construct
multiple scenarios. These may be an interim step on the way to creating what may be
known as positive visions, success scenarios or aspirational futures. Sometimes alternative
scenarios will be a major part of the output of a Foresight study, with the decision about
what preferred future to build being left to other mechanisms (Planning and Strategy).

Strategic Foresight
• Strategic Foresight is the ability to create and maintain a high-quality, coherent
and functional forward view, and to use the insights arising in useful organisational
ways. For example to detect adverse conditions, guide policy, shape strategy, and
to explore new markets, products and services. It represents a fusion of futures
methods with those of strategic management (Slaughter (1999), p.287).
• Strategic Envisioning – Future outcomes, goals and objectives are defined via
Strategic Foresight and are determined by design, planning and management - so
that the future becomes realistic and achievable. Possible futures may comply with
our preferred options - and therefore our vision of an ideal future and desired
outcomes could thus be fulfilled.
– Positivism – articulating a single, preferred vision of the future. The future will conform
to our preferred options - thus our vision of an ideal future and desired outcomes will be
fulfilled.
– Futurism – assessing possible, probable and alternative futures – selecting those futures
offering conditions that best fit our strategic goals and objectives for achieving a
preferred and desired future. Filtering for a more detailed analysis may be achieved by
discounting isolated outliers and focusing upon those closely clustered future
descriptions which best support our desired future outcomes, goals and objectives.

Risk Management
• Risk Management is a structured approach to managing uncertainty through foresight and
planning. A risk is related to a specific threat (or group of related threats) managed through a
sequence of activities using various resources: -
– Risk Research – Risk Identification – Scenario Planning & Impact Analysis – Risk Assessment – Risk
Prioritization – Risk Management Strategies – Risk Planning – Risk Mitigation
• Risk Management strategies may include: -
– Transferring the risk to another party
– Avoiding the risk
– Reducing the negative effect of the risk
– Accepting part or all of the consequences of a particular risk .
• For any given set of Risk Management Scenarios, a prioritization process ranks those risks with
the greatest potential loss and the greatest probability of occurrence to be handled first – and
those risks with a lower probability of occurrence and lower consequential losses are then
handled subsequently in descending order of impact.
• In practice this prioritization can be challenging. Comparing and balancing the overall threat of
risks with a high probability of occurrence but lower loss -versus risks with higher potential loss
but lower probability of occurrence -can often be misleading.

Scenario Planning and Impact Analysis
• Scenario Panning and Impact Analysis: - In any Opportunity / Threat Assessment Scenario, a
prioritization process ranks those risks with the greatest potential loss and the greatest probability
of occurring to be handled first - subsequent risks with lower probability of occurrence and lower
consequential losses are then handled in descending order. As a foresight concept, Wild Card or
Black Swan events refer to those events which have a low probability of occurrence - but an
inordinately high impact when they do occur.
– Risk Assessment and Horizon Scanning have become key tools in policy making and strategic planning for
many governments and global enterprises. We are now moving into a period of time impacted by
unprecedented and accelerating transformation by rapidly evolving catalysts and agents of change in a
world of increasingly uncertain, complex and interwoven global events.
– Scenario Planning and Impact Analysis have served us well as a strategic planning tools for the last 15
years or so - but there are also limitations to this technique in this period of unprecedented complexity and
change. In support of Scenario Planning and Impact Analysis new approaches have to be explored and
integrated into our risk management and strategic planning processes.
• Back-casting and Back-sight: - “Wild Card” or “Black Swan” events are ultra-extreme
manifestations with a very low probability of, occurrence - but an inordinately high impact when
they do occur. In any post-apocalyptic “Black Swan Event” Scenario Analysis, we can use Causal
Layer Analysis (CLA) techniques in order to analyse and review our Risk Management Strategies –
with a view to identifying those Weak Signals which may have predicated subsequent appearances
of unexpected Wild Card or Black Swan events.

Weak Signals and Wild Cards
• “Wild Card” or "Black Swan" manifestations are extreme and unexpected events
which have a very low probability of occurrence, but an inordinately high impact
when they do happen Trend-making and Trend-breaking agents or catalysts of
change may predicate, influence or cause wild card events which are very hard - or
even impossible - to anticipate, forecast or predict.
• In any chaotic, fast-evolving and highly complex global environment, as is currently
developing and unfolding across the world today, the possibility of any such "Wild
Card” or "Black Swan" events arising may, nevertheless, be suspected - or even
expected. "Weak Signals" are subliminal indicators or signs which may be detected
amongst the background noise - that in turn point us towards any "Wild Card” or
"Black Swan" random, chaotic, disruptive and / or catastrophic events which may be
on the horizon, or just beyond......
• Back-casting and Back-sight: - In a post-apocalyptic Black Swan Event Scenario, we
can use Causal Layer Analysis (CLA) techniques in order to analyse and review our
Risk Management Strategies to identify those Weak Signals which may have
predicted, suggested, pointed towards or indicated subsequent Wild Cards or Black
Swan Events – in order to discover changes and improvements to strengthen
Enterprise Risk Management Frameworks.

• Foresight and Precognition – Contemplative, mystic, meditative and psychic methods for pre-cognitive
viewing of the future and how the future will unfold. These activities have been recorded throughout
history (Josephus, Nostradamus) and are well known within certain cultures (Central American Indians)
and government agencies (US and Soviet Military) - and may also involve the use of hypnotic or
hallucinogenic states.
• The Intelligence Revolution – Artificial Intelligence will revolutionise homes, workplaces and lifestyles -
and new virtual worlds will become so realistic that they will rival the physical world. Robots with human-
level intelligence may finally become a reality, and at the ultimate stage of mastery, we'll even be able to
merge human capacities with machine intelligence and attributes – via the man-machine interface.
• The Biotech Revolution – Genetics and biotechnology promise a future of unprecedented health and
longevity: DNA screening could prevent many diseases, gene therapy could cure them and, thanks to
laboratory-grown organs, the human body could be repaired as easily as a car, with spare parts readily
available. Ultimately, the ageing process itself could be slowed or even halted.
• Trans-humanism – advocates the ethical use of technology to expand current human capacities,
supporting the use of future science and technology to enhance human capabilities and qualities, in order
to overcome undesirable and unnecessary aspects of the present human condition.
• The Quantum Revolution – The quantum revolution could turn many ideas of science fiction into science
fact - from meta-materials with mind-boggling properties like invisibility through limitless quantum energy
and room temperature superconductors to Arthur C Clarke's space elevator. Some scientists even forecast
that in the latter half of the century everybody will have a personal fabricator that re-arranges molecules
to produce everything from almost anything. Yet how will we ultimately use our mastery of matter? Like
Samson, will we use our strength to bring down the temple? Or, like Solomon, will we have the wisdom to
match our technology?

• Renewable Resources. Any natural resource is a renewable resource if it is replenished by natural processes at a
rate comprisable to or faster than its rate of consumption by humans or other users. Some renewable resources
- solar radiation, tides, wind and hydroelectricity, nuclear fusion - are also classified as perpetual resources, in
that they will never be able to be consumed at a rate in excess of their long-term availability or renewal. The
term renewable resource also carries the implication of prolonged or perpetual sustainability for the processing
and absorption of waste products via natural ecological and environmental processes.
• Sustainability is a characteristic of a process or mechanism that can be maintained indefinitely at a certain
constant level or state – without showing any long-term degradation, decline or collapse.. This concept, in its
environmental usage, refers to the potential longevity of vital human ecological support systems - such as the
ecology, environment the and man-made systems of agriculture, industry, forestry, fisheries - and the planet's
climate and natural processes and cycles upon which they depend.
• Global Massive Change is an evaluation of global capacities and limitations. It includes both utopian and
dystopian views of the emerging world future state, in which climate, the environment and geology are
dominated by human manipulation –
– Human impact is now the major factor in climate change and environmental degradation.
– Extinction rate is currently greater than in the Permian-Triassic boundary extinction event
– Man now moves more rock and earth than do natural geological processes.
• In the past, many complex human societies (Clovis, Mayan, Easter Island) have failed, died out or just simply
disappeared - often as a result of either climate change or their own growth-associated impacts on ecological
and environmental support systems. Thus there is a clear precedent for modern industrial societies - which
continue to grow unchecked in terms of globalisation complexity and scale, population growth and drift,
urbanisation and environmental impact – societies which are ultimately unsustainable, and so in turn must also
be destined for sudden and catastrophic instability, failure and collapse.

Complexity Paradigms
System Complexity is typically characterised by the number of elements in a system,
the number of interactions and the nature (type) of those interactions. One of the
problems in addressing complexity issues has always been distinguishing between the
large number of elements and relationships, or interactions evident in chaotic
(unconstrained) systems - and the still large, but significantly smaller number of
elements and interactions found in ordered (constrained) systems. Orderly
Frameworks act to both reduce the total number of elements and interactions – with
fewer and smaller classes of more-uniform elements – and with less regimes of
reduced size featuring more highly-ordered, internally correlated and constrained
interactions – as compared with Disorderly Frameworks.

• Simplexity (Reductionist) Paradigm – Linear Systems & Chaotic Interaction
– Linear Systems and Game Theory
– War-gaming and Lanchester Theory
• Entropic (Ordered, Systemic, Mechanistic) Paradigm – Structural Futurism
– Complex Adaptive Systems (CAS)
• Complexity (Constructionist) Paradigm – Complex Systems & Chaos Theory
– Complex Ordered Systems
– Complex Disordered Systems
• Uncertainty (Random, Chaotic, Disorderly, Enthalpy) Paradigm – Disruptive Futurism
– Cosmology
– Climatology
– Black Swan Events - Weak Signals, Wild Cards, Chaos, Uncertainty & Disruption

Complexity (Constructionist) Paradigm
– Complex Systems and Chaos Theory
• Complexity tends to be used to characterize systems with many elements or parts arranged in
a complex or intricate relationship. The study of these complex linkages, relationships, or
interactions between elements is the main goal of network theory and network science. In
science there are a number of approaches to characterizing complexity, many of which are
reflected in this Paradigm. In a business context, complexity management is the methodology
to minimize value-destroying complexity and efficiently control value-adding complexity in a
cross-functional system approach.
• Definitions are often tied to the concept of a "system"—a set of parts or elements which have
relationships among them differentiated from relationships with other elements outside the
relational framework or regime. Many definitions tend to assume that complexity expresses a
condition with numerous elements in a system and numerous instances and types of
relationships between the elements. Simplexity, the sister paradigm to Complexity, helps us to
differentiate between the analysis of complex systems and reduction of complex systems into
multiple simple systems.
• Warren Weaver has postulated that the complexity of any particular system is the degree of
difficulty in predicting system outcomes when the properties of the system's parts and
relationships are known and understood. Other definitions relate Complexity to the
probability of encountering any given condition in a system once the behaviours or
characteristics of the system have been specified. In Weaver's view, complexity comes in two
forms: Disorganized Complexity and Organized Complexity.
• From Wikipedia, the free encyclopedia

• One of the problems in addressing complexity issues has always been distinguishing
conceptually between the large number of elements and relationships, or interactions evident
in chaotic (unconstrained) systems - and the still large, but significantly smaller number of
elements and interactions found in ordered (constrained) systems. Order acts to both reduce
the number of elements and interactions - and at the same time creates smaller regimes of
more-uniform, ordered or correlated, interactions.
• Weaver perceived and addressed this problem, in at least a preliminary way, by drawing a
distinction between "disorganized complexity" and "organized complexity".
• Weaver's paper has influenced contemporary thinking about complexity. In Weaver's view,
Disorganized Complexity results from a system having a very large number of parts - say
millions. billions or many more. Though the interactions of these parts in a "disorganized
complexity" paradigm can be seen as random – thus properties of the system as a whole can
be understood by using probability and statistical analysis. System size, therefore, brings with
it a new type of Complexity - all of its own…..
• Organized Complexity, in Weaver's view, resides in the property of a non-random, ordered, or
correlated, interaction between the parts. These correlated relationships create a
differentiated structure which can act as a system and interact freely with other systems. The
coordinated system exhibits properties that are not carried by, or dictated by, its individual
parts. The organisational aspect of this form of complexity compared with other types of
system is that the subject system “develops”, “emerges! or “evolves” without any external
intervention from any form of "guiding hand".

Complexity theory has been used extensively in the field of Futures Studies, Strategic
Management, Organisational Theory and Operational Analysis. It is applied in these domains to
understand how organisations or enterprises adapt to their environment. The theory treats
organizations and firms as collections of strategies and structures.
When organisations or enterprises demonstrate properties of Complex Adaptive Systems (CAS)
- which is often defined as consisting of a small number of relatively simple and loosely
connected systems - then they are much more likely to adapt to their environment and, thus,
survive the impact of change and random events. Complexity theory thinking has been present
in strategic and organisational studies since the first inception of Complex Adaptive Systems
(CAS) as an academic discipline.
Complex Adaptive Systems are contrasted with ordered and chaotic systems by the relationship
that exists between the system and the agents and catalysts of change which act upon it. In an
ordered system the level of constraint means that all agent behaviour is limited to the rules of
the system. In a chaotic system these agents are unconstrained and are capable of random
events, uncertainty and disruption.
In a CAS, both the system and the agents co-evolve together; the system acting to lightly
constrain the agents behaviour - the agents of change, however, modify the system by their
interaction. CAS approaches to strategy seek to understand both the nature of system
constraints and change agent interactions and generally takes an evolutionary or naturalistic
approach to scenario planning and strategy development

Simplexity (Reductionist) Paradigm –
Linear Systems and Chaotic Interaction
• Simplexity • has it’s origins in the field of Science - Jack Cohen (the scientist) and his
collaborator Ian Stewart are authors of the book “The Collapse of Chaos” (1995), a non-fiction
work that attempts to explain chaos theory and complex systems to a general audience.. The
complexity of algorithms and of mathematical problems is one of the core subjects of
theoretical computer science – which prompted computer scientists Broder and Stolfi to
whimsically describe • Simplexity • as a concept worthy of just as much attention as its twin
paradigm, complexity, attracts.
• Simplexity • has been popularised in the book “Simplexity: Why Simple Things Become
Complex (and How Complex Things Can Be Made Simple)” by Jeffrey Kluger – which describes
some of the ways in which simplexity theory can be applied to many scenarios across multiple
disciplines. Kluger offers a look at simplexity in economics, sports, linguistics, technology,
medicine, and human behaviour. Simplexity also provides insight into how futurists and
strategists can improve their frameworks, paradigms and models - by understanding how the
interplay of simplicity (Linear Systems) and chaos (the possibility of random events introducing
uncertainty and disruption) can form both complexity (Complex Adaptive Systems) and also
simplexity (reduction of Complex Systems into an integrated set of linear or simplistic systems
interacting with random events).
• Simplexity • is an intriguing future paradigm that will drive new thinking in many novel,
exciting and surprising directions over the coming years. • Simplexity • is an elegant and
pleasing paradigm which will feature prominently for a good while into the future.

Michelangelo was once asked how he created his sculptures. “I take a stone – and
remove anything which is not required”. – thus demonstrating that a beautiful
artefact may be created by the removal of everything which detracts from the
intrinsic simplicity of that beauty. • Simplexity • shows itself in subtle design that at
first glance appears to be something plain and simple; easy to use as well as beautiful
to behold – but on closer inspection it becomes apparent that the artefact is
constructed from many harmonious layered components. The complex functionality
of the design is muted and disguised as sophisticated components integrated within
the • Simplexity • paradigm of a compelling idea, elegant functional concept or
simple design vision.
In the future, a consumer-oriented Western world, faced with diminishing availability
of natural resource and increased costs - may become less materialistic and
consumption-focused as we are driven to think more carefully about how we use and
recycle valuable possessions. Smart Devices (Laptops, Tablets and Smart Phones) with
intuitive user interfaces are lifestyle accessories and high-status fashion items which
may be continuously and easily personalised, customised and configured to uniquely
respond to their owners changing needs – thus complying with the simple beauty of
the • Simplexity • paradigm.

Already, trade mechanisms are emerging to recycle these expensive • Simplexity •
items for refurbishment and re-sale – as an integral part of a new and emerging
strategy for sustainably acquiring and replacing our artefacts and goods. We already
have has systems and processes in place for re-using and re-cycling vehicles for many
years. This trend will tend to drive manufacturers to make fewer, but better quality
artefacts – and in turn multi-owner consumer behaviour will have to be reflected in
the future Recycling processes - Recovery. Refurbishment and Resale - embedded in
the corporate planning and strategy of manufacturing economies – such as China.
• Simplexity • artefacts will have a longer useful lifespan under multiple owners in
order to continue to offer a Lifetime Cost of Ownership which remains inexpensive in
real terms. This will bring many challenges to manufacturing enterprises – with their
responsibility to recover and recycle expensive items for re-manufacturing,
refurbishment and re-sale - and at the same time have to contend with a
transformation of domestic and export markets in which demand for new goods is
cyclical – fragile when new ideas are scarce - strong when multiple trends are
emerging and interacting. In order to succeed, businesses will become increasingly
transparent, collaborative and interactive with consumers – or face becoming unable
to compete effectively. • Simplexity • artefacts will cost more, but still remain
relatively inexpensive in real terms in order to pander to the perfidious wishes and
desires of western consumers.

Randomness
The Nature of Uncertainty
The Nature of Uncertainty – Randomness
Classical (Newtonian) Physics – apparent randomness is as a result of Unknown Forces
Relativity Theory – any apparent randomness or asymmetry is as a result of Quantum effects
Quantum Mechanics – all events are truly and intrinsically both symmetrical and random
Wave (String) Theory –apparent randomness and asymmetry is as a result of Unknown Forces

Minkowski Space-time Continuum
• In1907 the German mathematical physicist Hermann Minkowski developed the concept
of a single space-time continuum - which provides a conceptual framework for all the
mathematical proofs used in relativity - including Albert Einstein's general and special
theory of relativity. Minkowski space-time is an integrated and unified four-dimensional
continuum - composed of three Positional Dimensions (Loci or Vectors x, y and z
coordinates) defining Space (vector / position) – which is entirely integrated and wholly
unified with a fourth Temporal Dimension (t coordinate) – defining Time (history).
• Minkowski quickly realised that the preliminary work on relativity theory could best be
explained and understood in a multi-dimensional universe which extended beyond the
three spatial dimensions (x, y and z axes) - to include a temporal dimension (t axis) - as
the foundation of a new, non-Euclidean four-dimensional geometry. Minkowski coupled
the two separate dimensions of Space and Time together to create a unified four-
dimensional Space-Time continuum - which was then employed in his own treatment of
a four-dimensional study of electrodynamics. This study involved a combination of two
previously separate systems – Space (with x, y and z axes) and Time (t axis) – to form
Space-Time (with x, y, z and t axes). He noticed that the invariant interval between
two events shared some of the properties of distance in Euclidean three-dimensional
geometry and formulated this invariant interval as the square root of a sum and
difference of squares of the intervals of both Space and Time.

Minkowski
Space-Time
continuum
• In an attempt to
understand the
previous works of
Lorentz and Einstein,
during 1907 Hermann
Minkowski synthesised
a revolutionary four-
dimensional view of a
single, integrated
space-time continuum.
• Until the development
of Minkowski space-
time continuum - the
three-dimensional
coordinate system
describing Space
(position) in Classical
(Newtonian) physics
and the other universal
dimension, the flow of
Time, were considered
to exist independently.

• Space (position) and Time (history) flow inextricably together in a single direction –
towards the future – just as a river can only flow downhill, towards the sea. Space and
Time can only exist together within a single, unified Space-Time continuum. Without
Space – there can be no Time , and without Time – there can be no Space.
• Minkowski space-time is also often referred to as Minkowski space or the Minkowski
universe. . In order to exploit the principles of the Minkowski space-time continuum, this
type of coupling must fully demonstrate that the history of a particle or the
transformation of a process over time is dependent on both its spatial and historical
components. Minkowski space-time is used predominately in the study of relativity,
although it can also be applied to other subjects and fields of human endeavour
involving the coupling of time and spatial vectors –for example, in “Big Data” which is
used for Predictive Analytics, Geospatial Propensity Modelling and Future Analysis. .

• The three-dimensional coordinate
system describing Space (position) in
Classical (Newtonian) physics along with
the other universal dimension, the flow of
Time (history), were considered to exist
and act entirely independently of each
other - until the synthesis of Space-Time
• During 1907, in an attempt to gain an
understanding of the previous work of
Lorentz and Einstein, the German
Mathematician Hermann Minkowski
developed a four-dimensional view of the
universe as a single, integrated and
unified Space-Time continuum.
• In order to demonstrate the principle
properties of the Minkowski Space-
Time continuum – any type of Spatial
and Temporal coupling must be able to
show over time that the History of a
particle or the Transformation of a
process is fully and entirely dependent
on both its Spatial (positional) and
Temporal (historic) components.

• Using this concept, events which are localized in both space and time may be
considered as the analogues of points in three-dimensional geometry. Thus the Time
dimension in the history of a single particle or the timeline of an event in Minkowski
space-time - resembles the arc of a curve in a three-dimensional Space, and is thus
fully dependent on both its spatial and historical components.
• Like Space, Time is a Dimension – but Time only flows in a single direction, as does a
River. Time and Space can only exist together within a single, unified Space-Time
continuum. Without Time – there can be no Space, and without Space – there can be
no Time. Minkowski space-time is also often referred to as Minkowski space or the
Minkowski universe. Minkowski space-time is used predominately in the study of
relativity, although it can also be applied to other subjects involving the coupling of
spatial and temporal vectors – such as Futures Studies. In order to exploit the
Minkowski space-time continuum, this type of coupling must demonstrate that the
history of a particle or the transformation of a process over time is fully dependent
on both Space and Time.

Minkowski
Space-Time
continuum
• Space (position) and
Time (history) flow
inextricably together in
a single direction –
towards the future.
• In order to exploit the
principle properties of
the Minkowski space-
time continuum, any
type of Spatial and
Temporal coupling
must be able to fully
demonstrate that the
History of a particle
or the Transformation
of a process over time
is entirely dependent
on both its spatial and
historical components.

Heat Death of the Universe
• Space (position) and Time (history) flow inextricably together in a single direction –
towards the future – just as a river can only flow downhill, towards the sea. Space
and Time can only exist together within a single, unified Space-Time continuum.
• Along the path leading towards Universal Heat Death is the theory of increasing
Universal entropy (disorder, chaos). Over time, the universe is experiencing a slow
dissipation of its stored mechanical energy. The second law of thermodynamics states
that entropy tends to increase within an isolated thermal system. The concept at the
heart of Heat Death is that the universe is the largest isolated system in existence, and
it naturally has an entropy which increases over time. As time goes on, the entropy
(chaos) in the universe will approach its potential maximum – at which point Relativity,
Chemical, Physical, Thermodynamic and Quantum universal processes would cease.
• There is some speculation that entropy may be stored in black holes. As black holes
evaporate radiation, they consequently release stored entropy (which is what causes
objects to get sucked into them) - which in turn increases universal entropy. In order
for the universe to exist, the entropy level has to remain below the maximum value, as
it does now. It is estimated that we are currently at an entropy level of 10^104, while
the maximum remains at 10^122. We are a long way from reaching this level, but
entropy today is significantly higher than that of the very early universe (10^88).

Heat Death of the Universe
• As the universe eventually reaches total thermal equilibrium, anything which remains in
the Universe will be at absolute zero. Atoms and subatomic particles inside of the nuclei
of stars will begin a slow decay and evaporation - by releasing radiation (the epoch of
degeneration). As matter continues to be released as radiation, over an unimaginable
long period of time even the ultra-massive black holes at the centre of Galaxies will
ultimately evaporate away to nothing. Eventually there will no longer be any physical
matter left within the universe. Finally, the night sky will become both completely empty
and totally dark – the dark era. When the last atom eventually decays, evaporates and
disappears, there is no longer anything left to mark the passage of time. Without Matter
and Energy, there can be no Space. Without Space, there can be no Time, and without
Time, there can be no Universe. So Space collapses and disappears – the “Big Crunch”.
• The universe is so vast, and is still constantly expanding – so there is still much energy in
the universe and still much variation in the amount of energy between different places (a
multi-thousand degree Kelvin star vs. empty space at just above absolute zero), so that
Heat Death of the Universe is not going to happen for 10^37 years – by which time all
matter will have decayed, evaporated and radiated away – so all that remains is radiation.
As discussed before, without Space, there can be no Time, and without Time, there can
be no Space – resulting in the end (“heat-death”) of the Universe – the “Big Crunch”.

The Flow of Information through Time
• Time Present is always in some way inextricably woven into both Time Past and Time
Future – with the potential, therefore, to give us notice of future random events – before
they actually occur. Chaos Theory suggests that even the most subliminal inputs, so
minute as to be undetectable, may ultimately be amplified over many system cycles – to
grow in influence and effect to trigger dramatic changes in future outcomes. So any
given item of Information or Data (Global Content) may contain faint traces which hold
hints or clues about the outcomes of linked Clusters of Past, Present and Future
Events.
• Every item of Global Content that we find in the Present is somehow connected with
both the Past and the Future. Space-Time is a Dimension – which flows in a single
direction, as does a River. Space-Time, like water diverted along an alternative river
channel, does not flow uniformly – outside of the main channel there could well be
“submerged objects” (random events) that disturb the passage of time, and may
possess the potential capability of creating unforeseen eddies, whirlpools and currents
in the flow of Time (disorder and uncertainty) – which in turn posses the capacity to
generate ripples, and waves (chaos and disruption) – thus changing the course of the
Time-Space continuum. “Weak Signals” are “Ghosts in the Machine” of these
subliminal temporal interactions – with the capability to contain information about future
“Wild card” or “Black Swan” random events.

Space-time Disturbances
• Time, like Water, does not flow uniformly – outside the depths of the main
channel within which Time travels, there may also be submerged objects
(random events) that posses the ability to cause disturbances, eddies and
currents in the flow (disorder and uncertainty) – which in turn have the capacity
to generate ripples, whirlpools and waves (chaos and disruption) that flow
through the Space-Time continuum bringing with it the possibility for change -
thus precipitating novel and unexpected outcomes.
• These unpredictable temporal interactions (random events) may interact with
current and emerging waves, patterns and trends to cause Chaos, Disorder,
Uncertainty and Disruption – which in turn have the capacity tp generate Wild
Card or Black Swan Events – manifestations of randomness that act in such a
way as to prevent Time flowing smoothly and uniformly towards an unerringly
predictable outcome or conclusion. Random Events change the flow of Time –
thus the deflected course taken by Time interacting with Random Events means
that the Future becomes unpredictable. Instead of smooth, linear outcomes – we
experience surprises.

introduces into our daily lives. Research into stochastic (random) processes
looks towards how we might anticipate, prepare for and manage the chaos and
uncertainty which acts on complex systems – including natural systems such as
Cosmology and Climate, as well as human systems such as Politics and the
Economy - in order that we may anticipate future change and prepare for it…..
– Any apparent randomness is as a result of Unknown Forces
– Apparent randomness or asymmetry is as a result of Quantum effects
– Every Quantum event is truly and intrinsically both symmetrical and random
• Wave Mechanics (String Theory)
– Any apparent randomness and asymmetry is as a result of Unknown Forces

Space-Time v. Energy-Matter
Domain Object Process Outcome Timeline Size Range
Classical Mechanics
(Newtonian Physics)
Common, Everyday
and Celestial Objects
Motion Change of
Position
4.6 x 10.(12) yr 1 Solar Mass
Thermodynamics Energy (Entropy and
Enthalpy)
Transformation
and Flow
Change in State 10.(37) yr 10.(34) atoms
Biology Organisms Evolution Life and Death 3.7 x 10.(12) yr
Chemistry Molecules Transformation Change in State 1.37 x 10.(13) yr
Atomic Theory Atoms Interaction Change in State 1.37 x (10) 13 yr
Quantum Mechanics Sub-atomic particles Interaction Objects created
and destroyed
10.(37) yr 10.(34) atoms
Geology Earth Transformation Change in State
and Position
4.6 x 10.(12) yr 1 Earth Mass
Astronomy Observable Celestial
Objects
Motion Change in State
and Position
1.37 x 10.(13) yr 10.(24) solar
masses
Celestial Objects
Transformation Change in State
and Position
10.(37) yr 10.(34) atoms
Relativity Theory The Universe Interaction Change in State
and Position
10.(37) yr 10.(34) atoms
Wave Mechanics
(String Theory or
Quantum Dynamics)
The Universe,
Membranes and
Hyperspace
Motion, Flow,
Transformation
and Interaction
Objects created
and destroyed,
with change in
State and Position
10.(37) yr 10.(34) atoms

Space-Time v. Energy-Matter
– governs the behaviour of unimaginably small objects (fundamental sub-atomic particles)

Wave Mechanics (String Theory)
Metaphysical
Philosophy
metaphysical
philosophy
In Western
philosophy,
metaphysics has
become the study of
the fundamental
nature of reality -
what it is, why it is,
and how we are to
understand its
properties.....
Related: -
Alchemy
Ontology
Taxonomy
Classification
Natural History
Natural Philosophy
Systemic
Methodology
String Theory is a set of mathematical models which
attempts to address the four known fundamental
interactions – gravitation, electromagnetism, strong
nuclear force, weak nuclear interaction – integrated
into a single, universal Theory of Wave Dynamics. .
In theoretical physics, M-theory is an extension of
string theory in which the 11 dimensions of space-
time are identified as 7 higher-dimensions plus the 4
common dimensions (11D st = 7 hd + 4D).
Proponents believe that the 11-dimensional theory
unites all five 10-dimensional string theories (10D st =
6 hd + 4D) and supersedes them. Though a full
description of the theory is not known, the low-entropy
dynamics are thought to be supergravity interacting
with 2- and 5-dimensional membranes in a single,
unified Theory of Wave Dynamics.
Gabriele Veneziano – is an Italian
theoretical physicist and string
theorist. His 1968 dual resonance
model of the strong interaction was
the first component of string theory to
be described - he is regarded as a
founder of this field of String Theory.
Brian Greene, author of the book
about string theory, The Elegant
Universe, was educated at Harvard
and Oxford, graduating in 1987. After
working at Harvard and Cornell, he is
currently a Professor of Physics and
Mathematics at Columbia.

The Theory of Hyperspace - Prof. Michiu Kaku
• According to this theory, before the Big Bang, our cosmos was actually a
perfect ten-dimensional universe, a world where inter-dimensional travel was
possible. However, this ten-dimensional universe "cracked" in two, creating
two separate universes: a four- and a six- dimensional universe. The universe
in which we live was born in that cosmic cataclysm. Our four-dimensional
universe expanded explosively, while our twin six-dimensional universe
contracted violently, until it shrank to almost infinitesimal size.
• This would explain the origin of the Big Bang. If correct, this theory
demonstrates that the rapid expansion of the universe was just a rather minor
aftershock of a much greater cataclysmic event, the cracking of space and
time itself. The energy that drives the observed expansion of the universe is
then found in the collapse of ten-dimensional space and time. According to
this theory, the distant stars and galaxies are receding from us at astronomical
speeds because of the original collapse of ten-dimensional space and time.
This theory predicts that our universe still has a dwarf twin, a companion
universe containing the residual dimensions, curled up into a small six-
dimensional ball that is too small to be detected or observed.....

String Theory of Hyperspace - Prof. Michiu Kaku
• Many scientists now believe, although we cannot yet prove it, that the multiversity (multiple
universes) hyperspace which contains our own universe – can exist in up to eleven dimensions.
Think of this hyperspace as a multi-dimensional arena in which there are floating a vast number
of bubbles. The surface membrane of every one of these bubbles represents an entire universe,
so our own universe exists on a single bubble membrane. It’s a three dimensional bubble. This
three dimensional bubble is rapidly expanding – according to the Big Bang theory - sometimes
these bubbles can bump into each other, at other times they could split apart – this is the event
that theoretical cosmologists think caused the Big Bang. So we even have a theory of the origin
of the Big Bang itself. In string theory we can have bubbles consisting of different dimensions.
• The highest stable number of dimension in a universe is 11. Universes containing dimensions
beyond 11 become unstable and collapse. When we attempt to describe the mathematics
behind the theory of a 13-, 15-dimensional universe, those universes are intrinsically unstable
and all of them rapidly collapse down to an 11-dimensional universe. Even in the case of an 11-
dimensionsional universe - bubbles can split apart to become 3-dimensional, 4-dimensional , 5-
dimensional and 6-dimensional universes. These bubbles are membranes, so for short we call
them “branes”. Branes may exist with different numbers of dimensions. If we use P to represent
the total number of dimensions belonging to each bubble or membrane – then they become p-
branes. So a p-brane is simply a universe with variable numbers of dimensions – large numbers
of which are floating in a much larger arena - the hyperspace which we discussed earlier.

• String Theory is a set of mathematical models which attempts to address the four known
fundamental interactions – gravitation, electromagnetism, strong nuclear force, weak nuclear
interaction – integrated into a single, universal Theory of Wave Dynamics.
• From the late 1970s, quantum field theory and Einstein's general theory of relativity (classical
theory of gravity) proved to be suitable theoretical frameworks to explain many or most of
observed features of our universe, from elementary particles like electrons and protons to
evolution of the universe in the cosmological scale. There were still , however, many
fundamental problems which remained unexplained and unresolved. The elevation of gravity
to the quantum level remained one of the grand unsolved problems since the days of Einstein,
while other smaller but equally mysterious problems - such as how to solve quantum chromo-
dynamics (QCD), why the cosmological constant of our universe is so small (thought to vanish
at some point but later proven otherwise by observation), and whether properties of black
holes are consistent with quantum principle – still remained unexplained and unresolved.
• Now, 30 years since on, many theoretical physicists seem to believe that string theory did or
will offer answers to many such questions. The original idea of string theory that everything in
nature originates from loops or segments of strings moving in the relativistic way, seemed
ludicrous at first. Yet, its unique ability to define a quantum mechanically consistent gravity is
not something that theorists could easily resist. Existence of gravity in string theory was
recognized as early as 1975, which was then elevated to a realistic computational framework
in 1980's, but putting this to actual use was another problem.

• Better understanding and use of string theory became possible through the realization
in the 1990's that there are hidden symmetries, known as "duality." Recently, it has
been shown that a strongly coupled regime of one superstring theory can sometimes be
understood as a weakly coupled regime of another, "dual" superstring theory. Such
relations demonstrated that different models of superstrings are actually different
perturbative realizations of one and the same theory. One ultimate theory, which was
conjectured to contain all superstring theory as special cases, has been named M
theory. Another lesson from these developments in the 1990's is that string theory is not
only made up of open and closed strings, but all kinds of other extended objects which
are postulated to exist in Hyperspace – including D-branes and M-branes.
• Probably the most celebrated example of dualities, found in 1997 and has been
exploited and generalized widely since then, is AdS/CFT. In its most general
reincarnation, this model asserts equivalence between certain pairs of open string
theory and closed string theory. In practice, one actually considers the limiting cases
where the open string side reduces to a strongly coupled gauge field theory and/or the
closed string side reduces to classical gravitational theory.

• The equivalence offers completely new methods for solving many strongly interacting
theories, most notably quantum chromo-dynamics (QCD). The very acute issue of black
hole in quantum gravity was also addressed through these developments, resulting in a
consensus among many theoretical physicists that quantum principle is probably not
destroyed by existence of quantum black holes in string theory. A complete resolution of
the problem, applicable to all type of black holes is, however, still unavailable.
• Pioneers of string theory such as Michael Green and Michio Kaku hoped that they might
be able to "derive" a unique theory of universe where every fundamental law of nature can
be predicted unambiguously and accurately. With better understanding over the last twenty
years, we now begin to realize that this hope was probably mislaid. String theory is far
more than a single unified theory of the universe. It proved to be a new physical modelling
paradigm and framework – even more so than the ubiquitous quantum field theory.
• Whether and how we can describe a new model of the universe within this framework is a
very highly constrained and difficult problem, which still carries significant uncertainty when
compared to conventional model building methods in particle physics and in cosmology.

Space-Time Analytics – The Temporal Wave
• The Temporal Wave is a novel and innovative method for Visual Modelling and Exploration of
Time-series Geospatial Data Sets – data with dimensions which exist simultaneously with a Time
(history) and Space (geographic) context. The problems encountered in exploring and analysing
vast volumes of spatial–temporal information in today's data-rich landscape – are becoming
increasingly difficult to manage effectively. In order to overcome the problem of data volume and
scale in a Time (history) and Space (location) context requires not only traditional location–
space and attribute–space analysis common in GIS Mapping and Spatial Analysis - but now with
the additional dimension of time–space analysis. The Temporal Wave supports a new method of
Visual Exploration for Geospatial (location) data within a Temporal (timeline) context.
(timeline) dataset - along with data visualisation techniques - thus improving accessibility,
exploration and analysis of the huge amounts of geo-spatial data used to support geo-visual “Big
Data” analytics. The temporal wave combines the strengths of both linear timeline and cyclical
wave-form analysis – and is able to represent data both within a Time (history) and Space
(geographic) context simultaneously – and even at different levels of granularity. Linear and
cyclic trends in space-time data may be represented in combination with other graphic
representations typical for location–space and attribute–space data-types. The Temporal Wave
can be used in roles as a time–space data reference system, as a time–space continuum
representation tool, and as time–space interaction tool.

The Flow of Information through Time
• Time Present is always in some way inextricably woven into both Time Past and Time Future –
with the potential, therefore, to give us notice of future random events – subliminal indications
before they actually occur. Chaos Theory suggests that even the most subliminal inputs, so
minute as to be undetectable, may ultimately be amplified over many system cycles – to grow
in influence and effect to trigger dramatic changes in future outcomes. So any given item of
Information or Data (Global Content) may contain faint traces which hold hints or clues about
the outcomes of linked Clusters of Past, Present and Future Events.
• Every item of Global Content that we find in the Present is somehow connected with both the
Past and the Future. Space-Time is a Dimension – which flows in a single direction, as does a
River. Space-Time, like water diverted along an alternative river channel, does not flow
uniformly – outside of the main channel there could well be “submerged objects” (random
events) that disturb the passage of time, and may possess the potential capability of creating
unforeseen eddies, whirlpools and currents in the flow of Time (disorder and uncertainty) –
which in turn posses the capacity to generate ripples, and waves (chaos and disruption) – thus
changing the course of the Space-Time continuum. “Weak Signals” are “Ghosts in the
Machine” of these subliminal temporal interactions – with the capability to contain information
about future “Wild card” or “Black Swan” random events.

Temporal Disturbances in the
Space–Time Continuum
• Weak Signals, Strong Signals, Wild Cards and Black Swan Events – are a sequence
of waves linked and integrated in ascending order of magnitude, which have a common
source or origin - either a single Random Event instance or arising from a linked series
of chaotic and disruptive Random Events - an Event Storm. These Random Events
propagate through the space-time continuum as a related and integrated series of waves
with an ascending order of magnitude and impact – the first wave to arrive is the fastest
travelling,- Weak Signals - something like a faint echo of a Random Event which may in
turn be followed in turn by a ripple (Strong Signals) then possibly by a wave (Wild Card)
- which may indicate the unfolding a further increase in magnitude and intensity which
finally arrives catastrophically - something like a tsunami (Black Swan Event).
Sequence of Events - Emerging Waves Stage View of Wave Series Development
1. Random Event 1. Discovery
2. Weak Signals 1.1 Establishment
3. Strong Signals 1.2 Development
4. Wild Cards 2. Growth
5. Black Swan Event 3. Plateau
4. Decline
5. Collapse
5.1 Renewal
5.2 Replacement

Space-Time Analytics
• 4D Geospatial Analytics is the
profiling and analysis of large
aggregated datasets in order to
determine a ‘natural’ structure of
groupings provides an important
technique for many statistical and
analytic applications.
• Environmental and Demographic
Geospatial Cluster Analysis - on the
basis of profile similarities or
geographic distribution - is a statistical
method whereby no prior assumptions
are made concerning the number of
groups or group hierarchies and
internal structure. Geo-spatial and
geodemographic techniques are
frequently used in order to profile and
segment populations by ‘natural’
groupings - such as common
behavioural traits, Clinical Trial,
Morbidity or Actuarial outcomes - along
with many other shared characteristics
and common factors.....

Space-Time Analytics – London Timeline

Space-Time Analytics – London Timeline
• How did London evolve from its creation as a Roman city in 43AD into the crowded, chaotic
cosmopolitan megacity we see today? What will London look like in the future? The London
Evolution Animation takes a holistic view of what has been constructed in the capital over
different historical periods – what has been lost, what is saved and what will be protected.
• Greater London covers 600 square miles. Up until the 17th century, however, the capital city
was crammed largely into a single square mile which today is marked by the skyscrapers which
are a feature of the financial district of the City. Unlike other historical cities such as Athens or
Rome, with an obvious patchwork of districts from different periods, London's individual
structures scheduled sites and listed buildings are in many cases constructed gradually by parts
assembled during different periods. Researchers who have tried previously to locate and
document archaeological structures and research historic references will know that these
features, when plotted, appear scrambled up like pieces of different jigsaw puzzles – all
scattered across the contemporary London cityscape.
• This visualisation, originally created for the Almost Lost exhibition by the Bartlett Centre for
Advanced Spatial Analysis (CASA), explores the historic evolution of the city by plotting a
timeline of the development of the road network - along with documented buildings and other
features – through 4D geospatial analysis of a vast number of diverse geographic,
archaeological and historic data sets.

Spatial versus Temporal Domains
Spatial Analysis
(Location)
Temporal
Analysis
(History)
Sub-atomic
Phenomena Transitive
Phenomena
Long-lived
Phenomena
Space-Time Continuum
Global
Phenomena Economic Analysis
Cosmic Space-Time
Temporal
Analysis
Earth Sciences
Applied mathematics
Chemistry
Engineering
Geography
Geology
Geo-physicsEnvironmental Sciences
Archaeology
Palaeontology
Futures Studies
Weather Forecasting
Strategic Foresight
Predictive Analytics
Climate Change
Statistics
Cluster TheoryParticle Physics
Quantum Mechanics
Phenomenology
Anthropology and Pre-history
Social Sciences
Sociology
Economics
Life Sciences
Biology Ecology
String Theory
Cosmology
Astronomy
Relativity
Astrophysics
Astrology
Future Management
Pure mathematics
Universal
Phenomena
Local
Phenomena
Regional
Phenomena
Short-lived
Phenomena
Atomic Space-Time
Micro-
Phenomena

Randomness
Patterns in the Chaos

Event Clusters and Connectivity
A
B
C
D
E
G
H
F
The above is an illustration of Event relationships - how Events might be connected. Any detailed,
intimate understanding of the connection between Events may help us to answer questions such as: -
• If Event A occurs does it make Event B or H more or less likely to occur ?
• If Event B occurs what effect does it have on Events C,D,E, F and G ?
Answering questions such as these allows us to plan our Event Management approach and Risk
mitigation strategy – and to decide how better to focus our Incident / Event resources and effort…..

• Aggregated Event includes coincident, related, connected and interconnected Event: -
• Coincident - two or more Events appear simultaneously in the same domain –
but they arise from different triggers (unrelated causal events)
• Related - two more Events materialise in the same domain sharing common
Event features or characteristics (may share a possible hidden common trigger or
cause – and so are candidates for further analysis and investigation)
• Connected - two more Events materialise in the same domain due to the same
trigger (common cause)
• Interconnected - two more Events materialise together in a Event cluster, series
or “storm” - the previous (prior) Event event triggering the subsequent (next) event
in an Event Series…..
• A series of Aggregated Events may result in a significant cumulative impact - and are
therefore frequently identified incorrectly as Wild-card or Black Swan Events - rather
than just simply as event clusters or event “storms”.....

1
2
3
4
5
7
8
6
The above is an illustration of Event relationships - how Risk Events might be connected. A detailed and
intimate understanding of Event clusters and the connection between Events may help us to understand: -
• What is the relationship between Events 1 and 8, and what impact do they have on Events 2 - 7 ?
• Events 2 - 5 and Events 6 and 7 occur in clusters – what are the factors influencing these clusters ?
Answering questions such as these allows us to plan our Risk Event management approach and mitigation
strategy – and to decide how to better focus our resources and effort on Risk Events and fraud management.
Claimant 1
Risk Event
Claimant 2
Residence
Vehicle
Event
Cluster

Aggregated Event Types
ATrigger A
Coincident Events
BTrigger B
Event
Event
CTrigger 1
Related Events
DTrigger 2
Event
Event
E
Trigger
Connected Events
Event
EventF
GTrigger
Inter-connected Events
Event Event
H

Randomness
• Disruptive Future paradigms in Future Studies along with Wave (String) Theory in Physics - alert
us to the possibility of chaotic and radically disruptive Random Events that may generate ripples
which propagate outwards from the causal event like a wave surging across Space-Time.
Some waves might travel through the Space-Time continuum at slightly different speeds due to
the granularity of the substance of the Space-Time Continuum at the most atomic level – or due
to the presence and influence of Unknown Forces (dark energy, dark flow and dark matter).
• Certain types of wave may thus be able to travel faster than others – either due to Unknown
Forces (dark energy, flow, matter), or some types of Wave that propagate through Space-Time
more rapidly than other wave types - or because certain types of wave form can take advantage
of a “short cut” across a “warp” in the Space-Time continuum - a “warp” which brings two discrete
points from different Hyperspace Planes close enough together to allow a Hyperspace Jump.
ComplexitySimplicity
Simplexity
Ordered
Complexity
Disordered
Complexity
Complex Adaptive
Systems (CAS)
Linear
Systems
(element and interaction density)
ChaosOrder

Randomness
• Space (position) and Time (history)
flow inextricably together in a single
direction – towards the future. In
order to demonstrate the principle
properties of the Minkowski Space-
Time Continuum, any type of
Spatial and Temporal coupling in a
Model or System must be able to
demonstrate over time that the
History of a particle or the
Transformation of a process are
fully and totally dependent on both its
Spatial (positional) and Temporal
(historic) components - acting
together and in unison.
• Over any given time interval -
multiple Hyperspace Planes stack up
on top of each other to create a time-
line which extends along the
temporal axis of the Minkowski
Space-Time Continuum.
Minkowski Space-Time Continuum.

Randomness
• Neither data-driven nor model-driven representations of the future are able alone, to deal with
the concept of randomness (uncertainty). We therefore need to consider and factor in further
novel and disruptive systemic modelling approaches in order to help us to understand how
both Natural Systems (Cosmology, Climate) and Human Activity Systems (Economics, Crowd
Behaviour) perform.
• Systems Modelling techniques offer us the possibility to manage uncertainty by searching for,
detecting and identifying Weak Signals – which are like the faint seismic disturbances which
warn us of the coming of Earth-quakes and Tsunamis and are always followed by Strong
Signals, Wild Card or Black Swan events – may help us to predict Natural Events like Earth-
quakes and Tsunamis – as well as Human Processes such as the rise and fall of Commodity,
Stocks and Share prices in Global Markets. Weak Signal, Wild Card or Black Swan events
may in this way be factored into our Future Systems Models.
Simplexity
Ordered
Complexity
Disordered
Complexity
Complex Adaptive
Systems (CAS)
Linear
Systems
ChaosOrder

Stochastic Processes –
Random Events
– any apparent randomness is as a result of Unknown Forces
– all events are truly and intrinsically both symmetrical and random
– any apparent randomness or asymmetry is as a result of Quantum Dynamics
Wave Mechanics (String Theory) – integrates the behaviour of every size & type of object
– apparent randomness and asymmetry is as a result of Quantum and Unknown Forces

• Randomness makes precise prediction of future outcomes impossible. We are unable to predict any
outcome with any significant degree of confidence or accuracy – due to the inherent presence of
randomness and uncertainty associated with Complex Systems. Randomness in Complex Systems
introduces chaos and disorder – causing disruption. Events no longer continue along a predictable linear
course leading towards an inevitable outcome – instead, we experience surprises.
• What we can do, however, is to identify the degree of uncertainty present in those Systems, based on
known, objective measures of System Order and Complexity - the number and nature of elements
present in the system, and the number and nature of relationships which exist between those System
elements. This in turn enables us to describe the risk associated with possible, probable and alternative
Scenarios, and thus equips us to be able to forecast risk and the probability of each of those future
Scenarios materialising.
• If true randomness exists and future outcomes cannot be predicted – then what is the origin of
that randomness? For example, are unexpected outcomes simply apparent as a result of sub-
atomic nano-randomness existing at the quantum level – such as uncertainty phenomena etc…..?
• The Stephen Hawking Paradox postulates that uncertainty dominates complex and chaotic systems to
such an extent that future outcomes are both unknown - and unknowable. The working context of this
paradox is restricted, however, to the realm of Quantum Mechanics – where each and every natural event
that occurs at the sub-atomic level is truly intrinsically and completely both symmetrical and random.

• What is the explanation for randomness evident in all high-order phenomena
found in nature…?
• In order to obtain realistic glimpses into the Future, - then the major paradigm
differences between the Actual Reality that we experience every day and our
limited Systemic Models which attempt to simplify, abstract and simulate reality -
must be clearly distinguished between and understood.
• When we design our Systemic Models representing Actual Reality – such as the
Economy, Geo-political systems, Climate Change, Weather and so on - if we are
lucky enough, then some high-order phenomena found in nature may be captured
by a random rule; and with even more luck, by a deterministic rule (which can be
regarded as a special case of randomness) - but if we are unlucky - then those
rules might not be no captured at all. Regarding the nature of reality - it still
remains unclear what factors distinguish truly random phenomenon found in
nature at the Quantum level (e.g. radioactive decay?) from Random Events which
are triggered by unseen forces.

• Can we accept that these natural phenomena are not truly random at all – that is, given sufficient
information such as complete event data sets - it is possible to predict random events? If so,
are all random events the result of the same natural phenomenon - unseen or hidden forces ? “
• Classical (Newtonian) Physics describe the laws which govern all of the systems and objects that we are
familiar with in our everyday routine lives. Relativity Theory, on the other hand, describes unimaginably
large things, whilst Quantum Mechanics describes impossibly small things – and Wave Theory (String
Theory) attempts to describe everything. True randomness does not really exist in Classical (Newtonian)
Physics – the laws which control Chaos and Complex Systems that govern every aspect of our life on Earth
today – from Natural Systems such as Cosmology, Astronomy, Climatology, Geology and Biology through to
Human Activity Systems such as Political, Economic and Sociological Complex Adaptive Systems (CAS).
Randomness is simply the results of those forces which are not known, not recognised, not understood, are
not under the control of the observer or simply occur outside of the known boundaries of observable system
components – but, nevertheless, must still exist and exert influence over the system. Over many System
Cycles, immeasurably small inputs interacting with Complex System components and relationships - may
be amplified into extremely significant outputs.....
1. Classical (Newtonian) Physics – apparent randomness is as a result of Unknown Forces
2. Relativity Theory – any apparent randomness or asymmetry is as a result of Quantum effects
3. Quantum Mechanics – all events are truly and intrinsically both symmetrical and random
4. Wave (String) Theory – apparent randomness and asymmetry is as a result of Unknown Forces -
which may in turn have their origination in Quantum Mechanics effects

Weak Signals, Wild Cards and Black Swan Events
• Economic systems tend to demonstrate Complex Adaptive System (CAS) behaviour –
rather than a simple series of chaotic “Random Events” – very similar to the behaviour of
living organisms. The remarkable long-term stability and resilience of market economies is
demonstrated by the impact and subsequent recovery from Wild Card and Black Swan
Events. Surprising pattern changes occur during wars, arm races, and during Republican
administrations, causing unexpected stock market crashes - such as oil price shocks and
credit crises. Wave-form Analytics for non-stationary time series analysis opens up a new
and remarkable opportunity for business cycle studies and economic policy diagnostics.
• The role of time scale and preferred reference from economic observation is explored in
detail. For example - fundamental constraints for Friedman's rational arbitrageurs are re
examined from the view of information ambiguity and dynamic instability. Alongside
Joseph Schumpter’s Economic Wave Series and Strauss and Howe’s Generation Waves,
we also discuss Robert Bronson's SMECT Forecasting Model - which integrates both
Business and multiple Stock-Market Cycles into its structure.....
• Composite Economic Wave Series
– Saeculum - Century Waves
– Generation Waves (Strauss and Howe)
– Joseph Schumpter’s Economic Wave Series
– Robert Bronson’s SMECT Forecasting Model

• Randomness may be somewhat difficult to demonstrate, as Randomness in chaotic system
behaviour is not always readily or easily distinguishable from any other “noise” that we may
find in Complex Systems – such as foreground and background wave harmonics, resonance
and interference patterns. Complex Systems may be influenced by both internal and
external factors which remain hidden - unrecognised or unknown. These unknown and
hidden factors may lie far beyond our ability to detect them. The existence of weak internal
or external forces simply may not be visible to the observer – the subliminal temporal forces
which nevertheless can influence Complex System behaviour in such a way that the
presence of imperceptibly tiny inputs, propagated and amplified over many system cycles -
are able to create massive observable changes to outcomes in complex system behaviourr.
• Randomness. Neither data-driven nor model-driven macro-economic or micro-economic
models currently available to us today - seem able to deal with the concept or impact of
Random Events (uncertainty). We therefore need to consider and factor in further novel and
disruptive (systemic) approaches which offer us the possibility to manage uncertainty. We
can do this by searching for, detecting and identifying Weak Signals – small, unexpected
variations or disturbances in System outputs indicating hidden data within the general
background System “noise” - which in turn may predicate the possible future existence or
presence of emerging chaotic, and radically disruptive Wild Card or Black Swan events
beginning to form on the detectable Horizon – or even just beyond. Random Events can
then be factored into Complex Systems Modelling. Complex Systems interact with unseen
forces – which in turn act to inject disorder, randomness, uncertainty, chaos and disruption.
The Global Economy, and other Complex Adaptive Systems, may in future be considered
and modelled successfully as a very large set of multiple interacting Ordered (Constrained)
Complex Systems - each individual System loosely coupled with all of the others, and every
System with its own clear set of rules and an ordered (restricted) number of elements and
classes, relationships and types.

Randomness
Stochastic Processes – Random Events
• A tradition that begins with the classical Greek natural philosophers (circa 600 -
200 BC) and continues through contemporary science - holds that change and
the order of nature are the result of natural forces. What is the role of random,
stochastic processes in a universe that exhibits such order? When we examine
the heavens there seems to be a great deal of order to the appearance and
movement of the celestial bodies - galaxies, stars, planets, asteroids, etc.
• Since the dawn of our species, humans have speculated on how these bodies
were formed and on the meaning of their movements. Most observations of
natural phenomena support the contention that nature is ordered. The force
that brought about this order differs depending upon the source of the historic
explanation of how this order came to be. For most of human history, super-
natural forces were credited with the imposition of order on nature.

Randomness
Random Processes
• Random Processes may influence any natural and human phenomena, such as: -
– the history of an object
– the outcome of an event
– the execution of a process
• Randomness may be somewhat difficult to demonstrate, as true Randomness in chaotic
system behaviour is not always readily or easily distinguishable from any of the “noise”
that we may find in Complex Systems – such as foreground and background wave
harmonics, resonance and interference. Complex Systems may be influenced by both
internal and external factors which remain hidden – either unrecognised or unknown.
These hidden and unknown factors may exist far beyond our ability to detect them – but
nevertheless, still exert influence. The existence of weak internal or external forces acting
on systems may not be visible to the observer – these subliminal temporal forces can
influence Complex System behaviour in such a way that the presence of imperceptibly tiny
inputs, acting on a system, amplified in effect over many system cycles - are ultimately
able to create massive observable changes to outcomes in complex system behaviour.

Randomness
introduces into our daily lives. Research into stochastic (random) processes looks
towards how we might anticipate, prepare for and manage the chaos and uncertainty
which acts on complex systems – including natural systems such as Cosmology and
Climate, as well as human systems such as Politics and the Economy – so that we may
anticipate future change and prepare for it…..
1. Classical Mechanics - Any apparent randomness is as a result of Unknown Forces
2. Thermodynamics - Randomness, chaos and uncertainty is directly a result of Entropy
3. Biology - Any apparent randomness is as a result of Unknown Forces
4. Chemistry - Any apparent randomness is as a result of Unknown Forces
5. Atomic Theory - All events are utterly and unerringly predictable (Dirac Equation)
6. Quantum Mechanics - Every event is both symmetrical and random (Hawking Paradox)
7. Geology - Any randomness or asymmetry is a result of Unknown Forces
8. Astronomy - Any randomness or asymmetry is a result of Unknown Forces
9. Cosmology - Any randomness or asymmetry is as a result of Dark Matter, Energy, Flow
10. Relativity Theory - Randomness or asymmetry may be a result of Quantum effects
11. Wave Mechanics - Any randomness and asymmetry is as a result of Unknown Forces

Randomness
Classical Mechanics
(Newtonian Physics)
Everyday objects Any apparent randomness is as
Sir Isaac Newton
Thermodynamics Entropy, Enthalpy Newcomen, Trevithick,
Watt, Stephenson
Biology Evolution Darwin, Banks, Huxley,
Krebs, Crick, Watson
Chemistry Molecules Lavoisier, Priestley
Atomic Theory Atoms Each and every Quantum event
is truly and intrinsically fully
symmetrical and random
Max Plank, Niels Bohr
Quantum Mechanics Sub-atomic particles Erwin Schrodinger ,
Werner Heisenberg,
Paul Dirac,
Richard Feynman

Randomness
Geology The Earth, Planets,
Planetoids, Asteroids,
Meteors / Meteorites
Any apparent randomness is as
Hutton, Lyell, Wagner
Astronomy Common, Observable
Celestial Objects
Any apparent randomness or
asymmetry may be as a result
of Quantum effects or other
Unknown Forces acting early in
the history of Space-Time
Galileo, Copernicus,
Kepler, Lovell, Hubble
Celestial Objects
Hoyle, Ryall, Rees,
Penrose, Bell-Burnell
Relativity Theory The Universe Any apparent randomness or
asymmetry is as a result of
Unknown Forces / Dimensions
Albert Einstein,
Hermann Minkowski,
Stephen Hawking
Wave Mechanics
(String Theory or
Quantum Dynamics)
The Universe,
Membranes and
Hyperspace
Michael Green,
Michio Kaku

Randomness
• Thermodynamics
– governs the flow of energy and the transformation (change in state) of systems
– randomness, chaos and uncertainty is the result of the effects of Enthalpy and Entropy
• Chemistry
– Chemistry (Transformation) governs the change in state of atoms and molecules
• Biology
– Biology (Ecology ) governs Evolution - the life and death of all living Organisms

Randomness
• Atomic Theory
– governs the behaviour of unimaginably small objects (atoms and sub-atomic particles)
– all events are truly and intrinsically, utterly and unerringly predictable (Dirac Equation).
– governs the behaviour of unimaginably tiny objects (fundamental sub-atomic particles)
• Geology
– Geology governs the behaviour of local Solar System Objects (such as The Earth, Planets,
Planetoids, Asteroids, Meteors / Meteorites) which populate the Solar System
unmeasurable Unknown Forces - either internal or external - acting upon a System
• Astronomy
– Astronomy governs the behaviour of Common, Observable Celestial Objects (such as
Asteroids, Planets, Stars and Stellar Clusters) which populate and structure Galaxies

Randomness
• Cosmology
– Cosmology governs the behaviour of impossibly super-massive cosmic building blocks
– any apparent randomness or asymmetry is due to the influence of Quantum Effects,
Unknown Forces (Dark Matter, Dark Flow and Dark Energy) or Unknown Dimensions

Randomness
• If the movement of an object resulted from the operation of stochastic
processes, a repeating pattern of motion would not occur - and we would not
be able to predict with any accuracy the next location of the object as it move
down its path. Examples of stochastic processes include: - the translational
motion of atomic or molecular substances, such as the hydrogen ions in the
core of the sun; the outcomes from flipping a coin; etc. Stochastic processes
govern the outcome of games of chance – unless those games are “fixed”.
• Disruptive Future paradigms in Future Studies, when considered along with
Wave (String) Theory in Physics – alert us to the possibility of chaotic and
radically disruptive Random Events that generate ripples which propagate
outwards from the causal event like a wave – to flow across Space-Time.
Different waves might travel through the Time-Space continuum at slightly
different speeds due to the “viscosity” (granularity) in the substance of the
Space-Time Continuum (dark energy and dark matter).

Randomness
• Some types of Wave may thus be able to travel faster than others – either
because those types of Wave can propagate through Time-Space more rapidly
than other Wave types – or because certain types of Wave form can take
advantage of a “short cut” across a “warp” in the Time-Space continuum.
• A “warp” brings two discrete points from different Hyperspace Planes close
enough together to allow a Hyperspace Jump. Over any given time interval -
multiple Hyperspace Planes stack up on top of each other to create a time-line
which extends along the temporal axis of the Minkowski Space-Time Continuum.
• As we have discussed previously - Space (position) and Time (history) flow
inextricably together in a single direction – towards the future. In order to
demonstrate the principle properties of the Minkowski Space-Time continuum,
any type of Spatial and Temporal coupling in a Model or System must be able to
show over time that the History of a particle or the Transformation of a
process are fully and totally dependent on both its Spatial (positional) and
Temporal (historic) components acting together in unison.

Randomness
• Neither data-driven nor model-driven representations of the future are capable
alone, and by themselves, of dealing with the effects of chaos (uncertainty). We
therefore need to consider and factor in further novel and disruptive system
modelling approaches in order to help us to understand how Natural Systems
(Cosmology, Climate) and Human Activity Systems (Economics, Sociology)
perform. Random, Chaotic and Disruptive Wild Card or Black Swan events
may thus be factored into our System Models in order to account for uncertainty.
• Horizon Scanning, Tracking and Monitoring techniques offer us the possibility to
manage uncertainty by searching for, detecting and identifying Weak Signals –
which are messages from Random Events coming towards us from the future.
Faint seismic disturbances warn us of coming of Earth-quakes and Tsunamis.
Weak Signals (seismic disturbances) may often be followed by Strong Signals
(changes in topology), Wild Card (volcanic eruptions) or Black Swan (pyroclastic
cloud and ocean wave events), Horizon Scanning may help us to use Systems
Modelling to predict Natural Events like Earth-quakes and Tsunamis – as well as
Biological processes such as the future of Ecosystems, and Human Processes
such as the cyclic rise and fall of Commodity, Stocks and Shares market prices.

Data-driven v. Model-driven Domains
Model-driven
Data-driven
Rationalism
Positivism Gnosticism, Sophism
Reaction
Scepticism
Dogma
Enlightenment
Pragmatism
Realism
Social Sciences
Sociology
Economics
Life Sciences
Biology Ecology Anthropology and Pre-history
Earth Sciences
Economic Analysis
Applied mathematics
Geography
Geology
Chemistry
Engineering
Geo-physics Environmental Sciences
Archaeology
Palaeontology
Future Management
Astronomy
Cosmology
Relativity
Astrophysics
Astrology
Climate Change
Statistics
Strategic Foresight
Cluster Theory
Pure mathematics
Particle Physics
String Theory
Quantum Mechanics
Futures Studies
Weather ForecastingPredictive Analytics

The Nature of Randomness – Uncertainty
• Randomness makes any precise prediction of future outcomes impossible.
We are unable to predict any future outcome with any significant degree of
confidence or accuracy – due to the inherent presence of uncertainty
associated with Complex Systems. Randomness in Complex Systems
introduces chaos and disorder – causing disruption. Events no longer continue
to unfold along a smooth, predictable linear course leading towards an
inevitable outcome – instead, we experience surprises.
• What we can do, however, is to identify the degree of uncertainty present in
those Systems, based on known, objective measures of System Order and
Complexity - the number and nature of elements present in the system, and the
number and nature of relationships which exist between those System
elements. This in turn enables us to describe the risk associated with possible,
probable and alternative Scenarios, and thus equips us to be able to forecast
risk and the probability of each of those future Scenarios materialising.

The Nature of Randomness – Uncertainty
• If true randomness exists and future outcomes cannot be predicted –
then what is the origin of that randomness? For example, are unexpected
outcomes simply apparent as a result of sub-atomic nano-randomness
existing at the quantum level – such as uncertainty phenomena etc…..?
• The Stephen Hawking Paradox postulates that uncertainty dominates complex
and chaotic systems to such an extent that future outcomes are both unknown -
and unknowable. The working context of this paradox is however, largely
restricted to the realm of Quantum Mechanics – where each and every natural
event that occurs at the sub-atomic level is truly completely and intrinsically –
both symmetrical and random.
Simplexity
Ordered
Complexity
Disordered
Complexity
Complex Adaptive
Systems (CAS)
Linear
Systems
ChaosOrder

• What is the explanation for randomness evident in all high-order
phenomena found in nature…?
• In order to obtain realistic glimpses into the Future, - then the major paradigm
differences between the Actual Reality that we experience every day and our
limited Systemic Models which attempt to simplify, abstract and simulate
reality - must be clearly distinguished between and understood.
• When we design our Systemic Models representing Actual Reality – such as
the Economy, Geo-political systems, Climate Change, Weather and so on - if
we are lucky enough, then some high-order phenomena found in nature may
be captured by a random rule; and with even more luck, by a deterministic rule
(which can be regarded as a special case of randomness) - but if we are
unlucky - then none of those rules might be captured at all. Regarding the
nature of reality - it still remains unclear what factors distinguish those truly
random phenomenon found in nature at the Quantum level (e.g. radioactive
decay?) from other Random Events - which are triggered by unseen forces.

• Can we accept that these natural phenomena are not truly random at all
– that is, given sufficient information such as complete event data sets -
it is possible to predict random events? If so, are all random events the
result of the same natural phenomenon - unseen or hidden forces ? “
• Classical Mechanics (Newtonian Physics) describe the laws which govern all
of the systems and objects that we are familiar with in our everyday routine
lives. Relativity Theory, on the other hand, describes unimaginably large
things, whilst Quantum Mechanics describes impossibly small things – and
Wave Mechanics (String Theory) attempts to describe everything. True
randomness does not really exist in Classical (Newtonian) Physics – the laws
which control Chaos and Complex Systems that govern every aspect of our
life on Earth today – from Natural Systems such as Cosmology, Astronomy,
Climatology, Geology and Biology through to Human Activity Systems such
as Political, Economic and Sociological Complex Adaptive Systems (CAS).

• Randomness is simply the results of those forces which are not known, not
recognised, not understood, are not under the control of the observer or
simply occur outside of the known boundaries of observable system
components – but, nevertheless, must still exist and exert influence over the
system. Over many System Cycles, immeasurably small inputs interacting
with Complex System components and relationships - may be amplified into
extremely significant outputs.....
1. Classical (Newtonian) Physics – which governs all of the everyday events
around us – where apparent randomness is as a result of Unknown Forces
2. Relativity Theory – which governs the events of impossibly large objects –
any apparent randomness or asymmetry is as a result of Quantum effects
3. Quantum Mechanics – which governs the events of unimaginably small
objects – all events are truly and intrinsically both symmetrical and random
4. Wave (String) Theory – which attempts to integrate the behaviour of
everyday objects, impossibly large objects and unimaginably small objects –
apparent randomness and asymmetry is as a result of Unknown Forces –
which may in turn have their origination in Quantum Dynamics effects

Randomness
Black Swan – Nassim Taleb
• Black Swan by Nassim Taleb was first published in 2007 and quickly sold out, with close
to 3 million copies purchased as of February 2011. Fooled by Randomness and Black
Swan seized the public imagination, and quickly generated mass-market interest to create
a new, niche market segment for Future Management publications - which cross-over
General Interest, Professional and Academic sectors. Taleb's non-technical writing style
mixes a narrative text (often semi-autobiographical) and whimsical home-spun tales
backed up by some historical and scientific content. The success of Taleb's first two books
(Fooled by Randomness and the Black Swan) gained him an advance on Royalties of
$4 million for his follow-up book – the Blank Swan.
The Drunkard's Walk:- How Randomness Rules Our Lives - Leonard Mlodinow
• The Drunkard's Walk dives deeper into Randomness. This book is different - it is natural
for scientific books to discuss science – but unusual for them to contain highly readable
prose and good humour, not to mention useful and practical insights which help to live your
life with a greater understanding of the world about you. The book's major weakness is
that it comes up short on fundamental explanations of Chaos, Disruption, Complexity and
Randomness. Mlodinow simply advises readers to "be aware" and "conscious" of how
important randomness is.

Randomness –The Drunkards Walk
• Randomness The
Drunkards Walk – is
the motion of a moving
body subject to random
changes in direction
• This pattern is
sometimes referred to
as “the drunkard's walk”.
The intersecting lines at
the top and the right of
the picture are
Cartesian coordinates
and mark the origin
where X=0 and Y=0.
• The actual random walk
is long and torturous,
but the actual vector
distance travelled from
the point of origin cross
hairs (0,0) is very short.

• Disruptive Future paradigms in Future Studies along with Wave Theory (String
Theory) in Physics - alert us to the phenomenon of chaotic and radically disruptive
Random Events which can generate Temporal Disturbances in the Space–Time
Continuum – waves which propagate out like a ripple and travel outwards from
that Random Event - through the Space-Time Continuum.
• Weak Signals, Strong Signals, Wild Cards and Black Swan Events – are a
sequence of linked and integrated waves in ascending order of magnitude, which
have a common source or origin - either a single Random Event instance or
arising from a linked series of chaotic and disruptive Random Events - an Event
Storm. These Random Events propagate through the space-time continuum as a
related and integrated series of waves with an ascending order of magnitude and
impact – the first wave to arrive is the fastest travelling,- Weak Signals - something
like a faint echo of the causal Random Event, This may in turn be followed in turn
by a ripple (Strong Signals) then possibly by a wave (Wild Card) - which could
indicate the unfolding a further increase in magnitude and intensity which suddenly
and catastrophically arrives - something like a tsunami (Black Swan Event).

• Random Events may essentially “bend” the Time-Space continuum. Some Waves
may thus be able to travel faster than others – either because certain Wave form
types can propagate through Time-Space more rapidly than other Wave types - or
because certain types of Wave take a “short cut” as a “bend” in the Time-Space
continuum brings two discrete points closer together from different Hyperspace
Planes. Over a time interval multiple Hyperspace Planes stacked up on top of each
other create a time-line extending along the Time axis of the Minkowski Space-Time
Continuum.
Sequence of Events - Emerging Waves Stage View of Wave Series Development
1. Unseen Forces 1. Discovery
2. Random Event 1.1 Establishment
3. Weak Signals 1.2 Development
4. Strong Signals 2. Growth
5. Wild Cards 3. Plateau
6. Black Swan Event 4. Decline
5. Collapse
5.1 Renewal
5.2 Replacement

• Randomness. Weak Signals, Wild Cards and Black Swan Events – may be
evidence of a chain of radically disruptive and chaotic Random Events which are
due to the action of unseen forces – that propagate through the Space-Time
Continuum in the same way as a ripple becomes a wave and crosses the ocean.
• Perhaps some of the different Wave Types - Weak Signals, Wild Cards and
Black Swan Events can travel faster or take a different route compared with
some of the other types – perhaps because their Wave forms can propagate
through the Space- Time Matrix (which is made up of dark matter, dark energy
and dark flow) more rapidly than the other Wave forms - or perhaps they are
different types of Wave – and specific Wave Types may able to take a “short-cut”
between two points on different Hyperspace Planes – and so arrive sooner.
• It is possible that certain types of Random Event may be able to “bend” the Time-
Space continuum – to bring two discrete points on different Hyperspace Planes
closer together and so take a short-cut over a time interval extended through a
time-line flowing along the Time axis of the Minkowski Space-Time Continuum.

• Every item of Global Content that we find in the Present is somehow
connected with both the Past and the Future. Space-Time is a Dimension –
which flows in a single direction, as does a River – towards the Future.
• Space-Time, like water diverted along an alternative river channel, does not
always flow uniformly – outside of the main channel there could well be
“submerged objects” (random events) that disturb the passage of time, and
may possess the potential capability of creating unforeseen eddies, whirlpools
and currents in the flow of Time (disorder and uncertainty) – which in turn
posses the capacity to generate ripples, and waves (chaos and disruption) –
thus changing the course of the Time-Space continuum. “Weak Signals” are
“Ghosts in the Machine” of these subliminal temporal interactions – with the
capability to contain information about future “Wild card” or “Black Swan”
random events.

Complex Systems and
Chaos Theory
Complex Systems and Chaos Theory has been used
extensively in the field of Futures Studies, Strategic
Management, Natural Sciences and Behavioural
Science. It is applied in these domains to understand
how individuals within populations, societies,
economies and states act as a collection of loosely
coupled interacting systems which adapt to changing
environmental factors and random events – bio-
ecological, socio-economic or geo-political.....

Linear and Non-linear Systems
Linear Systems – all system outputs are directly and proportionally related to system inputs
• Types of linear algebraic function behaviours; examples of Simple Systems include: -
– Game Theory and Lanchester Theory
– Civilisations and SIM City Games
– Drake Equation (SETI) for Galactic Civilisations
Non-linear Systems – system outputs are asymmetric and not proportional or related to inputs
• Types of non-linear algebraic function behaviours: examples of Complex / Chaotic Systems are: -
– Complex Systems – large numbers of elements with both symmetric and asymmetric relationships
– Complex Adaptive Systems (CAS) – co-dependency and co-evolution with external systems
– Multi-stability – alternates between multiple exclusive states.(lift status = going up, down, static)
– Chaotic Systems
• Classical chaos – the behaviour of a chaotic system cannot be predicted.
• A-periodic oscillations – functions that do not repeat values after a certain period (# of cycles)
– Solitons – self-reinforcing solitary waves - due to feedback by forces within the same system
– Amplitude death – any oscillations present in the system cease after a certain period (# of cycles)
due to feedback by forces in the same system - or some kind of interaction with external systems.
– Navis-Stokes Equation for the motion of a fluid: -
• Weather Forecasting
• Plate Tectonics and Continental Drift

• System Complexity is typically characterised and measured by the number of elements in a
system, the number of interactions between elements and the nature (type) of interactions.
• One of the problems in addressing complexity issues has always been distinguishing between
the large number of elements (components) and relationships (interactions) evident in chaotic
(unconstrained) systems - Chaos Theory - and the still large, but significantly smaller number
of both and elements and interactions found in ordered (constrained) Complex Systems.
• Orderly System Frameworks tend to dramatically reduce the total number of elements and
interactions – with fewer and smaller classes of more uniform elements – and with reduced,
sparser regimes of more restricted relationships featuring more highly-ordered, better internally
correlated and constrained interactions – as compared with Disorderly System Frameworks.
Simplexity
Ordered
Complexity
Disordered
Complexity
Complex Adaptive
Systems (CAS)
Linear
Systems
ChaosOrder

System Complexity
• System Complexity is typically characterised by the number of elements in a system,
the number of interactions between those elements and the nature (type) of interactions.
One of the problems in addressing complexity issues has always been distinguishing
between the large number of elements and relationships, or interactions evident in
chaotic (disruptive, unconstrained) systems - and the still large, but significantly smaller
number of elements and interactions found in ordered (constrained) systems.
• Orderly (constrained) System Frameworks tend to have both a restricted number of
uniform elements with simple (linear, proportional, symmetric) interactions with just a few
element and interaction classes of small size, featuring explicit interaction rules which
govern more highly-ordered, internally correlated and constrained interactions – and
therefore tend to exhibit predictable system behaviour with smooth, linear outcomes.
• Disorderly (unconstrained) System Frameworks – tend to have both a very large total
number of non-uniform elements featuring complex (non-linear, asymmetric) interactions
which may be organised into many classes and regimes. Disorderly (unconstrained)
System Frameworks – feature a greater number of more disordered, uncorrelated and
unconstrained element interactions with implicit or random rules – which tend to exhibit
unpredictable, random, chaotic and disruptive system behaviour – and creates surprises.

Complex Systems and Chaos Theory
• A system may be defined as simple or linear whenever its evolution sensitively is fully
independent of its initial conditions – and may also be described as deterministic
whenever the behaviour of a simple (linear) systems can be accurately predicted and
when all of the observable system outputs are directly and proportionally related to
system inputs. We can expect smooth, linear, highly predictable outcomes to simple
systems which are driven by linear algebraic functions.
• A system may be described as chaotic whenever the system evolution sensitively is
fully dependant upon its initial conditions – and may also be defined as probabilistic –
whenever the behaviour of that stochastic system cannot be predicted. This property
of dependency on initial conditions in chaotic systems implies that from any two invisibly
different starting points or variations in starting conditions – then their trajectories begin
to diverge – and the degree of separation between the two trajectories increases
exponentially over the course of time. In this way, over numerous System Cycles –
invisibly small differences in initial conditions are amplified until they become radically
divergent, eventually producing totally unexpected results with unpredictable outcomes.
Instead of smooth, linear outcomes – we experience surprises. This is why complex,
chaotic systems such as weather and the economy – are impossible to accurately
predict. What we can do, however, is to describe possible, probable and alternative
future scenarios – and calculate the probability of each of those scenarios materialising.

• Chaos Theory has been used extensively in the fields of Futures Studies, Natural
Sciences, Behavioural Science, Strategic Management, Threat Analysis and Risk
Management. The requirements for a stochastic system to become chaotic, are that the
system must be non-linear and multi-dimensional – that is, the system posses at least
three dimensions. The Space-Time Continuum is already multi-dimensional – so any
complex (non-linear) and time-variant system which exists over time in three-dimensional
space - meets all of these criteria.
• The Control of Chaos refers to a process where a tiny external system influence is
applied to a chaotic system, so as to slightly vary system conditions – in order to achieve
a desirable and predictable (periodic or stationary) outcome. To synchronise and resolve
chaotic system behaviour we may invoke external procedures for stabilizing chaos which
interact with symbolic sequences of an embedded chaotic attractor - thus influencing
chaotic trajectories. The major concepts involved in the Control of Chaos, are described
by two methods – the Ott-Grebogi-Yorke (OGY) Method and the Adaptive Method.
• The Adaptive Method for the resolution of Complex, Chaotic Systems introduces multiple
relatively simple and loosely coupled interacting systems in an attempt to model over time
the behaviour of a single, large Complex and Chaotic System - which may still be subject
to undetermined external influences – thus creating random system effects.....

Wave-form Analytics in Cycles
• Wave-form Analytics is a powerful new analytical tool “borrowed” from spectral
wave frequency analysis in Physics – which is based on Time-frequency analysis –
a technique which exploits the wave frequency and time symmetry principle. This is
introduced here for the first time in the study of natural and human activity waves,
and in the field of economic cycles, business cycles, market patterns and trends.
• Trend-cycle decomposition is a critical technique for testing the validity of multiple
(compound) dynamic wave-form models competing in a complex array of
interacting and inter-dependant cyclic systems in the study of complex cyclic
phenomena - driven by both deterministic and stochastic (probabilistic) paradigms.
In order to study complex periodic economic phenomena there are a number of
competing analytic paradigms – which are driven by either deterministic methods
(goal-seeking - testing the validity of a range of explicit / pre-determined / pre-
selected cycle periodicity value) and stochastic (random / probabilistic / implicit -
testing every possible wave periodicity value - or by identifying actual wave
periodicity values from the “noise” – harmonic resonance and interference patterns).

• A fundamental challenge found everywhere in business cycle theory is how to
interpret very large scale / long period compound-wave (polyphonic) time series data
sets which are dynamic (non-stationary) in nature. Wave-form Analytics is a new
analytical too based on Time-frequency analysis – a technique which exploits the
wave frequency and time symmetry principle. The role of time scale and preferred
reference from economic observation are fundamental constraints for Friedman's
rational arbitrageurs - and will be re-examined from the viewpoint of information
ambiguity and dynamic instability.
• The Wigner-Gabor-Qian (WGQ) spectrogram demonstrates a distinct capability for
revealing multiple and complex superimposed cycles or waves within dynamic, noisy
and chaotic time-series data sets. A variety of competing deterministic and
stochastic methods, including the first difference (FD) and Hodrick-Prescott (HP)
filter - may be deployed with the multiple-frequency mixed case of overlaid cycles
and system noise. The FD filter does not produce a clear picture of business cycles
– however, the HP filter provides us with strong results for pattern recognition of
multiple co-impacting business cycles. The existence of stable characteristic
frequencies in large economic data aggregations (“Big Data”) provides us with strong
evidence and valuable information about the structure of Business Cycles.

Wave-form Analytics in Natural Cycles
• Solar, Oceanic and Atmospheric Climate Forcing systems demonstrate Complex Adaptive
System (CAS) behaviour – behaviour which is more similar to an organism than that of
random and chaotic “Stochastic” systems. The remarkable long-term stability and
sustainability of cyclic climatic systems contrasted with random and chaotic short-term
weather systems are demonstrated by the metronomic regularity of climate pattern
changes driven by Milankovich Solar Cycles along with 1470-year Dansgaard-Oeschger
and Bond Cycles – regular and predictable and Oceanic Forcing Climate Sub-systems.
Wave-form Analytics in Human Activity Cycles
• Economic systems also demonstrate Complex Adaptive System (CAS) behaviour - more
similar to an ecology than chaotic “Random” systems. The capacity of market economies
for cyclic “boom and bust” – financial crashes and recovery - can be seen from the impact
of Black Swan Events causing stock market crashes - such as the failure of sovereign
states (Portugal, Ireland, Greece, Iceland, Italy and Spain) and market participants
(Lehman Brothers) due to oil price shocks, money supply shocks and credit crises.
Surprising pattern changes occurred during wars, arm races, and during the Reagan
administration. Like microscopy for biology, non-stationary time series analysis opens up
a new space for business cycle studies and policy diagnostics.

Complex Adaptive Systems
Adaption and Evolution
When Systems demonstrate properties of Complex
Adaptive Systems (CAS) - often defined as a
collection or set of relatively simple and loosely
connected interacting systems exhibiting co-adapting
and co-evolving behaviour - then those systems are
much more likely to adapt successfully to their
environment and, thus better survive the impact of both
gradual change and of sudden random events.

• Complex Adaptive Systems (CAS) and Chaos Theory has also been
used extensively in the field of Futures Studies, Strategic Management,
Natural Sciences and Behavioural Science. It is applied in these domains
to understand how individuals within populations, societies, economies and
states act as a collection of loosely coupled interacting systems which
adapt to changing environmental factors and random events – biological,
ecological, socio-economic or geo-political.
• Complex Adaptive Systems (CAS) and Chaos Theory treats individuals,
crowds and populations as a collective of pervasive social structures which
may be influenced by random individual behaviours – such as flocks of
birds moving together in flight to avoid collision, shoals of fish forming a
“bait ball” in response to predation, or groups of individuals coordinating
their behaviour in order to respond to external stimuli – the threat of
predation or aggression – or in order to exploit novel and unexpected
opportunities which have been discovered or presented to them.

• When Systems demonstrate properties of Complex Adaptive Systems (CAS) - which is
often defined as a collection or set of relatively simple and loosely connected interacting
systems exhibiting co-adapting and co-evolving behaviour (sub-systems or components
changing together in response to the same external stimuli) - then those systems are
much more likely to adapt successfully to their environment and, thus better survive the
impact of both gradual change and of sudden random events. Complexity Theory
thinking has been present in biological, strategic and organisational system studies since
the first inception of Complex Adaptive Systems (CAS) as an academic discipline.
• Complex Adaptive Systems are further contrasted compared with other ordered and
chaotic systems by the relationship that exists between the system and the agents and
catalysts of change which act upon it. In an ordered system the level of constraint means
that all agent behaviour is limited to the rules of the system. In a chaotic system these
agents are unconstrained and are capable of random events, uncertainty and disruption.
In a CAS, both the system and the agents co-evolve together; the system acting to
lightly constrain the agents behaviour - the agents of change, however, modify the
system by their interaction. CAS approaches to behavioural science seek to understand
both the nature of system constraints and change agent interactions and generally takes
an evolutionary or naturalistic approach to crowd scenario planning and impact analysis.

• Biological, Sociological, Economic and Political systems all tend to demonstrate
Complex Adaptive System (CAS) behaviour - which appears to be more similar
in nature to biological behaviour in an population than to truly Disorderly, Chaotic,
Stochastic Systems (“Random” Systems). For example, the remarkable long-term
adaptability, stability and resilience of market economies may be demonstrated by
the impact of Black Swan Events causing stock market crashes - such as oil price
shocks (1970-72) and credit supply shocks (1927- 1929 and 2008 onwards) – by
the ability of Financial markets to rapidly absorb and recover from these events.
• Unexpected and surprising Cycle Pattern changes have historically occurred during
regional and global conflicts being fuelled by technology innovation-driven arms
races - and also during US Republican administrations (Reagan and Bush - why?).
Just as advances in electron microscopy have revolutionised the science of biology
- non-stationary time series wave-form analysis has opened up a new space for
Biological, Sociological, Economic and Political system studies and diagnostics.

Crowd Behaviour – the Swarm
• In a crowd of human beings or a swarm of animals, individuals are so closely
connected that they share the same mood and emotions (fear, greed, rage)
and demonstrate the same or very similar behaviour (fight, flee or feeding
frenzy). Only the initial few individuals exposed to the Causal Event or
incident may at first respond strongly and directly to the initial “trigger”
stimulus, causal event or incident (opportunity or threat – such as external
predation, aggression or discovery of a novel or unexpected opportunity to
satisfy a basic need – such as feeding, reproduction or territorialism).
• Those individuals who have been directly exposed to the initial “trigger” event
or incident - the system input or causal event that initiated a specific outbreak
of behaviour in a crowd or swarm – quickly communicate and propagate their
swarm response mechanism and share with all the other individuals – those
members of the Crowd immediately next to them – so that modified Crowd
behaviour quickly spreads from the periphery or edge of the Crowd.
• Peripheral Crowd members in turn adopt the Crowd response behaviour
without having been directly exposed to the “trigger”. Most members of the
crowd or swarm may be totally oblivious as to the initial source or nature of
the trigger stimulus - nonetheless, the common Crowd behaviour response
quickly spreads to all of the individuals in or around that crowd or swarm.

• An example of Random Clustering is a Crowd or Swarm. There are a various forces
which contribute towards Crowd Behaviour – or Swarming. In any crowd of human
beings or a swarm of animals, individuals in the crowd or swarm are closely connected
so that they share the same mood and emotions (fear, greed, rage) and demonstrate
the same or very similar behaviour (fight, flee or feeding frenzy). Only the initial few
individuals exposed to the Random Event or incident may at first respond strongly and
directly to the initial “trigger” stimulus, causal event or incident (opportunity or threat –
such as external predation, aggression or discovery of a novel or unexpected
opportunity to satisfy a basic need – such as feeding, reproduction or territorialism).
• Those individuals who have been directly exposed to the initial “trigger” event or incident -
the system input or causal event that initiated a specific outbreak of behaviour in a crowd or
swarm – quickly communicate and propagate their swarm response mechanism and share
with all the other individuals – those members of the Crowd immediately next to them – so
that modified Crowd behaviour quickly spreads from the periphery or edge of the Crowd.
• Peripheral Crowd members in turn adopt Crowd response behaviour without having been
directly exposed to the “trigger”. Members of the crowd or swarm may be oblivious to the
initial source or nature of the trigger stimulus - nonetheless, the common Crowd behaviour
response quickly spreads to all of the individuals in or around that core crowd or swarm.

• One of the dangers posed by human crowd behaviour is that of “de-individualisation” in a
crowd, where a group of random individuals aggregate together and begin acting in concert
- adopting common behaviour, aims and objectives – and may begin to exhibit uninhibited
crowd responses to external information and stimuli. Crowd participants in this state begin
to respond without the usual constraints of their normal social, ethical, moral, religious and
behavioural rules. These are the set of circumstances which led to events such as the Arab
Spring and London Riots - which spread rapidly through deprived communities across the
country, both urban and rural. This type of collective group behaviour – such as a “feeding
frenzy” – has been observed in primates and carnivores - and even in rodents and fish.....
• Crowd behaviour is not just the domain of Demonstrators and Protesters - it can also be
seen in failing economies with the actions of Economic Planners in Central Banks - along
with their Political Masters – who also behave as a group of individuals acting together in
concert without the usual constraints – and thus, under extreme psychological stress as
systems such as the economy begins to collapse unpredictably – start to demonstrate "de-
individualisation" - collective uninhibited responses to external information and stimuli,
without the constraints of their normal political, economic, social, ethical, moral and
behavioural rules. These circumstances may lead to further panic and crowd behaviour
across Towns and Cities, Banks and Financial Institutions, ultimately Municipal, State and
Federal government departments - causing the failure of Global Markets or the fall of
Governments – as was recently witnessed in both the Arab Spring and the Euro Crisis.

Moore's Law
• In 1965, the observation made by Gordon Moore, co-founder of Intel, is that the number
of transistors per square inch on integrated circuits had doubled every year since the integrated
circuit was invented. Moore predicted that this trend would continue for the foreseeable future. In
subsequent years, the pace of change has slowed down somewhat - but Data Storage Density
(gigabytes) has doubled approximately every 18 months - a definition which Moore himself has
blessed. The current definition of Moore's Law, accepted by most experts, including Disruptive
Futurists and Moore himself, is that Computing Power (gigaflops) will double about every two
years. Expect Moore's Law to hold good for at least another generation.....
• A forecast - and a challenge. Gordon Moore’s forecast for the pace of change in silicon
technology innovation - known as Moore's Law - essentially describes the basic business model
for the semiconductor industry. Intel, through investments in technology and manufacturing has
made Moore’s Law a reality. As transistor scale gets ever smaller Intel expects to continue to
deliver on Moore’s prediction well into the foreseeable future by using an entirely new transistor
formula that alleviates wasteful electricity leaks creating more energy-efficient processors.
• Exponential growth that continues today. Continuing Moore's Law means the rate of
progress in the semiconductor industry will far surpass that of nearly all other industries. The
future of Moore’s Law could deliver a magnitude of exponential capability increases, driving a
fundamental shift in computing, networking, storage, and communication devices to handle the
ever-growing digital content and Intel's vision of 15 billion intelligent, connected smart devices.

Forecasting and Predictive Analytics
• ECONOMIC MODELLING and LONG-RANGE FORECASTING •
• Economic Modelling and Long-range Forecasting is driven by atomic Data Warehouse
Structures and sophisticated Economic Models containing both Historic (up to 200 years daily
closing prices for Commodities, shares and bonds) and Future values (daily forecast and weekly
projected price curves, monthly and quarterly movement predictions, and so on for up to 50
years into the future – giving a total timeline of up to 250 years (Historic + 50 years Future trends
summary, outline movements and highlights). Forecast results are obtained using Economic
Models - Quantitative (technical) Analysis (Monte Carlo Simulation, Pattern and Trend Analysis -
Economic Growth and Recession / Depression shapes and Commodity Price Data Sets) in order
to construct a continuous 100 year “window” into Commodity Price Curves and Business Cycles
for Cluster Analysis and Causal Layer Analysis (CLA) – which in turn is used for driving out
Qualitative (narrative) Scenario Planning and Impact Analysis for describing future narrative epic
stories, scenarios and use-cases.
• PREDICTIVE ANALYITICS and EVENT FORECASTING •
• Predictive Analytics and Event Forecasting uses Horizon Scanning, Tracking and Monitoring
methods combined with Cycle, Pattern and Trend Analysis techniques for Event Forecasting and
Propensity Models in order to anticipate a wide range of business. economic, social and political
Future Events – ranging from micro-economic Market phenomena such as forecasting Market
Sentiment and Price Curve movements - to large-scale macro-economic Fiscal phenomena
using Weak Signal processing to predict future Wild Card and Black Swan Events - such as
Monetary System shocks.

• MARKET RISK •
Market Risk = Market Sentiment – Actual Results (Reality)
• The two Mood States – “Greed and Fear” are primitive human instincts which, until now, we've
struggled to accurately qualify and quantify. Social Networks, such as Twitter and Facebook,
burst on to the scene five years ago and have since grown into internet giants. Facebook has
over 900 million active members and Twitter over 250 million, with users posting over 2 billion
"tweets“ or messages every week. This provides hugely valuable and rich insights into how
Market Sentiment and Market Risk are impacting on Share Support / Resistance Price Levels –
and so is also a source of real-time data that can be “mined” by super-fast computers to forecast
changes to Commodity Price Curves
• STRATEGIC FORESIGHT •
• Strategic Foresight is the ability to create and maintain a high-quality, coherent and functional
forward view, and to utilise Future Insights in order to gain Competitive Advantage - for example
to identify and understand emerging opportunities and threats, to manage risk, to inform
planning and forecasting and to shape strategy development. Strategic Foresight is a fusion of
Foresight techniques with Strategy Analysis methods – and so is of great value in detecting
adverse conditions, threat assessment, guiding policy and strategic decision-modelling, in
identifying and exploring novel opportunities presented by emerging technologies, in evaluating
new markets, products and services and in driving transformation and change.

• INNOVATION •
• Technology Innovation is simply combining existing resources in new and different ways –
in order to create novel and innovative Products and Services. Understanding the impact
of Technology Convergence is the Key to driving Innovation. Many common and familiar
objects in use today exist only as a result of technology convergence? Your average,
everyday passenger vehicle or laptop computer is the culmination of a series of technology
consolidation and integration events from a large number of apparently unrelated
technological innovations and advancements. Light-weight batteries were developed to
provide independence from fixed power sockets and hard-disk drives were made compact
enough to be installed in portable devices. The smart phone and tablet resulted from a
further convergence of technologies such as cellular telecommunications, mobile internet,
and Smart Apps - mini-applications that do not need an on-board hard-disk drive.
• FUTURE MANAGEMENT •
• Providing future analysis and strategic advice to stakeholders so that they might
understanding how the Future may unfold - in order to anticipate, prepare for and manage
the Future, to resolve challenging business problems, to envision, architect, design and
deliver novel solutions in support of major technology refreshment and business
transformation programmes • Future Analysis • Innovation • Strategic Planning •
Business Transformation • Technology Refreshment •

. • GEO-DEMOGRAPHICS •
• The profiling and analysis of large aggregated datasets in order to determine a ‘natural’ or
implicit structure of data relationships or groupings where no prior assumptions are made
concerning the number or type of groups discovered or group relationships, hierarchies or
internal data structures - in order to discover hidden data relationships - is an important starting
point forming the basis of many statistical and analytic applications. The subsequent explicit
Cluster Analysis as of discovered data relationships is a critical technique which attempts to
explain the nature, cause and effect of those implicit profile similarities or geographic
distributions. Geo-demographic techniques are frequently used in order to profile and segment
populations by ‘natural’ groupings - such as common behavioural traits, Clinical Trial, Morbidity
or Actuarial outcomes, along with many other shared characteristics and common factors –and
then attempt to understand and explain those natural group affinities and geographical
distributions using methods such as Causal Layer Analysis (CLA).....
• Social Media is the fastest growing category of user-provided global content and will eventually
grow to 20% of all internet content. Gartner defines social media content as unstructured data
created, edited and published by users on external platforms including Facebook, MySpace,
LinkedIn, Twitter, Xing, YouTube and a myriad of other social networking platforms - in addition
to internal Corporate Wikis, special interest group blogs, communications and collaboration
platforms. Social Mapping is the method used to describe how social linkage between
individuals defines Social Networks and to understand the nature and dynamics of intimate
relationships between individuals

• GIS MAPPING and SPATIAL DATA ANALYSIS •
• A Geographic Information System (GIS) integrates hardware, software, and
data capture devices for acquiring, managing, analysing, distributing and
displaying all forms of geographically dependant location data – including
machine generated data such as Computer-aided Design (CAD) data from
land and building surveys, Global Positioning System (GPS) terrestrial
location data - as well as all kinds of aerial and satellite image data.
• Spatial Data Analysis is a set of techniques for analysing spatial
(Geographic) location data. The results of spatial analysis are dependent
on the locations of the objects being analysed. Software that implements
spatial analysis techniques requires access to both the locations of objects
and their physical attributes. Spatial statistics extends traditional statistics
to support the analysis of geographic data. Spatial Data Analysis provides
techniques to describe the distribution of data in the geographic space
(descriptive spatial statistics), analyse the spatial patterns of the data
(spatial pattern or cluster analysis), identify and measure spatial
relationships (spatial regression), and create a surface from sampled data
(spatial interpolation, usually categorized as geo-statistics).

• “BIG DATA” •
• “Big Data” refers to vast aggregations (super sets) of individual datasets whose size and
scope is beyond the capability of conventional transactional Database Management
Systems and Enterprise Software Tools to capture, store, analyse and manage. Examples
of Big Data include the vast and ever changing amounts of data generated in social
networks where we have (unstructured) conversations with each other, news data streams,
geo-demographic data, internet search and browser logs, as well as the ever-growing
amount of machine data generated by pervasive smart devices - monitors, sensors and
detectors in the environment – captured via the Smart Grid, then processed in the Cloud –
and delivered to end-user Smart Phones and Tablets via Intelligent Agents and Alerts.
• Data Set Mashing and “Big Data” Global Content Analysis – supports Horizon Scanning,
Monitoring and Tracking activities by taking numerous, apparently un-related RSS and
other Information Streams and Data Feeds, loading them into Very large Scale (VLS) DWH
Structures and Document Management Systems for Real-time Analytics – searching for
and identifying possible signs of relationships hidden in data (Facts/Events)– in order to
discover and interpret previously unknown “Weak Signals” indicating emerging and
developing Application Scenarios, Patterns and Trends - in turn predicating possible,
probable and alternative global transformations unfolding as future “Wild Card” or “Black
Swan” events.

• WAVE-FORM ANAYITICS in “BIG DATA” •
• Wave-form Analytics help identify Cycles, Patterns and Trends in Big Data – characterised as
a sequence of high and low activity in time-series data – resulting in periodic increased and
reduced phases in regular, recurring cyclic trends. This approach supports an integrated study
of the impact of multiple concurrent cycles - and no longer requires iterative and repetitive
processes of trend estimation and elimination from the background “noise”.
• FORENSIC “BIG DATA” •
• Social Media Content and Spatial Mapping Data is used in order to understand intimate
personal relationships between individuals and to identify, locate and describe their participation
in various Global Social Networks. Thus the identification, composition, monitoring, tracking
,activity and traffic analysis of Social Networks Criminal Enterprises and Terrorist Cells – as
defined by common locations, business connections, social links and inter-personal
relationships – is used by Businesses to drive Influencer Programmes and by Government for
National Security, Counter-Terrorism, Anti-Trafficking, Criminal Investigation and Fraud
Prevention purposes.....
• Forensic “Big Data” combines the use of Social Media and Social Mapping Data in order to
understand intimate inter-personal relationships for the purpose of National Security, anti-
Trafficking and Fraud Prevention – through the identification, composition, activity analysis and
monitoring of Criminal Enterprises and Terrorist Cells.....

Random Event Clustering

– any apparent randomness is as a result of Unknown Forces
– all events are truly and intrinsically both symmetrical and random
– any apparent randomness or asymmetry is as a result of Quantum Dynamics
Wave Mechanics (String Theory) – integrates the behaviour of every size & type of object
– apparent randomness and asymmetry is as a result of Quantum and Unknown Forces

Clustering of co-impacting Events
• It is the function of every Futures Researcher or Disruptive Futurist to seek out and
discover a combination, sequence or chain of apparently Random Events which
occur together in Time and Space as an integrated sequence (groups or clusters) of
linked events. Random Event Clusters interacting together demonstrate transient or
instantiated relationships or dependencies (are co-related or dependant) – that is,
they are influencing each other in some way or another. These hidden relationships
show up in the values of data items (variables) in the data stack when we apply
systems modelling techniques (to discover explicit data relationships) or “Big Data”
methods (to discover implicit data relationships) in order to resolve Future Domain
opportunities or threats, risks, challenges, issues or problems .
• What factors or forces do we need to consider as being in-scope and critical to the
overall behaviour of the system? Which other factors or forces have we ignored,
overlooked or simply failed to consider? What further unknown factors or unseen
forces are out there which we have not detected – but still exist – which are
somehow impacting upon the behaviour and outcomes of the observable system -
thus exerting minute but critical influence on system elements (objects and their
interactions ) through Space and Time?

• The discovery of Chaos and Complexity has increased our understanding of the
Cosmos and its effect on us. If you surf the chaos content regions of the internet,
you will invariably encounter terms such as: -
• These influences can take some time to manifest themselves, but that is the nature
of the phenomena identified as a "strange attractor." Such differences could be
small to the point of invisibility - how tiny can influences be to have any effect?
This is captured in the “butterfly effect” scenario which is described later.
1. Attraction 14. Phase space and locking
2. Chaos
3. Clustering
4. Complexity
5. Butterfly effect
6. Disruption
7. Dependence
8. Feedback loops
9. Fractal patterns and dimensions
10. Harmonic Resonance
11. Horizon of predictability
12. Interference patterns
13. Massively diverse outcomes
15. Randomness
16. Repellence
17. Sensitivity to conditions
18. Self similarity (self affinity)
19. Starting conditions
20. Stochastic events
21. Strange attractors
22. System cycles (iterations)
23. Time-series Events
24. Turbulence
25. Uncertainty
26. Vanishingly small differences

• Nothing in the galaxy, in our world, or in our own personal existence, ever
happens in isolation of other places, objects, individuals and events. This is a
very simple and fundamental fact about life, nature the universe and everything.
In precisely the same moment as one event occurs or one transformation
happens in one location, infinite other events are taking place simultaneously in
countless other locations - which in turn impact on an innumerable collection or
set of other co-impacted objects, individuals and events.
• In order to study and prepare for Threat Analysis, Hazard and Risk Identification
and Future Management - we need to bear in mind the fact that all objects and
events are potentially connected in some way or other. None of these random
events occurs in isolation, none are entirely independent or unconnected. Every
object in the Universe exerts an influence over every other object, every process
impacts on every other process – however tenuous these relationships. This
phenomenon of Event Clustering is something that, through our own everyday
experience, we are all familiar with, aware of and know about – and with training
and preparation we are all easily able to follow, analyse, interpret and
understand.

Multiple Random processes also occur in clusters.....
• Random Processes (with the notable exception of Quantum Events) are never truly or
completely random or symmetrical – they are triggered by the manifestation of “unseen
forces” interacting with complex systems. It is the nature of Random Processes to
generate Chaotic Events – which may occur together in multiple, related and similar
sequences as a result of these hidden forces.
• At the local level, we see stochastic processes at work when we experience the myriad
of phenomena that make up our everyday life experiences – which also have a tendency
to occur in groups or clusters. Almost without exception, we hear of events by type
occurring close together in temporal and spatial proximity. The saying that bad or good
news comes in groups has some validity based upon the nature of event clustering.
Human disasters – train, boat or plane accidents, along with natural disasters – volcanic
eruptions, earthquakes and tsunamis – often arrive in groups or clusters aggregated
together in Time and Space – separated by long periods of no such events.

• There is an interesting Sensitive Dependence phenomenon called Phase
Locking where two loosely coupled systems with slightly different frequencies
show a tendency to move into resonance – in order to harmonise with one
another. We also know that the opposite of Attraction (or system convergence)
– Repellence (or system divergence) - is another type of System Dependency
possible with phase-locked systems/ Sensitive Dependence to external
forces demonstrates that minute, imperceptible changes to forces acting during
a system cycle are sufficient to dramatically alter the final state of the system,
which can display diverge trajectories with only very tiny inputs – especially if we
run those harmonised phase-locked systems in reverse.....(why ?)
• Phase locking draws two nearly harmonic systems into resonance and to the
observer, gives us the appearance of a “coincidence”. There are, however, no
such thing as coincidences in Newtonian Physics. Complexity Theory also
shows us that minute, imperceptible changes to input parameters at the initial
state of a system, at the beginning of a cycle - Sensitive Dependence to initial
conditions - are sufficient to dramatically alter the final state after even only a
few iterations of the system cycle. Such “coincidences” are, however, entirely
due to external forces acting upon the system - far beyond our ability to detect.

Attractors and Repellents
• Sensitive Dependence in Complexity Theory tells us that minute, imperceptible
changes to a system – at the beginning of a cycle, or dynamic forces acting as the
cycle evolves - are sufficient to dramatically alter the final state of the system -
even after a relatively few iterations of the system cycle.. Changes to a system at
the initial state constitutes Initial Sensitive Dependence, whilst dynamic external
forces acting on the system as the cycle evolves constitutes Dynamic Sensitive
Dependence. Thus Attractors and Repellents are examples of Dynamic
Sensitive Dependence.
• Any trajectory of the dynamic system in the attractor does not have to satisfy any
special constraints - except for remaining as an attractor. The trajectory may be
periodic or chaotic. In a set of periodic or chaotic points, if the average flow in the
neighbourhood is generally towards the set, then it is an attractor. If the average
neighbourhood flow is generally away from the set, then that set is instead
referred to as a repellent (repellor)..

• An attractor is a set within a dynamic system, towards which a moving variable
evolves over time. That is, points in that set get close enough to the attractor to
remain close - even when slightly disturbed by an external force The evolving
time-variant variable may be represented algebraically as an n-dimensional vector.
• The attractor is a region in n-dimensional space. In physical systems, the n
dimensions may be, for example, three positional coordinates and one temporal
co-ordinate for each of one or more physical entities; in economic systems, they
may be separate variables such as the inflation rate and the unemployment rate.
• If the evolving variable is two- or three-dimensional, the attractor of the dynamic
process can be represented geometrically in two or three dimensions, (as for
example in the three-dimensional case depicted to the right). An attractor can be
a point, a finite set of points, a curve, a manifold, or even a complicated set with
a fractal structure known as a strange attractor. If the variable is a scalar, the
attractor is a subset of the real number line. Describing attractors in dynamic
chaotic systems has been one of the greatest achievements of chaos theory.

Strange Attractor
• A Strange Attractor has a fractal dimensional structure. This is often the case when the system
dynamics are chaotic. Strange attractors that are non-chaotic may also exist. The term Strange
Attractor was coined by David Ruelle and Floris Takens to describe the attractor resulting from
a series of bifurcations in a system modelling the heat convection dynamics of a fluid heated
from below and cooled at the top. This process drives Plate Tectonics in the Earth’s mantle –
causing the spreading of Oceans from the mid-oceanic rift and resulting in Continental Drift.
• Strange attractors are often differentiable in a few directions, but some are like Cantor dust, and
are therefore not differentiable. Strange attractors may also be found in presence of noise -
where they may be shown to support invariant Random Probability measures of Sinai-Ruelle-
Bowen type - see Chekroun et al. (2011).
• A Strange Attractor is an attracting set that has zero measure in the embedding phase
space and has fractal dimensions. Trajectories within a strange attractor appear to skip around
randomly. On the surface these three equations seem relatively simple to solve. However, they
represent an extremely complicated and variable dynamic system. If the results are plotted in
three dimensions, then the following three-dimensional figure, called the Lorenz attractor, is
obtained: -

• There is a further interesting phenomenon called Phase Locking where two
loosely coupled systems with slightly different frequencies show a tendency to
move into resonance – they are seeking to harmonise with one another. We also
know that the opposite of system convergence - system divergence - is also
possible with phase-locked systems, Sensitive dependence also tells us that very
tiny inputs are enough to completely alter the final state after several iterations of
the dynamic. We thus know of systems that diverge with only very tiny inputs, but
the opposite is also true with convergence, especially if we run things in reverse.
• Thus phase locking draws two nearly harmonic systems into resonance and gives
us the appearance of a “coincidence”. There are, however, no coincidences in
nature or Physics - all random processes (with the notable exception of Quantum
Events) – are neither truly random nor completely symmetrical – but are simply the
outcome of unseen forces acting on a system. Such 'coincidences' are like the
clusters of personality types that are governed by certain recurring planets -
according to the statistical researches of M. Guaquelin.

• Phase Divergence drives two phase-locked harmonic systems out of
synchronisation into random, chaotic and discordant behaviour - where phase
locked systems can diverge from each other with only very tiny inputs (especially
when we run those phase-locked harmonic system models in reverse).....
• It is safe to say that pure coincidence is a vanishingly small reality. In fact, it is safe to
say that phenomena such as coincidence (which is more properly called serendipity)
The fact that very complex systems are invoked – as seen drawing two interacting
bodies into perfect resonance - is due to unknown factors or unseen forces behind
effects such as phase locking, and sensitive dependence. Sensitive dependence and
the interaction of every object upon all of the rest accounts for the phenomenon of
clustering – not serendipity, coincidence or mere chance.....
• The structure of the universe is based on such stochastic events. Here too, we find
random clustering events. The distribution of matter in the universe is based on the
quantum foundation. Clustering at the quantum level when the universe was just a
few thousands of a millimetre across – has lead to the creation of the super massive
black holes at the centre of each galaxy which, through gravitational attraction drive
the clustering of star / planetary systems, star clusters, galaxies and galactic clusters.

• There are many kinds of stochastic or random processes that impact on every area of Natural
Cycles and Human Activity. Randomness can be found in Science and Technology and in
Humanities and the Arts. Random events are taking place almost everywhere we look – for
example from Complex Systems and Chaos Theory to Cosmology and the distribution and
flow of energy and matter in the Universe, from Brownian motion and quantum theory to
Fractal Branching and linear transformations. Further examples include Random Events,
Weak Signals and Wild Cards occurring in each aspect of Nature and Human Activity – from
Ecology and the Environment to Weather Systems and Climatology in Economics and in the
Biological basis of Behaviour. And then there are the examples of atmospheric turbulence,
and the complex orbital and solar interaction cycles – and much, much more besides.....
• There is an interesting phenomenon called Phase Locking where two loosely coupled
systems with slightly different frequencies show a tendency to move into resonance – in order
to harmonise with one another. We also know that the opposite of system convergence -
system divergence - is also possible with phase-locked systems, which can also diverge with
only very tiny inputs - especially if we run those systems in reverse. Thus phase locking
draws two nearly harmonic systems into resonance and gives us the appearance of a
“coincidence”. There are, however, no coincidences in Physics. Sensitive Dependence in
Complexity Theory also tells us that minute, imperceptible changes to inputs at the initial state
of a system, at the beginning of a cycle, are sufficient to dramatically alter the final state after
even only a few iterations of the system cycle.

Random Event Clustering – Patterns in
the Chaos.....
Order out of Chaos – Patterns in the Randomness
• The long horizon of predictability for astronomical cycles and planetary alignment
allows us to determine when events associated with the movement of the planets
will exhibit a trendy to cluster. Planetary clustering in a non-cyclic periodic
fashion will generate non-cyclic periodic effects, each object (sun, moon,
planets) impacting upon all others.
• The Earth is not exempt from the forces of these objects. They manifest in many
ways, obvious and subtle. Some are easy to understand, others are not. We
can calculate the perturbation and tidal influences with some ease and match
these with the real-life nature of these influences and effects that we experience.
The Psychic influences of stochastic clustering are much harder to track. Such
“coincidences” are the clusters of personality types that are “governed” by certain
planetary influences - according to the statistical research of M. Guaquelin.
• We can calculate the perturbation and tidal influences with some ease and match
these with real effects we experience. The psychic influences are much harder to
track. They are there nonetheless as evidenced by the lunar and solar influences. The
stochastic and clustering nature of these influences is what is behind the seeming
stochastic and clustering nature of events we experience.

Random Event Clustering – Patterns in
the Chaos.....
• Even when we look to the formation of solar systems, we see stellar evolution
mediated by forces, random events and harmonics in synchronicity. As random
events tend to cluster as part of the natural evolution of the universe, it is not
surprising to find that, as a natural consequence of this clustering, all complex
systems will evolve in a similar way. Planets in orbit around a star must have
orbital periods in dissonance to each other in order to have reasonable stability.
• This dissonance will evolve to create patterns that occur randomly in space and
time where planets aggregate together along one line of sight or another. Such
is the nature of the great planetary conjunctions - stelliums. In our solar system,
this kind of planetary alignment occurs roughly once very forty years, but no two
stelliums are ever exactly alike in planetary grouping, distribution or location in
reference to the other objects in the solar system - or even in alignment with
background stellar objects. Since planets orbit in more or less well-defined and
periods, these events are highly predictable - unlike the events in the quantum
realm or with a chained sequence of coin tosses forming random event clusters.

• Every Risk Analyst, Contingency Planner or Disruptive Futurist is
continuously seeking to discover a combination, sequence or chain of events
which occur together in clusters – and when acting together demonstrate
either transient or instantiated dependencies (are interacting or co-related)
or both. That is, they are impacting with each other in some way or another
in a manner whereby we are able to forecast the next event. Basically, we
need to apply complex systems and chaos theory thinking to resolving all
Future Domain problems, opportunities, threats, issues or challenges.
• What factors or forces do we need to consider as being in-scope and critical
to the behaviour of the system? Which other factors or forces have we
ignored, overlooked or not considered? What further unknown factors or
unseen forces are there which we have not detected – but may still exist –
and what unknown factors or unseen forces are somehow exerting influence
over the behaviour of the system subject to the study - thus influencing the
behaviour of that systems elements as it evolves through Space and Time?

Quantitative (Technical) Analysis
• Quantitative (Technical) Analysis involves studying detailed micro-economic
models which process vast quantities of Market Data (commodity price data sets).
This method utilises a form of historic data analysis technique which smoothes or
profiles market trends into more predictable short-term price curves - which will
vary over time within a specific market.
• Quantitative (Technical) Analysts can initiate specific market responses when
prices reach support and resistance levels – via manual information feeds to
human Traders or by tripping buying or selling triggers where autonomous
Computer Trading is deployed. Technical Analysis is data-driven (experiential), not
model-driven (empirical) because our current economic models do not support the
observed market data. The key to both approaches, however, is in identifying,
analysing, and anticipating subtle changes in the average direction of movement
for Price Curves – which in turn reflect relatively short-term Market Trends.

Quantitative v. Qualitative Domains
Quantitative
(Technical)
Qualitative
(Narrative)
Futures Studies
Numeric
Definitive
Quantitative
(Technical)
Analysis
Investigative
Descriptive
Analytic
Social Sciences
Sociology
Economics
Life Sciences
Biology Ecology
Climate Change
Formulaic
Earth Sciences
Applied mathematics
Future Management
Environmental Sciences
Weather Forecasting
Particle Physics
String Theory
Statistics
Strategic Foresight
Predictive Analytics
Anthropology and Pre-history
Astronomy
Cosmology
Relativity
Astrophysics
Quantitative Analysis Pure mathematics
Geography
Geology
Archaeology
Economic
Analysis
Chemistry
Engineering
Astrology
Geo-physics
Palaeontology
Cluster Theory
Interpretive
Qualitative
(Narrative)
Analysis
Quantum Mechanics

Quantitative / Qualitative Analysis Techniques
TECHNICAL (QUANTITATIVE) METHODS TECHNICAL (QUANTITATIVE) METHODS (cont.)
Asymptotic Methods and Perturbation Theory Statistical Arbitrage
“Big Data” - Statistical analysis of very large scale (VLS) datasets Technical (Quant) Analysis
Capital Adequacy – Liquidity Risk Modelling – Basle / Solvency II Trading Strategies - neutral, HFT, pairs, macro; derivatives;
Convex analysis Trade Risk Modelling: – Risk = Market Sentiment – Actual Results
Credit Risk Modelling (PD, LGD) Value-at-Risk (VaR)
Data Audit, Data Profiling. Data Mining and CHAID Analysis Volatility modelling (ARMA, GARCH)
Derivatives (vanilla and exotics)
Dynamic systems behaviour and bifurcation theory NARRATIVE (QUALITATIVE) METHODS
Dynamic systems complexity mapping and network reduction
Differential equations (stochastic, parabolic) “Big Data” -, Clinical Trials ,Morbidity and Actuarial Outcomes
Extreme value theory Business Strategy, Planning, Forecasting Simulation and Consolidation
Economic Growth / Recession Patterns (Boom / Bust Cycles) Causal Layer Analysis (CLA)
Economic Planning and Long-range Forecasting Chaos Theory
Economic Wave and Business Cycle Analysis Cluster Theory
Financial econometrics (economic factors and macro models) Complexity Theory
Financial time series analysis Complex (non-linear) Systems
Game Theory and Lanchester Theory Complex Adaptive Systems (CAS)
Integral equations Computational Theory (Turing)
Interest rates derivatives Delphi Oracle /Expert Panel / Social Media Survey
Ordered (Linear) Systems (simple linear multi-factor equations) Economic Wave Theory – Business Cycles (Austrian School)
Market Risk Modelling (Greeks; VaR) Fisher-Pry Analysis and Gomperttz Analysis
Markov Processes Forensic “Big Data” – Social Mapping and Fraud Detection
Monte Carlo Simulations and Cluster Analysis Geo-demographic Profiling and Cluster Analysis
Non-linear (quadratic) equations Horizon Scanning, Monitoring and Tracking
Neural networks, Machine Learning and Computerised Trading Information Theory (Shannon)
Numerical analysis & computational methods Monetary Theory – Money Supply (Neo-liberal and Neo-classical)
Optimal Goal-seeking, System Control and Optimisation Pattern, Cycle and Trend Analysis
Options pricing (Black-Scholes; binomial tree; extensions) Scenario Planning and Impact Analysis
Price Curves – Support / Resistance Price Levels - micro models Social Media – market sentiment forecasting and analysis
Quantitative (Technical) Analysis Value Chain Analysis – Wealth Creation and Consumption
Statistical Analysis and Graph Theory Weak Signals, Wild Cards and Black Swan Event Forecasting

Qualitative (Narrative) Analysis
• Qualitative (Narrative) Analysis involves further processing of summarised results
generated by Quantitative (Technical) Analysis - super sets of many individual
micro-economic model runs. Techniques such as Monte Carlo Simulation cycle
macro-economic model runs repeatedly through thousands of iterations – minutely
varying the starting conditions for each and every individual run cycle.
• Results appear as a scatter diagram consisting of thousands of individual points for
commodity prices over a given time line. Instead of a random distribution – we
discover clusters of closely related results in a background of a few scattered
outliers. Each of these clusters represents a Scenario – which is analysed using
Cluster Analysis methods - Causal Layer Analysis (CLA), Scenario Planning and
Impact Analysis– where numeric results are explained as a narrative story about a
possible future outcome – along with the probability of that scenario materialising.

• Wave-form Analytics is a new analytical tool “borrowed” from spectral wave
frequency analysis in Physics – and is based on Time-frequency analysis – a
technique which exploits the wave frequency and time symmetry principle. This is
introduced here for the first time in the study of human activity waves, and in the
field of economic cycles business cycles, patterns and trends.

in nature to biological behaviour in an organism than to Disorderly, Chaotic,
Stochastic Systems (“Random” Systems). For example, the remarkable
shocks (1970-72) and credit supply shocks (1927- 1929 and 2008 onwards).
Unexpected and surprising Cycle Pattern changes have historically occurred
during regional and global conflicts being fuelled by technology innovation-driven
arms races - and also during US Republican administrations (Reagan and Bush -
why?). Just as advances in electron microscopy have revolutionised biology -
non-stationary time series wave-form analysis has opened up a new space for

The Temporal Wave
• The Temporal Wave is a novel and innovative method for Visual Modelling and Exploration
of Geospatial “Big Data” - simultaneously within a Time (history) and Space (geographic)
context. The problems encountered in exploring and analysing vast volumes of spatial–
temporal information in today's data-rich landscape – are becoming increasingly difficult to
manage effectively. In order to overcome the problem of data volume and scale in a Time
(history) and Space (location) context requires not only traditional location–space and
attribute–space analysis common in GIS Mapping and Spatial Analysis - but now with the
additional dimension of time–space analysis. The Temporal Wave supports a new method
of Visual Exploration for Geospatial (location) data within a Temporal (timeline) context.
(timeline) data along with data visualisation techniques - thus improving accessibility,
exploration and analysis of the huge amounts of geo-spatial data used to support geo-
visual “Big Data” analytics. The temporal wave combines the strengths of both linear
timeline and cyclical wave-form analysis – and is able to represent data both within a Time
(history) and Space (geographic) context simultaneously – and even at different levels of
granularity. Linear and cyclic trends in space-time data may be represented in combination
with other graphic representations typical for location–space and attribute–space data-
types. The Temporal Wave can be used in roles as a time–space data reference system,
as a time–space continuum representation tool, and as time–space interaction tool.

Composite Economic Wave Series
• Economic systems tend to demonstrate Complex Adaptive System (CAS) behaviour –
rather than a simple series of chaotic “Random Events” – very similar to the behaviour of
living organisms. The remarkable long-term stability and resilience of market economies is
demonstrated by the impact and subsequent recovery from Wild Card and Black Swan
Events. Surprising pattern changes occur during wars, arm races, and during Republican
administrations, causing unexpected stock market crashes - such as oil price shocks and
credit crises. Wave-form Analytics for non-stationary time series analysis opens up a new
and remarkable opportunity for business cycle studies and economic policy diagnostics.
• The role of time scale and preferred reference from economic observation is explored in
detail. For example - fundamental constraints for Friedman's rational arbitrageurs are re
examined from the view of information ambiguity and dynamic instability. Alongside
Joseph Schumpter’s Economic Wave Series and Strauss and Howe’s Generation Waves,
we also discuss Robert Bronson's SMECT Forecasting Model - which integrates both
Business and multiple Stock-Market Cycles into its structure.....
• Composite Economic Wave Series
– Saeculum - Century Waves
– Generation Waves (Strauss and Howe)
– Joseph Schumpter’s Economic Wave Series
– Robert Bronson’s SMECT Forecasting Model

Weak Signals, Wild Cards and
Black Swan Event Scenarios
• In this section, we examine empiric evidence from global “Big Data” on how shock waves
to geo-political economic and business systems impact on business cycles, patterns and
trends. We first review Gail's work (1999), which uses long-running restrictions to identify
shock waves, and examine whether the identified shocks can be plausibly interpreted: -
• Wild card and Black Swan Event Types
– Technology Shock Waves
– Supply / Demand Shock Waves
– Political, Economic and Social Change
– Global Conflict – War, Terrorism, and Insecurity
– Natural Disasters and Catastrophes – Global Massive Change Events
• We do this in three ways. Firstly, we derive additional long-run restrictions and use them as
identification tests. Secondly, we compare the qualitative implications from the model with the
impulse responses of variables such as production, wages and consumption. Third, we test
whether some standard .exogenous. variables predicate the shock events. We discovered that
that Weak Signals may predicate coming technology shock waves, oil price shocks, and military
conflict. We then show ways in which a standard DGE model can be modified to fit Gail's finding
that a positive technology shock may lead to lower labour input. Finally, we re-examine the
properties of the other key shocks to the economic system and demonstrate the impact of oil
price shocks and military conflict .

Waves, Cycles, Patterns and Trends
• Business Cycles were once thought to be an economic phenomenon due to periodic
fluctuations in economic activity. These mid-term economic cycle fluctuations are usually
measured using Real (Austrian) Gross Domestic Product (rGDP). Business Cycles take
place against a long-term background trend in Economic Output – growth, stagnation or
recession – which affects Money Supply as well as the relative availability and consumption
(Demand v. Supply and Value v. Price) of other Economic Commodities. Any excess of
Money Supply may lead to an economic expansion or “boom”, conversely shortage of
Money Supply (Money Supply shocks – the Liquidity Trap) may lead to economic
contraction or “bust”. Business Cycles are recurring, fluctuating levels of economic activity
experiences in an economy over a significant timeline (decades or centuries).
• The five stages of Business Cycles are growth (expansion), peak, recession (contraction),
trough and recovery. Business Cycles were once widely thought to be extremely regular,
with predictable durations, but today’s Global Market Business Cycles are now thought to
be unstable and appear to behave in irregular, random and even chaotic patterns – varying
in frequency, range, magnitude and duration. Many leading economists now also suspect
that Business Cycles may be influenced by fiscal policy as much as market phenomena -
even that Global Economic “Wild Card” and “Black Swan” events are actually triggered
by Economic Planners in Government Treasury Departments and in Central Banks as a
result of manipulating the Money Supply under the interventionist Fiscal Policies adopted by
some Western Nations.

Weak Signals
• Weak Signals – are subliminal temporal indicators of ideas, patterns or trends
coming to meet us from the future – or perhaps indicators of novel and
emerging, ideas, influences and messages which may interact with both
current and pre-existing patterns and trends to impact or affect some change
taking place in our current environment – or even an early warning or news of
impending random events or catastrophes which, at some point in the Future,
time or place, may predicate, influence or impact on future processes or
events - or effect subtle to major changes in how we live, work and play.
• A Weak Signal is an early warning of change, which typically becomes
stronger by combining with other signals. The significance of a weak future
signal is determined by the objectives of its recipient, and finding it typically
requires systematic searching. A weak future signal requires: i) support, ii)
critical mass, iii) growth of its influence space, and dedicated actors, i.e. ‘the
champions’, in order to become a strong future signal, or to prevent itself from
becoming a strong negative signal. A Weak Future Signal is often recognised
by pioneers or special groups - not by acknowledged subject matter experts.

Weak Signals
Weak Signal Property Different views and viewpoints
1 Nature Weak Signals are subtle indicators of ideas, patterns or trends
that give us a glimpse into the future – predicating possible
future transformations and changes which are happening on
or even just over the visible horizon, changes in how we do
business, what business we do, and the future environment
in which we will all live and work.
2 Quality Weak Signals may be novel and surprising from the signal
analyst's vantage point - although many other signal analyst's
may have already, failed to recognise, misinterpreted or
dismissed the same Weak Signals
3 Purpose Weak Signals are used for Horizon Scanning, Tracking and
Monitoring and for Future Analysis and Management
4 Source Weak Signals, Strong Signals, Wild Cards and Black Swan
Events – are a sequence of waves linked and integrated in
ascending order of magnitude, which have a common source
or origin - either a single Random Event instance or arising
from a linked series of chaotic and disruptive Random Events
– crearing a Random Event Cluster or Random Event Storm.

Weak Signals
5 Wave-form Analytics
and “Big Data” Global
Internet Content
Wave-form Analytics may be used with “Big Data” to analyse
how Random Events propagate through the space-time
continuum in a related and integrated series of waves with an
ascending order of magnitude and impact – the first wave to
arrive is the fastest travelling - Weak Signals - something like
a faint echo of a Random Event which may be followed in
turn by a ripple (Strong Signals) then possibly by a wave (Wild
Card) - which may indicate the unfolding a further increase in
magnitude and intensity which finally arrives catastrophically
- something like a tsunami (Black Swan Event).
6 Identification Weak Signals are sometimes difficult to track down, receive,
tune in, identify, amplify and analyse amid the overwhelming
volume of “white noise” from stronger signals and other
foreground and background noise sources
7 Principle of Dual Nature
(possibility of either an
Opportunity or Threat)
Weak Signals may indicate the possibility of either a potential
future threat or opportunity to yourself or your organization -
or foretell the pending arrival of a future advantage or
reversal – a “Wild card” or Black Swan” event

Weak Signals
8 Perception Weak Signals are often missed, dismissed or scoffed at by
other Subject Matter Experts
9 Opportunity Weak Signals contain novel and emerging ideas, influences
and messages - therefore they represent an early window of
potential opportunity.
10 Impact Weak Signals arrive, become established, develop, grow and
mature - then peak, plateau decline and collapse – or interact
with current and pre-existing extrapolations, patterns or
trends to transform or change the landscape
11 Receipt / Observation Every Weak Future Signal requires –
1. a Receiver / Observer / Analyst (which could be
automated by deploying “Big Data” Analytics)
2. Subject mater experts, special interest groups etc. and
Empowered Stakeholders to achieve critical momentum
3. growth of its support, championship and influence space
4. dedicated actors, e.g. “supporters and champions”

Weak Signals
12 Duration Weak signals only last for a brief period: – Transient Signal
1. Weak signals are seen as a sign that lasts for a moment,
but a phenomenon behind it lasts longer – a following
Strong Signal
2. Weak signals are phenomena that last for a short Period
of time (wild cards?)
Weak signal lasts longer:– it now becomes a Strong Signal
3. A weak signal is a cause for a change in the future
4. A weak signal is itself a change phenomenon
13 Transition phenomenon 1. A weak signal phenomenon is a result of a Random Event
2. A weak signal is a sign of a future disruptive changes or
Individual / Local / Regional / Global Transformations
3. A weak signal may be a member of a larger Wave Series
4. The transition phenomenon of a weak signal is that in the
future it will either get stronger (becomes a Strong
Signal) or weaker (attenuate and disappear from view)

Weak Signals
14 Objectivity v. Subjectivity 1. Weak signals exist independently of their receiver.
2. “Weak signals float in the phenomena space and
wait for someone to find them” – automation via
“Big Data” Analytics can address this issue.....
3. A weak signal does not exist without reception /
interpretation by a receiver / observer (which may
mitigated by automated via “Big Data” Analytics)
15 Interpretation The interpretation of a same signal can be different
from the viewpoint of different receivers of the signal.
Human Interpretation adds subjectivity to the signal –
even though it is thought to be objective – “Big Data”
Analytics may be used for the Validation process
16 Signal Strength over Time 1. The weak signal (as an indicator) is strengthening
2. A phenomenon, interpreted as weak signal, is
strengthening – it now becomes a Strong Signal
3. A phenomenon whose status is in question, is
strengthening – it now becomes a Strong Signal

Weak Signals
17 Roles and Responsibilities –
Receivers /Observers /
Analysts of the weak signal
(who receives, identifies,
observes and classifies)
1. Difficulties in defining the concept of Weak Signals
arising from a single instance or linked series of
Random Events – or from an Event Cluster or
Storm – to Empowered Stakeholders, subject
mater experts, special interest groups, etc.
2. Differences in opinion on signal content between
signal Receiver, Observer and Analysts :- resolved
by special interest groups, subject mater experts
18 Roles and Responsibilities –
Analysts / Interpreters /
Stakeholders in the signal
(who analyses and draws
useful valid conclusions)
1. Who is drawing the conclusions on the cause-effect
relationship? – the Receiver and the Observer
2. Who is defining the credibility and significance of
weak signal? – the Observer and the Analyst
3. Who is the one that can affect important decisions
concerning the future? – Empowered Stakeholders

Strong Signals
• Strong Signals – represent the first clear and visible presence of a Random Event – the
secondary arrival of stronger but slower-travelling waves containing more information of
possible, probable and alternative future events – random events, future catastrophes, or
indications o novel and emerging, ideas, influences and messages which may interact with
both current and pre-existing patterns and trends to impact or affect some change taking
place in our environment - at some point, time or place in the future – for example, what
future climatic and ecological environment will live , work and play in what political, social
and economic environment will live , work and play in, how we live, work and play, what
business we do, how we do business and who we do Business with......
1. Strong Signals may demonstrate a substantial lag time before they follow their
preceding indicators, prior Weak Signals
2. Strong Signals may contain confirmation about future events – random events,
catastrophes, or indications o novel and emerging, ideas, influences and messages.
They therefore present a second potential window of opportunity if the first Weak Signals
in the series were undetected, overlooked or dismissed
3. Strong Signals arrive, become established, develop, grow and mature - then peak,
plateau decline and collapse or interact with current and pre-existing extrapolations,
patterns or trends which act to transform or change the current outlook or landscape.

Wild Cards
• Wild Cards – are any sudden and unexpected Local or Regional variation or change in the status or
condition of natural (environmental, ecological) or Human (military, political, social or economic)
perspectives - which promises either a new, unexpected or significant advantage, gain or opportunity -
or the prospect of the loss or failure of an important asset, capability or facility – and may also contain
within itself the threat of a possible future advantage or reversal – a “Wild card” or Black Swan” event.
1. Wild Card Events have been defined, for example, by Rockfellow (1994), who speculated that a wild card is "an
event having a low probability of occurrence, but an inordinately high impact if it does occur."
2. Wild Cards may represent either a potential threat or perceived opportunity to yourself and / or your organization -
and may contain within them, the seeds of a possible major future global advantage or reversal – a forthcoming
“Black Swan” event
3. Listing examples of specific 21st Century Wild Cards in 1994, Rockfellow defined three wild cards principles: -
1. 21st Century Wild Cards manifest at the beginning of the Business Cycle (i.e. within 11 years of a prior
cycle),
2. 21st Century Wild Cards have a probability of occurring at a rate of less than 1 in 10 years,
3. 21st Century Wild Cards events will likely have high impact on international businesses
4. Wild Cards are "low-probability, hi-impact events that happen quickly" and "they have huge sweeping
consequences." Wild cards, according to Petersen, generally surprise everyone, because they materialize so
quickly that the underlying social systems cannot effectively anticipate or respond to them (Petersen 1999).
5. According to Cornish (2003: 19), a Wild Card is an unexpected, surprising or even startling event that has sudden
impact, important outcomes and far-reaching consequences. He continues: "Wild cards have the power to radically
change many processes and events and to entirely overturn people's thinking, planning and actions."

Black Swan Events
• Black Swan Events – refer to any unforeseen, sudden and extreme Global-level
transformation or change events in either the military, political, social, economic or
environmental landscape - having an inordinately low probability of occurrence -
coupled with an extraordinarily high impact when they do occur (Nassim Taleb).
1. Black Swan Events are a complete and totally unexpected surprise to the observer
2. Black Swan Events have a major impact as a catalyst or agent of global
transformation and change.
3. Black Swan Events may represent wither a potential catastrophic threat or novel,
unexpected opportunities.
4. At its first appearance, the Black Swan Event is rationalized by hindsight, as if it
could or should have been expected (e.g., the relevant Weak Signals were available
but not detected, identified or accounted for).

Temporal Disturbances in the Space-
Time Continuum – Random Events
• Randomness. Weak Signals, Wild Cards and Black Swan Events – may be evidence of radically
disruptive and chaotic Random Events which propagate through the Space-Time Continuum in the
same way as a ripple or wave crosses the ocean. Random Events may be able to “bend” the Space-
Time continuum- which brings two discrete points on different Hyperspace Planes closer together
over a time interval extended through a time-line along the Time axis of the Minkowski Space-Time
Continuum. Perhaps some Wave Types - Weak Signals, Wild Cards and Black Swan Events - can
travel faster or take a different route – perhaps because their Wave forms can propagate through
Space-Time more rapidly than the Wave forms of the other types of Wave - or specific Wave Types
are able to take a “short cut” between two points on different Hyperspace Planes.
• • CASE STUDY • A Pyroclastic Volcanic Eruption begins with a series of linked and integrated
events which have a common origin or source - which in turn generate a sequence of waves in
ascending orders of magnitude. Pyroclastic Volcanic Eruptions begin with a sequence of Random
Events - in this case, it is a sequence of chaotic and disruptive Earthquakes somewhere deep under
an Mountain Chain which is built up from Andesitic Volcanoes – such as the Andes Mountain Chain.
The Andes Mountains are located near an Oceanic Plate subduction zone – an area where the Pacific
Oceanic Plate plunges under the South American Continental Margin. Sediment, sea water and
organic remains from the Ocean floor are carried down towards earth’s mantle and heat up as the
Oceanic Plate plunges deeper into the Earth’s Mantle. Liquids and gases released by this heating
cause the rocks in the Earth’s Mantle to melt, turning from a plastic semi-solid into a liquid. This liquid
then rises through the Earth’s crust and travels towards the surface, collecting in pools forming
Magma Chambers - before finally breaking at the surface through and erupting as Volcanic Magma

• Randomness. Weak Signals, Wild Cards and Black Swan Events – may be evidence of radically disruptive
and chaotic Random Events which propagate through the Space-Time Continuum in the same way as a ripple or
wave crosses the ocean. Random Events may be able to “bend” the Space-Time continuum- which brings two
discrete points on different Hyperspace Planes closer together over a time interval extended through a time-line
along the Time axis of the Minkowski Space-Time Continuum. Perhaps some Wave Types - Weak Signals, Wild
Cards and Black Swan Events - can travel faster or take a different route – perhaps because their Wave forms
can propagate through Space-Time more rapidly than the Wave forms of the other types of Wave - or specific
Wave Types are able to take a “short cut” between two points on different Hyperspace Planes.
• • CASE STUDY • A Pyroclastic Volcanic Eruption begins with a series of linked and integrated events which
have a common origin or source - which in turn generate a sequence of waves in ascending orders of magnitude.
Pyroclastic Volcanic Eruptions begin with a sequence of Random Events - in this case, it is a sequence of
chaotic and disruptive Earthquakes somewhere deep under an Mountain Chain which is built up from Andesitic
Volcanoes – such as the Andes Mountain Chain. The Andes Mountains are located near an Oceanic Plate
subduction zone – an area where the Pacific Oceanic Plate plunges under the South American Continental
Margin. Sediment, sea water and organic remains from the Ocean floor are carried down towards earth’s mantle
and heat up as the Oceanic Plate plunges deeper into the Earth’s Mantle. Liquids and gases released by this
heating cause the rocks in the Earth’s Mantle to melt, turning from a plastic semi-solid into a liquid. This liquid
then rises through the Earth’s crust and travels towards the surface, collecting in pools forming Magma Chambers
- before finally breaking at the surface through and erupting as Volcanic Magma
• Earthquakes are created when the Continental and Oceanic Plates stick together – and periodically unzip and
slide over each other - causing a sequence of tremors or waves. P-waves oscillate up-and-down, whilst S-waves
oscillate from side-to-side. The P and S waves from the Earthquake propagate rapidly through the earth in a
related and integrated series of waves - but travelling at different speeds. The first waves to arrive at an observer
of the event are vertical (up / down) disturbances (P-waves) which are followed moments later by a horizontal
(side-to-side) disturbance (S-waves) which have increased magnitude and intensity.

• Tectonic Earthquakes are created when Continental and Oceanic Plates stick together –
then periodically unzip and slide over each other – thus creating a sequence of tremors or
waves. P-waves oscillate up-and-down, whilst S-waves oscillate from side-to-side. The P
and S waves from the Earthquake propagate rapidly through the earth in a related and
integrated series of waves - but travelling at different speeds. The first waves to arrive at
an observer of the event are vertical (up / down) disturbances (P-waves) which are
followed moments later by a horizontal (side-to-side) disturbance (S-waves) which have
increased magnitude and intensity.
• P-waves travel fastest through the earth so they arrive first, as Weak Signals. These faster
P-waves are followed by slower but more dramatic and intense S-waves – these Strong
Signals are now giving indications of what is about to follow. Next in the sequence is the
Wild Card Event. As the volcano erupts, its ash cloud billows high into the atmosphere.
Finally the Black Swan Event arrives. As the volcano continues to erupt, the ash
column can no longer support its own weight. It collapses in onto itself and plunges down
the slopes of the Volcano. Surging relentlessly downhill, the catastrophic disturbance of
the Pyroclastic wave destroys all life and covers the landscape with layers of suffocating,
burning hot ash, a black wave covering over everything that lies before it. This, for
example, is what happened when Vesuvius erupted and covered Herculaneum and
Pompeii with over twenty metres of superheated magma erupting as gases and ash.

• Earthquakes are created when the Continental and Oceanic Plates stick together – and periodically unzip and slide over each other - causing a sequence of tremors or
waves. P-waves oscillate up-and-down, whilst S-waves oscillate from side-to-side. The P and S waves from the Earthquake propagate rapidly through the earth in a
related and integrated series of waves - but travelling at different speeds. The first waves to arrive at an observer of the event are vertical (up / down) disturbances (P-
waves) which are followed moments later by a horizontal (side-to-side) disturbance (S-waves) which have increased magnitude and intensity.
•
• P-waves travel fastest through the earth so they arrive first, as Weak Signals. These faster P-waves are followed by slower but more dramatic and intense S-waves –
these Strong Signals are now giving indications of what is about to follow. Next in the sequence is the Wild Card Event. As the volcano erupts, its ash cloud billows
high into the atmosphere. Finally the Black Swan Event arrives. As the volcano continues to erupt, the ash column can no longer support its own weight. It collapses
in onto itself and plunges down the slopes of the Volcano. Surging relentlessly downhill, the catastrophic disturbance of the Pyroclastic wave destroys all life and covers
the landscape with layers of suffocating, burning hot ash, a black wave covering over everything that lies before it. This, for example, is what happened when Vesuvius
erupted and covered Herculaneum and Pompeii with over twenty metres of superheated magma erupting as gases and ash.
•
• • CASE STUDY • A Tsunami Event consists of a sequence of linked and integrated waves in ascending orders of magnitude which have a common origin or source – in
this case, the Random Events begin with a series of chaotic and disruptive Earthquakes somewhere offshore in a subduction zone at a Continental and Oceanic Plate
Margin. Earthquakes are formed as the Continental and Oceanic Plates stick together – and then unzip, causing a sequence of random and chaotic tremors. The P and
S waves from the Earthquake propagate rapidly through the earth in a related and integrated series of waves travelling at different speeds – the first to arrive are vertical
(up / down) disturbances (P-waves) which are followed by a horizontal (side-to-side) disturbance (S-waves) which further increases magnitude and intensity. The P and S
waves from the Earthquake propagate rapidly through the earth in a related and integrated series of wave forms travelling at different speeds – the first to arrive are
vertical (up / down) disturbances (P-waves) which are followed by a horizontal (side-to-side) disturbance (S-waves) which further increases magnitude and intensity.
•
• Earthquakes are created either when stratigraphic units either side of a geological fault are displaced in relation to each other - or when either Continental or Oceanic
Plates moving relative to each other get stuck together. Over time stress builds up at the fault-line, causing them to dramatically unzip – then slip and slide over each
other - releasing a sequence of energetic tremors or waves. P-waves oscillate up-and-down, whilst S-waves oscillate from side-to-side. The P and S waves from the
Earthquake propagate rapidly through the earth in a related and integrated series of waves - but travelling at different speeds. The first waves to arrive at an observer of
the event are vertical (up / down) disturbances (P-waves) which are followed moments later by a horizontal (side-to-side) disturbance (S-waves) which have increased
magnitude and intensity.
•
• P-waves travel fastest through the earth so they arrive first, as Weak Signals. These faster P-waves are followed by slower but more dramatic and intense S-waves –
these Strong Signals are now giving indications of what is about to come. Next in the sequence comes a Wild Card Event. At the nearest coastline, the sea level first
falls as the Tsunami Wave withdraws water from the shore – this is the final warning, the Wild Card Event. Finally the tsunami arrives as a Black Swan Event. This is the
last Wave in the sequence. The chaotic and radically disruptive Tsunami wave travels through the ocean waters and arrives as the final Black Swan Event. Surging
relentlessly inland, threatening life and shifting scenery, the catastrophic disturbance of the Tsunami wave sweeps up everything that lies in its way, a black wave
swallowing everything before it. This type of Black Swan Event has occurred twice this century - the Boxing Day and Japanese Tsunamis.
•

•
• Randomness. Weak Signals, Wild Cards and Black Swan Events – may be evidence of disruptive and chaotic Random Events which propagate through the Space-Time Continuum in the
same way as a ripple or wave. Random Events may be able to “bend” the Space-Time continuum- which brings two discrete points on different Hyperspace Planes closer together over a
time interval extended through a time-line along the Time axis of the Minkowski Space-Time Continuum. Perhaps some Wave Types - Weak Signals, Wild Cards and Black Swan Events -
can travel faster or take a different route – perhaps because their Wave forms can propagate through Space-Time more rapidly than the Wave forms of the other types of Wave - or
specific Wave Types are able to take a “short cut” between two points on different Hyperspace Planes.
•
• • CASE STUDY • A Pyroclastic Volcanic Eruption begins with a series of linked and integrated events which have a common origin or source - which in turn generate a sequence of
waves in ascending orders of magnitude. Pyroclastic Volcanic Eruptions begin with a sequence of Random Events - in this case, it is a sequence of chaotic and disruptive Earthquakes
somewhere deep under an Mountain Chain which is built up from Andesitic Volcanoes – such as the Andes Mountain Chain. The Andes Mountains are located near an Oceanic Plate
subduction zone – an area where the Pacific Oceanic Plate plunges under the South American Continental Margin. Sediment, sea water and organic remains from the Ocean floor are
carried down towards earth’s mantle and heat up as the Oceanic Plate plunges deeper into the Earth’s Mantle. Liquids and gases released by this heating cause the rocks in the Earth’s
Mantle to melt, turning from a plastic semi-solid into a liquid. This liquid then rises through the Earth’s crust and travels towards the surface, collecting in pools forming Magma
Chambers - before finally breaking at the surface through and erupting as Volcanic Magma
•
• Earthquakes are created when the Continental and Oceanic Plates stick together – and periodically unzip and slide over each other - causing a sequence of tremors or waves. P-waves
oscillate up-and-down, whilst S-waves oscillate from side-to-side. The P and S waves from the Earthquake propagate rapidly through the earth in a related and integrated series of waves
- but travelling at different speeds. The first waves to arrive at an observer of the event are vertical (up / down) disturbances (P-waves) which are followed moments later by a horizontal
(side-to-side) disturbance (S-waves) which have increased magnitude and intensity.
•
• P-waves travel fastest through the earth so they arrive first, as Weak Signals. These faster P-waves are followed by slower but more dramatic and intense S-waves – these Strong Signals
are now giving indications of what is about to follow. Next in the sequence is the Wild Card Event. As the volcano erupts, its ash cloud billows high into the atmosphere. Finally the
Black Swan Event arrives. As the volcano continues to erupt, the ash column can no longer support its own weight. It collapses in onto itself and plunges down the slopes of the Volcano.
Surging relentlessly downhill, the catastrophic disturbance of the Pyroclastic wave destroys all life and covers the landscape with layers of suffocating, burning hot ash, a black wave
covering over everything that lies before it. This, for example, is what happened when Vesuvius erupted and covered Herculaneum and Pompeii with over twenty metres of superheated
magma erupting as gases and ash.
•
• • CASE STUDY • A Tsunami Event consists of a sequence of linked and integrated waves in ascending orders of magnitude which have a common origin or source – in this case, the
Random Events begin with a series of chaotic and disruptive Earthquakes somewhere offshore in a subduction zone at a Continental and Oceanic Plate Margin. Earthquakes are formed
as the Continental and Oceanic Plates stick together – and then unzip, causing a sequence of random and chaotic tremors. The P and S waves from the Earthquake propagate rapidly
through the earth in a related and integrated series of waves travelling at different speeds – the first to arrive are vertical (up / down) disturbances (P-waves) which are followed by a
horizontal (side-to-side) disturbance (S-waves) which further increases magnitude and intensity. The P and S waves from the Earthquake propagate rapidly through the earth in a related
and integrated series of wave forms travelling at different speeds – the first to arrive are vertical (up / down) disturbances (P-waves) which are followed by a horizontal (side-to-side)
disturbance (S-waves) which further increases magnitude and intensity.
•
• Earthquakes are created either when stratigraphic units either side of a geological fault are displaced in relation to each other - or when either Continental or Oceanic Plates moving
relative to each other get stuck together. Over time stress builds up at the fault-line, causing them to dramatically unzip – then slip and slide over each other - releasing a sequence of
energetic tremors or waves. P-waves oscillate up-and-down, whilst S-waves oscillate from side-to-side. The P and S waves from the Earthquake propagate rapidly through the earth in a
related and integrated series of waves - but travelling at different speeds. The first waves to arrive at an observer of the event are vertical (up / down) disturbances (P-waves) which are
followed moments later by a horizontal (side-to-side) disturbance (S-waves) which have increased magnitude and intensity.
•
• P-waves travel fastest through the earth so they arrive first, as Weak Signals. These faster P-waves are followed by slower but more dramatic and intense S-waves – these Strong Signals
are now giving indications of what is about to come. Next in the sequence comes a Wild Card Event. At the nearest coastline, the sea level first falls as the Tsunami Wave withdraws
water from the shore – this is the final warning, the Wild Card Event. Finally the tsunami arrives as a Black Swan Event. This is the last Wave in the sequence. The chaotic and radically
disruptive Tsunami wave travels through the ocean waters and arrives as the final Black Swan Event. Surging relentlessly inland, threatening life and shifting scenery, the catastrophic
disturbance of the Tsunami wave sweeps up everything that lies in its way, a black wave swallowing everything before it. This type of Black Swan Event has occurred twice this century -
the Boxing Day and Japanese Tsunamis.
•

• Nothing in the galaxy, in our world, or in our own personal existence, ever
happens in isolation of other places, objects, individuals and events. This
is a very simple and fundamental fact about life, nature and the universe.
In the same moment as one event occurs or happens in one location,
infinite other events are simultaneously taking place in countless other
locations, which in turn impact on an innumerable collection of other co-
impacted objects, individuals and events.
• In order to study and prepare for the future - we need to bear in mind the
fact that all objects and events are potentially connected in some way or
other. None of these random events occurs in isolation, none are entirely
independent or unconnected – as every object in the Universe exerts an
influence over every other object – however tenuously. This phenomenon
of Event Clustering is something that, through our own experience, we are
all familiar with, know about, and can easily follow and understand.

• It is the dream of every Futurist to be the first to discover an important and
significant chain of random events which occur together in clusters, that
together demonstrate a transient or instantiated dependencies - that is,
they are interacting or co-related so they are impacting upon each other in
some way or another. Fundamentally - we need to apply systems thinking
to analyse a combination or sequence of random events in order to resolve
Future Domain problems or opportunities, threats, issues or challenges.
• What factors or forces do we need to consider as being in-scope and critical
to the behaviour of the system we are studying? Which other factors or
forces have we ignored, overlooked or simply just not considered? What
further unknown factors or unseen forces are there which we have not
detected – but may still exist – which are impacting on or interacting with
various system elements and thus influencing the behaviour of the system?

Weak Signals, Wild Cards and
Black Swan Events
• In this section, we examine empiric evidence from global “Big Data” on how shock waves
to geo-political economic and business systems impact on business cycles, patterns and
trends. We first review Gail's work (1999), which uses long-running restrictions to identify
shock waves, and examine whether the identified shocks can be plausibly interpreted: -
• Wild card and Black Swan Events
– Impact of War, Terrorism and Insecurity
• We do this in three ways. Firstly, we derive additional long-run restrictions and use them
as identification tests. Secondly, we compare the qualitative implications from the model
with the impulse responses of variables such as production, wages and consumption.
Third, we test whether some standard .exogenous. variables predicate the shock events.
We discovered that that Weak Signals may predicate coming technology shock waves, oil
price shocks, and military conflict. We then show ways in which a standard DGE model
can be modified to fit Gail's finding that a positive technology shock may lead to lower
labour input. Finally, we re-examine the properties of the other key shocks to the economic
system and demonstrate the impact of oil price shocks and military conflict .

Waves, Cycles, Patterns and Trends
• Business Cycles were once thought to be an economic phenomenon due to
periodic fluctuations in economic activity. These mid-term economic cycle
fluctuations are usually measured using Real (Austrian) Gross Domestic Product
(rGDP). Business Cycles take place against a long-term background trend in
Economic Output – growth, stagnation or recession – which affects Money
Supply as well as the relative availability and consumption (Demand v. Supply
and Value v. Price) of other Economic Commodities. Any excess of Money
Supply may lead to an economic expansion or “boom”, conversely shortage of
Money Supply may lead to economic contraction or “bust”. Business Cycles are
recurring, fluctuating levels of economic activity experiences in an economy over
a significant timeline (decades or centuries).
• The five stages of Business Cycles are growth (expansion), peak, recession
(contraction), trough and recovery. Business Cycles were once widely thought
to be extremely regular, with predictable durations, but today’s Global Market
Business Cycles are now thought to be unstable and appear to behave in
irregular, random and even chaotic patterns – varying in frequency, range,
magnitude and duration. Many leading economists now also suspect that
Business Cycles may be influenced by fiscal policy as much as market
phenomena - even that Global Economic “Wild Card” and “Black Swan”
events are actually triggered by Economic Planners in Government Treasury
Departments and in Central Banks as a result of manipulating the Money Supply
under the interventionalist Fiscal Policies adopted by some Western Nations.

• Complex Systems and Chaos Theory has been used extensively in the field
of Futures Studies, Strategic Management, Natural Sciences and Behavioural
Science. It is applied in these domains to understand how individuals within
populations, societies, economies and states act as a collection of loosely
coupled interacting systems which adapt to changing environmental factors
and random events – bio-ecological, socio-economic or geo-political.
• Complex Systems and Chaos Theory treats individuals, crowds and
populations as a collective of pervasive social structures which are influenced
by random individual behaviours – such as flocks of birds moving together in
flight to avoid collision, shoals of fish forming a “bait ball” in response to
predation, or groups of individuals coordinating their behaviour in order to
respond to external stimuli – the threat of predation or aggression – or in order
to exploit novel and unexpected opportunities which have been discovered or
presented to them.

• The defining concept for understanding the effects of Chaos Theory on Complex Systems is that with
any vanishingly small differences in the initial conditions at the onset of a chaotic system cycle – those
minute and imperceptible differences which create slightly different starting points result in massively
different outcomes between two otherwise identical systems, both operating within the same time frame.
• The discovery of Chaos and Complexity has increased our understanding of the Cosmos and its effect
on us. If you surf the chaos content regions of the internet, you will invariably encounter terms such as: -
• These influences can take some time to manifest themselves, but that is the nature of the phenomena
identified as a "strange attractor." Such differences could be small to the point of invisibility - how tiny
can influences be to have any effect? This is captured in the “butterfly scenario” described below.
1. Chaos
2. Clustering
3. Complexity
4. Butterfly effect
5. Disruption
6. Dependence
7. Feedback loops
8. Fractal patterns and dimensions
9. Harmonic Resonance
10. Horizon of predictability
11. Interference patterns
12. Massively diverse outcomes
13. Phase space and locking
14. Randomness
15. Sensitivity to initial conditions
16. Self similarity (self affinity)
17. Starting conditions
18. Stochastic events
19. Strange attractors
20. System cycles (iterations)
21. Time-series Events
22. Turbulence
23. Uncertainty
24. Vanishingly small differences

• Weaver (Complexity Theory) along with Gleick and Lorenzo (Chaos Theory) have
given us some of the tools that we need to understand these complex, interrelated
chaotic and radically disruptive political, economic and social events such as the
collapse of Global markets – and the various protests against this - using Event
Decomposition, Complexity Mapping, and Statistical Analysis to help us identify
patterns, extrapolations, scenarios and trends unfolding as seemingly unrelated,
random and chaotic events. The Hawking Paradox, however, challenges this view of
Complex Systems by postulating that uncertainty dominates complex, chaotic systems
to such an extent that future outcomes are both unknown - and unknowable.
• System Complexity is typically characterised by the number of elements in a system,
the number of interactions between those elements and the nature (type) of
interactions. One of the problems in addressing complexity issues has always been
distinguishing between the large number of elements and relationships, or
interactions evident in chaotic (disruptive, unconstrained) systems - and the still large,
but significantly smaller number of elements and interactions found in ordered
(constrained) systems. Orderly (constrained) System Frameworks tend to act to both
reduce the total number of more-uniform elements and interactions with fewer
regimes and of reduced size – and feature explicit rules which govern less random
and chaotic, but more highly-ordered, internally correlated and constrained
interactions – as compared with the massively increased random, chaotic and
disruptive behaviour exhibited by Disorderly (unconstrained) System Frameworks.

• System Complexity is typically characterised and measured by the number of elements in a
system, the number of interactions between elements and the nature (type) of interactions.
• One of the problems in addressing complexity issues has always been distinguishing between
the large number of elements (components) and relationships (interactions) evident in chaotic
(unconstrained) systems - Chaos Theory - and the still large, but significantly smaller number
of both and elements and interactions found in ordered (constrained) Complex Systems.
• Orderly System Frameworks tend to dramatically reduce the total number of elements and
interactions – with fewer and smaller classes of more uniform elements – with reduced and
sparser regimes of more restricted relationships featuring more highly-ordered, better internally
correlated and constrained interactions – as compared with Disorderly System Frameworks.
Simplexity
Ordered
Complexity
Disordered
Complexity
Complex Adaptive
Systems (CAS)
Linear
Systems
ChaosOrder

• When Systems demonstrate properties of Complex Adaptive Systems (CAS) - which is
often defined as consisting of a small number of relatively simple and loosely connected
systems - then they are much more likely to adapt to their environment and, thus,
survive the impact of change and random events. Complexity Theory thinking has been
present in strategic and organisational studies since the first inception of Complex
Adaptive Systems (CAS) as an academic discipline.
• Complex Adaptive Systems are further contrasted compared with other ordered and
chaotic systems by the relationship that exists between the system and the agents and
catalysts of change which act upon it. In an ordered system the level of constraint means
that all agent behaviour is limited to the rules of the system. In a chaotic system these
agents are unconstrained and are capable of random events, uncertainty and disruption.
In a CAS, both the system and the agents co-evolve together; the system acting to
lightly constrain the agents behaviour - the agents of change, however, modify the
system by their interaction. CAS approaches to behavioural science seek to understand
both the nature of system constraints and change agent interactions and generally takes
an evolutionary or naturalistic approach to crowd scenario planning and impact analysis.

• In a crowd of human beings or a swarm of animals, individuals are so closely
connected that they share the same mood and emotions (fear, greed, rage)
and demonstrate the same or very similar behaviour (fight, flee or feeding
frenzy). Only the first few individuals exposed to the Causal Event or incident
may at first respond strongly and directly to the initial “trigger” stimulus,
causal event or incident (opportunity or threat – such as external predation,
aggression or discovery of a novel or unexpected opportunity to satisfy a
basic need – such as feeding, reproduction or territorialism).
• Those individuals who have been directly exposed to the initial “trigger” event
or incident - the system input or causal event that initiated a specific outbreak
of behaviour in a crowd or swarm – quickly communicate and propagate their
swarm response mechanism and share with all the other individuals – those
members of the Crowd immediately next to them – so that modified Crowd
behaviour quickly spreads from the periphery or edge of the Crowd.
• Peripheral Crowd members in turn adopt the Crowd response behaviour
without having been directly exposed to the “trigger”. Most members of the
crowd or swarm may be totally oblivious as to the initial source or nature of
the trigger stimulus - nonetheless, the common Crowd behaviour response
quickly spreads to all of the individuals in or around that crowd or swarm.

• There are many kinds of stochastic or random processes that impacts on every area of
Nature and Human Activity. Randomness can be found in Science and Technology and in
Humanities and the Arts. Random events are taking place almost everywhere we look – for
example from Complex Systems and Chaos Theory to Cosmology and the distribution and
flow of energy and matter in the Universe, from Brownian motion and quantum theory to
Fractal Branching and linear transformations. Further examples include Random Events,
Weak Signals and Wild Cards occurring in each aspect of Nature and Human Activity – from
Ecology and the Environment to Weather Systems and Climatology in Economics and in the
Biological basis of Behaviour. And then there are the examples of atmospheric turbulence,
and the complex orbital and solar interaction cycles – and much, much more than this.....
• There is an interesting phenomenon called Phase Locking where two loosely coupled
systems with slightly different frequencies show a tendency to move into resonance – in order
to harmonise with one another. We also know that the opposite of system convergence -
system divergence - is also possible with phase-locked systems, which can also diverge with
only very tiny inputs - especially if we run those systems in reverse. Thus phase locking
draws two nearly harmonic systems into resonance and gives us the appearance of a
“coincidence”. There are, however, no coincidences in Physics. Sensitive Dependence in
Complexity Theory also tells us that minute, imperceptible changes to inputs at the initial state
of a system, at the beginning of a cycle, are sufficient to dramatically alter the final state after
even only a few iterations of the system cycle.

Wave-form Analytics in Economic Cycles
• A fundamental challenge found everywhere in business cycle theory is how to interpret very large
scale / long period compound-wave (polyphonic) time series data sets which are dynamic (non-
stationary) in nature. Wave-form Analytics is a new analytical too based on Time-frequency
analysis – a technique which exploits the wave frequency and time symmetry principle. Trend-
cycle decomposition is a critical technique for testing the validity of multiple compound dynamic
wave-form models in a complex array of interacting and inter-dependant cyclic systems. In order
to study complex periodic economic phenomena there are a number of competing analytic
paradigms – which are driven by either deterministic methods (goal-seeking - testing the validity
of a range of explicit / pre-determined / pre-selected cycle periodicity value) and stochastic
(random / probabilistic / implicit - testing every possible wave periodicity value - or by identifying
actual wave periodicity values from the “noise” – harmonic resonance and interference patterns).
• The Wigner-Gabor-Qian (WGQ) spectrogram demonstrates a distinct capability for revealing
complex cycles within dynamic, noisy and unstable time-series data sets. A variety of competing
deterministic and stochastic methods, including the first difference (FD) and Hodrick-Prescott (HP)
filter - may be deployed with the multiple-frequency mixed case of overlaid cycles and system
noise. The FD filter does not produce a clear picture of business cycles – however, the HP filter
provides us with strong results for pattern recognition of multiple co-impacting business cycles.
The existence of stable characteristic frequencies in large economic data aggregations (“Big Data”)
provides us with strong evidence and valuable information about the structure of Business Cycles.

Wave-form Analytics in Economic Cycles
• Economic systems demonstrate Complex Adaptive System (CAS) behaviour -
more similar to an organism than to Chaotic Disorderly System “Random Walks”.
The remarkable adaptability, stability and resilience of market economies may be
demonstrated by the impact of Black Swan Events causing stock market crashes
- such as oil price shocks (1970-72) and credit supply shocks (1927- 1929 and
2008 onwards).
• Unexpected and surprising Cycle Pattern changes have occurred during wars,
and technology innovation-driven arms races - and also during the Reagan
Republican administration (why?). Just as advances in electron microscopy
revolutionised biology - non-stationary time series wave-form analysis has
opened up a new space for business cycle studies and economic policy
diagnostics.
• The role of time scale and preferred reference from economic observation are
fundamental constraints for Friedman's rational arbitrageurs and will be re[-
examined from the viewpoint of information ambiguity and dynamic instability.

• Every Disruptive Futurist is seeking to discover a combination, sequence or
chain of events which occur together in clusters, and when acting together
demonstrate transient or instantiated dependencies (are interacting or co-
related) – that is, they are impacting upon each other in some way or
another.. Basically, we need to apply systems thinking to resolving Future
Domain problems, opportunities, threats, issues or challenges.
• What factors or forces do we need to consider as being in-scope and
critical to the behaviour of the system? Which other factors or forces have
we ignored, overlooked or not considered? What further unknown factors
or unseen forces are there which we have not detected – but may still
exist – which are somehow exerting influence over the behaviour of the
system - thus impacting on system elements through Space and Time?

Multiple Random processes also occur in clusters.....
• Random Processes (with the notable exception of Quantum Events) are
never truly nor completely random nor symmetrical – they are triggered by the
manifestation of “unseen forces”. It is the nature of Random Processes –
which tend to occur together in multiple, related and similar sequences – to
generate rare Chaotic Events – which have a tendency to occur in clusters.
• At the local level, we see stochastic processes at work when we experience
the myriad of phenomena that make up our everyday life experiences. Almost
without exception, we hear of events by type occurring close together in
temporal and spatial proximity. The saying that bad or good news comes in
groups has some validity based upon the nature of event clustering. Plane,
train or bus crashes come in groups spaced close together in time, separated
by long periods of no such events.

The Butterfly Effect
• Weather prediction is an extremely difficult problem. Meteorologists can predict
the weather for short periods of time, a couple days at most, but beyond that
predictions are generally poor.
• Edward Lorenz was a mathematician and meteorologist at the Massachusetts
Institute of Technology who loved the study of weather. With the advent of
computers, Lorenz saw the chance to combine mathematics and meteorology. He
set out to construct a mathematical model of the weather, namely a set of
differential equations that represented changes in temperature, pressure, wind
velocity, etc. In the end, Lorenz stripped the weather down to a crude model
containing a set of 12 differential equations.
• On a particular day in the winter of 1961, Lorenz wanted to re-examine a sequence
of data coming from his model. Instead of restarting the entire run, he decided to
save time and restart the run from somewhere in the middle. Using data printouts,
he entered the conditions at some point near the middle of the previous run, and
re-started the model calculation. What he found was very unusual and
unexpected. The data from the second run should have exactly matched the data
from the first run. While they matched at first, the runs eventually began to
diverge dramatically — the second run losing all resemblance to the first within a
few "model" months. A sample of the data from his two runs in shown below:

• At first Lorenz thought that a vacuum tube had gone bad in his computer, a Royal
McBee — an extremely slow and crude machine by today's standards. After
discovering that there was no malfunction, Lorenz finally found the source of the
problem. To save space, his printouts only showed three digits while the data in the
computer's memory contained six digits. Lorenz had entered the rounded-off data
from the printouts assuming that the difference was inconsequential. For example,
even today temperature is not routinely measured within one part in a thousand.
• This led Lorenz to conclude that detailed long-term weather forecasting was doomed.
His simple model exhibits the phenomenon known as "sensitive dependence on
initial conditions." This is sometimes referred to as the Butterfly Effect, e.g. a
butterfly flapping its wings in South America can affect the weather in Central Park.
• The question then arises — why does a set of completely deterministic equations
exhibit this chaotic behaviour? After all, scientists are often taught that small initial
perturbations lead to small changes in model behaviour. This was clearly not the
case in Lorenz's model of the weather – where small initial perturbations in
temperature lead to massive changes in weather model behaviour. The answer lies in
the nature of the equations; they were nonlinear equations. While they are difficult
to solve, nonlinear systems are central to chaos theory and often exhibit fantastically
complex and chaotic behaviour.

• Extremes in weather follow a similar stochastic pattern. Everyone is familiar
with the expression "When it rains, it pours”.....
• Lorenz's initial weather model exhibited chaotic behaviour, which involved a set
of 12 nonlinear differential equations. Lorenz decided to look for complex
behaviour in an even simpler set of equations, and was led to the phenomenon
of rolling fluid convection. The physical model is simple: place a gas in a solid
rectangular box with a heat source on the bottom.
• Lorenz then simplified several fluid dynamics equations (called the Navier-
Stokes equations) and from the original twelve nonlinear equations ended up
with a simplified set of just three nonlinear equations: -
• Where P is the Pr and tl number representing the ratio of the fluid viscosity to
its thermal conductivity, R represents the difference in temperature between the
top and bottom of the system, and B is the ratio of the width to height of the box
used to hold the system. The values Lorenz used are P = 10, R = 28, B = 8/3.

• Sensitive Dependence in Complexity Theory tells us that minute, imperceptible changes
to a system – at the beginning of a cycle, or dynamic forces acting as the cycle evolves -
are sufficient to dramatically alter the final state of the system - even after a relatively few
iterations of the system cycle.. Changes to a system at the initial state constitutes Initial
Sensitive Dependence, whilst dynamic external forces acting on the system as the cycle
evolves constitutes Dynamic Sensitive Dependence. Thus Attractors and Repellents
are examples of Dynamic Sensitive Dependence.
• Any trajectory of the dynamic system in the attractor does not have to satisfy any special
constraints - except for remaining as an attractor. The trajectory may be periodic or
chaotic. In a set of periodic or chaotic points, if the average flow in the neighbourhood is
generally towards the set, then it is an attractor. If the average neighbourhood flow is
generally away from the set, then that set is instead referred to as a repellent (repellor)..

Strange Attractor
• A Strange Attractor has a fractal dimensional structure. This is often the case when
the system dynamics are chaotic. Strange attractors that are non-chaotic may also
exist. The term Strange Attractor was coined by David Ruelle and Floris Takens to
describe the attractor resulting from a series of bifurcations in a system modelling the
heat convection dynamics of a fluid heated from below and cooled at the top – this
process drives Plate Tectonics in the Earths mantle – causing Continental Drift.
• Strange attractors are often differentiable in a few directions, but some are like
Cantor dust, and are therefore not differentiable. Strange attractors may also be
found in presence of noise - where they may be shown to support invariant Random
Probability measures of Sinai-Ruelle-Bowen type - see Chekroun et al. (2011).
• A Strange Attractor is an attracting set that has zero measure in the embedding
phase space and has fractal dimensions. Trajectories within a strange attractor
appear to skip around randomly. On the surface these three equations seem
relatively simple to solve. However, they represent an extremely complicated and
variable dynamic system. If the results are plotted in three dimensions, then the
following three-dimensional figure, called the Lorenz attractor, is obtained: -

• Phase Divergence drives two phase-locked harmonic systems out of
synchronisation into random, chaotic and discordant behaviour - where phase
locked systems can diverge from each other with only very tiny inputs (especially
when we run those phase-locked harmonic system models in reverse).....
• Due to the fact that very complex systems are invoked, it is safe to say that pure
coincidence is a vanishingly small reality. In fact, it is safe to say that phenomena
such as coincidence (which is more properly called serendipity)– as seen drawing
two interacting bodies into perfect resonance - is due to unknown factors or unseen
forces behind effects such as phase locking, and sensitive dependence. Sensitive
dependence and the interaction of every object upon all of the rest accounts for the
phenomenon of clustering – not serendipity, coincidence or mere chance.....
• The structure of the universe is based on such stochastic events. Here too, we find
random clustering events. The distribution of matter in the universe is based on the
quantum foundation. Clustering at the quantum level when the universe was just a
few thousands of a millimetre across – has lead to the creation of the super massive
black holes at the centre of each galaxy which, through gravitational attraction drive
the clustering of star / planetary systems, star clusters, galaxies and galactic clusters.

• There is another interesting phenomenon called Phase Locking where two
loosely coupled systems with slightly different frequencies show a tendency to
move into resonance – they are seeking to harmonise with one another. We also
know that the opposite of system convergence - system divergence - is also
possible with phase-locked systems, Sensitive dependence also tells us that very
tiny inputs are enough to completely alter the final state after several iterations of
the dynamic. We thus know of systems that diverge with only very tiny inputs, but
the opposite is also true with convergence, especially if we run things in reverse.
• Thus phase locking draws two nearly harmonic systems into resonance and gives
us the appearance of a “coincidence”. There are, however, no coincidences in
nature or Physics - all random processes (with the notable exception of Quantum
Events) – are neither truly random nor completely symmetrical – but are simply the
outcome of unseen forces acting on the system. Such 'coincidences' are like the
clusters of personality types that are governed by certain recurring planets -
according to the statistical researches of M. Guaquelin.

Random Event Clustering –
Patterns in the Chaos.....
• Even when we look to the formation of solar systems, we see evolution
mediated by forces, random events and harmonics in synchronicity. Since
random events tend to cluster as part of the natural evolution of the cosmos, it is
not surprising to find that complex systems will evolve as a natural
consequence. Planets in orbit around a star must have orbital periods in
dissonance to each other in order to have reasonable stability.
• This dissonance will evolve through time to create patterns that occur randomly
in time where planets cluster along one line of sight or another. Such is the
nature of great stelliums. In our solar system, this kind of thing occurs roughly
once very forty years, but no two stelliums are alike in planetary grouping, angle
of spread or where they are in reference to the background stars. Since planets
orbit in more or less well-defined periods, these events are highly predictable,
unlike the events in the quantum realm or with coin tosses in a chained
sequence of events.

Random Event Clustering –
Patterns in the Chaos.....
• The long horizon of predictability of planetary alignments can allow us to
determine when events associated with the planets will tend to cluster as well.
Planetary clustering in an non-periodic fashion will generate non-periodic
effects, each object impacting upon all others. Earth is not exempt from the
forces of these planets. They manifest in many ways, obvious and subtle. Some
are easy to understand, others are not.
• We can calculate the perturbation and tidal influences with some ease and
match these with real effects we experience. The psychic influences are much
harder to track. They are there nonetheless as evidenced by the lunar and solar
influences. The stochastic and clustering nature of these influences is what is
behind the seeming stochastic and clustering nature of events we experience.

SIX VISIONS OF THE FUTURE –
THE ELTVILLE MODEL
There are six viewpoints or lenses from which we may understand the future: -
1. BLUE lenses are for PROBABLISTIC FUTURE – RATIONAL FUTURISTS
2. RED lenses are for FUTURE THREATS – DISRUPTIVE FUTURISTS
3. GREEN lenses are for FUTURE OPPORTUNISTIIES – EVOLUTIONARY FUTURISTS
4. GOLD lenses are for DESIRED FUTURE VISION – GOAL ANALYSTS
5. INDIGO lenses are for STEADY STATE FUTURES – EXTRAPOLATION / PATTERN ANALYSTS
6. The VIOLET lenses are for a DETERMINISITC FUTURE – STRATEGIC POSITIVISTS

• Many of the issues that we encounter in Future Management Studies – from driving
Private-sector strategic management to formulating Government Political, Economic and
Social Policies - result from attempts to integrate multiple viewpoints from different
people. Everybody subconsciously believes that other people thinks about, articulates and
understands the Future Narrative in exactly the same way as they do. Stakeholders often
tend to assume that everyone else is looking through the same ”futures lenses” - which
may lead to misunderstanding, conflict, frustration or failure.
• The Eltville Model consists of a process model that describes six different viewpoints or
perspectives of the future (the “six futures lenses") - as a sequence of mental steps (for
exploration and discovery in a workshop) environment, and a results model, which
captures the results achieved in the process as "thought objects“.
The SIX futures lenses below make it easier to analyse and understand the future: -
5. INDIGO lenses are for STEADY STATE FUTURE – EXTRAPOLATION and PATTERN ANALYSTS
6. VIOLET lenses are for DETERMINISITC FUTURE – STRATEGIC POSITIVISTS
THE ELTVILLE MODEL by Pero Mićić

• The Eltville Model serves as a holistic "cognitive map" for terms such as scenario,
vision, trend, wild card, assumption etc, - which may frequently be used in varying
context in different ways by diverse stakeholders. The terms used in the Eltville
Model are unambiguously defined and semantically related to each other - and are
further based on wide futures phenomenological analysis,.
– The ELTVILLE MODEL helps us all to structure our future scenarios and thoughts
about future outcomes to formulate future strategy in a coherent way without omitting
any important determining factors or neglecting any essential viewpoints.
– The ELTVILLE MODEL helps us to obtain some clarity on the most important Future
Management outcomes, goals and objectives and communicate in a clear narrative
about the future of our market and our companies place in that market.
– The ELTVILLE MODEL guides us to implement Strategy Analysis and Future
Management methods and tools in the areas where they are most effective.
• The Eltville Model is a result of observation and phenomenological analysis of more
than 800 workshops with management teams. It was developed by Pero Mićić and is
now being developed further by the Future Management Group consultants

• The SIX futures lenses and the resulting "ELTVILLE MODEL" bridges the gap between
strategic management and corporate planning and futures studies - research for creating a
better everyday way of life .
• Using phenomenon-based scenario planning and impact analysis, the ELTVILLE FUTURE
MANAGEMENT! MODEL is proven in more than a thousand projects. Future
Management Group have defined the essential meaning of Future Management terms and
their key application to deliver a cognitive model and a cognitive map from them.
• The ELTVILLE MODEL helps us all to apply the common Strategy Analysis and Strategic
Foresight tools much more effectively within a comprehensive Futures Framework. This
model also provides participants with a road map for thinking and communicating about
the future with your stakeholders and an integrated future-oriented structure for managing
strategy delivery projects.
5. INDIGO lenses are for STEADY STATE FUTURE – EXTRAPOLATION and PATTERN ANALYSTS

• The Eltville Model of Future Management is used by companies and public institutions to
support thinking and communicating about future environmental changes, the early
recognition of future markets, the development of future strategies and the building up of
future competence with a sound system of terms. The Eltville Model provides a
comprehensive and integrated terminology. It links the requirements on scientific future
management with the necessities of a company’s day-to-day business.
• The ELTVILLE MODEL has been developed through futures research in more than a
thousand workshops and projects with governmental and non-profit organizations – as well
as with major corporations around the world, - including BOSCH, Microsoft, BAYER,
AstraZeneca, Roche, Ernst+Young, Ford, Vodafone, EADS and Nestle.
5. INDIGO lenses are for STEADY STATE FUTURE – EXTRAPOLATION / PATTERN ANALYSTS

The Eltville Model – Rational Futurism
1. The ELTVILLE MODEL BLUE lenses are for a PROBABLISTIC FUTURE – RATIONAL
FUTURISM – Rational Futurists believe that the future is, to a large extent, both unknown
and unknowable. Reality is non-liner – that is, chaotic – and therefore it is impossible to
predict the future. With chaos comes the potential for disruption. Possible and Alternative
Futures emerge from the interaction of chaos and uncertainty amongst the interplay of
current trends and emerging factors of change – presenting an inexorable mixture of
challenges and opportunities.
• Probable future outcomes and events may be synthesised and implied via an intuitive
assimilation and cognitive filtering of Weak Signals, inexorable trends, random and chaotic
actions and disruptive Wild Card and Black Swan events. Just as the future remains
uncertain, indeterminate and unpredictable, so it will be volatile and enigmatic – but it may
also be subject to synthesis by man.....
The Probabilistic Future – Synthesis: -
– Rational Futurism
– Weak Signals and Wild Cards
– Cognitive Filtering and Intuitive Assimilation
– Nominal Group Conferences and Delphi Surveys
– Horizon Scanning, Tracking and Monitoring for emerging catalysts of Global Change

The Eltville Model – Disruptive Futurism
2. The ELTVILLE MODEL RED lenses are for FUTURE THREATS – DISRUPTIVE
FUTURISM – Disruptive Futurism is an ongoing forward analysis of the impact of new and
emerging factors of Disruptive Change on Environmental, Political, Economic, Social,
Industrial, Agronomy and Technology and how Disruptive Change is driving Business and
Technology Innovation. Understanding how current patterns, trends and extrapolations
along with emerging agents and catalysts of change interact with chaos, disruption and
uncertainty (Random Events) - to create novel opportunities – as well as posing clear and
present dangers that threaten the status quo of the world as we know it today.....
• The purpose of the “Disruptive Futurist” role is to provide future analysis and strategic
direction to support senior client stakeholders who are charged by their organisations with
thinking about the future. This involves enabling clients to anticipate, prepare for and
manage the future by helping them to understanding how the future might unfold - thus
realising the Stakeholder Strategic Vision and Communications / Benefits Realisation
Strategies. This is achieved by scoping, influencing and shaping client organisational
change and driving technology innovation to enable rapid business transformation.
• Future Threats and Chaos – Disruptive Futurism -
– Risk Management
– Disruptive Change
– Weak Signals and Wild cards
– Black Swan (Random) Events
– Horizon Scanning, Monitoring and Tracking for Weak Signals

The Eltville Model – Evolutionary Futurism
3. In the ELTVILLE MODEL GREEN lenses represent FUTURE OPPORTUNISTIIES –
EVOLUTIONARY FUTURISM – Evolutionists believe that the geological, ecological and
climatic systems interact with human activity to behave as a self-regulating collection of
loosely coupled forces and systems – the Gaia Theory. Global Massive Change is
driven by climatic, geological, biosphere, anthropologic and geo-political systems
dominate at the macro-level – and at the micro-level local weather, ecology and
environmental, social and economic sub-systems prevail.
4. The future will evolve from a series of actions and events which emerge, unfold and
develop – and then plateau, decline and collapse. These actions and events are
essentially natural responses to human impact on ecological and environmental support
systems - creating massive global change through population growth, environmental
degradation and scarcity of natural resources. Over the long term, global stability and
sustainability of those systems will be preserved – at the expense of world-wide human
population levels.
• The Creatable Future – Opportunities: -
– Complex Adaptive Systems (CAS)
– Evolution - Opportunities and Adaptation
– Geological Cycles and Biological Systems
– Social Anthropology and Human Behaviour
– Global Massive Change and Human Impact
– Climatic Studies and Environmental Science
– Population Curves and Growth Limit Analysis

The Eltville Model – Goal Analysts
4. In the ELTVILLE MODEL GOLD lenses stand for our PREFERED and DESIRED
FUTURE VISION – GOAL ANALYSTS believe that the future will be governed by the
orchestrated vision, beliefs, goals and objectives of various influential and well
connected Global Leaders, working with other stakeholders - movers, shakers and
influencers such as the good and the great in Industry, Economics, Politics and
Government, along with other well integrated and highly coordinated individuals from
Academia, Media and Society in general – and realised through the plans and actions
of global and influential organizations, institutions and groups to which they belong.
• The shape of the future may thus be discerned by Goal Analysis and interpretation of
the policies, behaviours and actions of such individuals and of those groups to which
they subscribe and belong.
The Preferred Future – Vision: -
– Goal Analysis
– Causal Layer Analysis (CLA)
– Value Models and Roadmaps
– Political Science and Policy Studies
– Religious Studies and Future Beliefs
– Peace and Conflict Studies, Military Science
– Leadership Studies and Stakeholder Analysis

The Eltville Model – Extrapolation Analysis
5. In the ELTVILLE MODEL – INDIGO lenses are for EXTRAPOLATION – PATTERN and TREND
ANALYSIS. Extrapolation, Pattern and Trend Analysts believe that the past is the key to the
future-present. The future-present is therefore just a logical extrapolation, extension and
continuum of past events, carried foreword on historic waves, cycles, patterns and trends.....
• Throughout eternity, all that is of like form comes around again – everything that is the
same must return again in its own everlasting cycle.....
• Marcus Aurelius – Emperor of Rome •
• As the future-present develops and unfolds – it does so as a continuum of time past, time
present and time future – and so eternally perpetuating the eternally unfolding, extension,
replication and preservation of those historic cycles, patterns and trends that have shaped and
influenced actions and events throughout time.
The Probable Future – Assumptions: -
– Patent and Content Analysis
– Causal Layer Analysis (CLA)
– Fisher-Pry and Gompertz Analysis
– Pattern Analysis and Extrapolation
– Technology and Precursor Trend Analysis
– Morphological Matrices and Analogy Analysis

The Eltville Model - Strategic Positivism
6. The ELTVILLE MODEL VIOLET lenses are for STRATEGIC POSITIVISM – STRATEGIC
POSITIVISTS are deterministic, optimistic and somewhat Utopian in nature – they believe
that their future outcomes, goals and objectives can be determined using Strategic Foresight
and the future designed via Future Management – strategy planning, and delivery through
the action link – to be delivered through Business Transformation – organisational change,
process improvement and technology refreshment – so that their desired future becomes
both realistic and achievable.
• The future may develop and unfold so as to comply with our positive vision of an ideal future
– and thus fulfil all of our desired outcomes, goals and objectives – in order that the planned
future becomes attainable and our preferred future options may ultimately be realised.
• The Planned Future – Strategy: -
– Linear Systems and Game Theory
– Scenario Planning and Impact Analysis
– Future Landscape Modelling and Terrain Mapping
– Threat Assessment and Risk Management
– Economic Modelling and Financial Analysis
– Strategic Foresight and Future Management

• Scenario Planning and Impact Analysis is the archetypical method for futures studies
because it embodies the central principles of the discipline:
– It is vitally important that we think deeply and creatively about the future, or else we run
the risk of being either unprepared or surprised – or both......
– At the same time, the future is uncertain - so we must prepare for a range of multiple
possible and plausible futures, not just the one we expect to happen.
• Scenarios contain the stories of these multiple futures, from the expected to the
wildcard, in forms that are analytically coherent and imaginatively engaging. A good
scenario grabs us by the collar and says, ‘‘Take a good look at this future. This could be
your future. Are you going to be ready?’’
• As consultants and organizations have come to recognize the value of scenarios, they
have also latched onto one scenario technique – a very good one in fact – as the
default for all their scenario work. That technique is the Royal Dutch Shell/Global
Business Network (GBN) matrix approach, created by Pierre Wack in the 1970s and
popularized by Schwartz (1991) in the Art of the Long View and Van der Heijden (1996)
in Scenarios: The Art of Strategic Conversations. In fact, Millett (2003, p. 18) calls it the
‘‘gold standard of corporate scenario generation.’’

Outsights "21 Drivers for the 21st Century"
1. War, terrorism and insecurity
2. Layers of power
3. Economic and financial stability
4. BRICs and emerging powers
• Brazil
• Russia
• India
• China
5. The Five Flows of Globalisation
• Ideas
• Goods
• People
• Capital
• Services
6. Intellectual Property and Knowledge
7. Health, Wealth and Wellbeing
8. Demographics, Ethnographics and Social
Anthropology - Transhumanism
9. Population Drift, Migration and Mobility
10. Trust and Reputation
11. Human Values and Beliefs
12. History, Culture and Human Identity
13. Consumerism and the rise of the Middle
Classes
14. Networks and Social Connectivity
15. Space - the final frontier
• The Cosmology Revolution
16. Science and Technology Futures
• The Nano Revolution
• The Quantum Revolution
• The Information Revolution
• The Bio-Technology Revolution
• The Energy Revolution • Oil Shale Kerogen • Tar
Sands • Methane Hydrate • Nuclear Fusion •
17. Science and Society - Social Impact of
Technology
18. Natural Resources – availability, scarcity and
control
19. Climate Change
• Global Massive Change – the Climate Revolution
20. Environmental Degradation & Mass Extinction
21. Urbanisation

• Scenarios are specially constructed stories about the future - each one portraying
a distinct, challenging and plausible world in which we might one day live and
work - and for which we need to anticipate, plan and prepare.
• The Outsights Technique emphasises collaborative scenario building with internal
clients and stakeholders. Embedding a new way of thinking about the future in the
organisation is essential if full value is to be achieved – a fundamental principle of
the “enabling, not dictating” approach
• The Outsights Technique promotes the development and execution of purposeful
action plans so that the valuable learning experience from “outside-in” scenario
planning enables building profitable business change.
• The Outsights Technique develops scenarios at the geographical level; at the
business segment, unit and product level, and for specific threats, risks and
challenges facing organisations. Scenarios add value to organisations in many
ways: - future management, business strategy, managing change, managing risk
and communicating strategy initiatives throughout an organisation.

Seeing in Multiple Horizons: -
Connecting Strategy to the Future
• THE THREE HORIZONS MODEL describes a Strategic Foresight method called “Seeing in Multiple
Horizons: - Connecting Strategy to the Futures " The current THREE HORIZONS MODEL differs
significantly from the original version first described in management literature over a decade ago.
This model enables a range of Futures Studies techniques to be integrated with Strategy Analysis
methods in order to reveal powerful and compelling future insights – and may be deployed in various
combinations, whenever or wherever the Futures Studies techniques and Strategy Analysis methods
are deemed to support the futures domains, subjects, applications and data in the current study.
• THE THREE HORIZONS MODEL method connects the Present Timeline with deterministic (desired or
proposed) futures, and also helps us to identify probabilistic (forecast or predicted) future scenarios
which may emerge as a result of interaction between embedded present-day factors and emerging
catalysts of change – thus presenting us with a range of divergent possible futures. The “Three
Horizons” method connects to models of change developed within the “Social Shaping” Strategy
Development Framework via the Action Link to Strategy Execution. Finally, it summarises a number of
futures applications where this evolving technique has been successfully deployed.
• The new approach to “Seeing in Multiple Horizons: - Connecting Strategy to the Future” has several
unique features. It can relate change drivers and trends-based futures analysis to emerging issues. It
enables policy or strategy implications of futures to be identified – and links futures work to processes
of change. In doing so this enables Foresight to be connected to existing and proposed underlying
system domains and data structures, with different rates of change propagation impacting across
different parts of the system, and also to integrate seamlessly with tools and processes which facilitate
Strategic Analysis. This approach is especially helpful where there are complex transformations which
are likely to be radically disruptive in nature - rather than simple incremental transitions.

Andrew Curry
Henley Centre HeadlightVision
United Kingdom
Anthony Hodgson
Decision Integrity
United Kingdom
Seeing in Multiple Horizons: -
Connecting Strategy to the Future
The Three Horizons

• The way that we think about the future must mirror how the future actually
unfolds. As we have learned from recent experience, the future is not a
straightforward extrapolation of simple, single-domain trends. We now have
to consider ways in which the possibility of random, chaotic and radically
disruptive events may be factored into enterprise threat assessment and risk
management frameworks and incorporated into enterprise decision-making
structures and processes.
• Managers and organisations often aim to “stay focused” and maintain a
narrow perspective in dealing with key business issues, challenges and
targets. A concentration of focus may risk overlooking those Weak Signals
indicating potential issues and events, agents and catalysts of change. These
Weak Signals – along with their resultant Wild Cards, Black Swan Events and
global transformations - are even now taking shape at the very periphery of
corporate awareness, perception and vision – or even just beyond.
• These agents of change may precipitate global impact-level events which
either threaten the very survival of the organisation - or present novel and
unexpected opportunities for expansion and growth. The ability to include
weak signals and peripheral vision into the strategy and planning process may
therefore be critical in contributing towards the organisation's continued
growth, success, well being and survival.

Futures Studies
• Precognition and Prediction – Contemplative, mystic, meditative and even
psychic methods for pre-cognitive viewing of the future and how the future might
unfold. These future scanning activities have been recorded throughout history
(Flavius Josephus, Michel Nostradamus, Leonardo da Vinci, Jules Verne, H.G.
Wells) and are also well known within certain cultures (Central American Indians)
and government agencies (US and Soviet Military) – some techniques may also
involve the use of hypnotic or hallucinogenic states.
• Futures Studies, Foresight, or Futurology – is the science, practice and art of
postulating possible, probable, and preferable futures. Futures studies (colloquially
called "Futures" by many of the field's practitioners) seeks to understand what is
likely to continue, what is likely to change, and what is a novel, emerging pattern or
trend. Part of the discipline thus seeks a systematic and extrapolation-based
understanding of both past and present events - in order to determine the
probability and impact of future events, patterns and trends.
• Future Management– is an interdisciplinary curriculum, studying yesterday's and
today's changes, and aggregating and analyzing both lay and professional content
and strategies, beliefs and opinions, forecasts and predictions with respect to
shaping tomorrow. It includes analysing the sources, agents and causes, patterns
and trends of both change and stability in an attempt to develop foresight and to
map possible, probable and alternative futures.

Foresight
• Foresight draws on traditions of work in long-range forecasting and strategic
planning horizontal policymaking and democratic planning, horizon scanning and
futures studies (Aguillar-Milan, Ansoff, Feather, van der Hijden, Slaughter et all) -
but was also highly influenced by systemic approaches to innovation studies,
global design, massive change, science and technology futures, economic, social
and demographic policy, fashion and design - and the analysis of "weak signals"
and "wild cards", "future trends“ "critical technologies“ and “cultural evolution".
– The longer-term - futures that are usually at least 10 years away (though
there are some exceptions to this, especially in its use in private business).
Since Foresight is an action-oriented discipline (via the planning link) it will
rarely be applied to perspectives beyond a few decades out. Where major
infrastructure decisions such as petrology reservoir exploitation, aircraft design,
power station construction, transport hubs and town master planning decisions
are concerned - then the planning horizon may well be half a century.
– Alternative futures: it is helpful to examine alternative paths of development,
not just what is currently believed to be most likely or business as usual. Often
Foresight will construct multiple scenarios. These may be an interim step on
the way to creating what may be known as positive visions, success scenarios
or aspirational futures. Sometimes alternative scenarios will be a major part of
the output of a Foresight study, with the decision about what preferred future to
build being left to other mechanisms (Planning and Strategy).

Strategic Foresight
• Strategic Foresight is the ability to create and maintain a high-quality, coherent
and functional forward view, and to use the insights arising in useful organisational
ways. For example to detect adverse conditions, guide policy, shape strategy, and
to explore new markets, products and services. It represents a fusion of futures
methods with those of strategic management (Slaughter (1999), p.287).
– Probabilistic Futures (Rational Futurism) – assessing possible, probable
and alternative futures – selecting those futures offering conditions that best fit
our strategic goals and objectives for achieving a preferred and desired future.
Filtering for a more detailed analysis may be achieved by discounting isolated
outliers and focusing upon those closely clustered future descriptions which
best support our desired future outcomes, goals and objectives.
• Strategy Visioning – Future outcomes, goals and objectives are defined via
Strategic Foresight and are determined by design, planning and management - so
that the future becomes realistic and achievable. Possible futures may comply with
our preferred options - and therefore our vision of an ideal future and desired
outcomes could thus be fulfilled.
– Deterministic Futurism (Strategic Positivism) – articulating a single,
preferred vision of the future. The future will conform to our preferred options -
thus our vision of an ideal future and desired outcomes will be fulfilled.

• INNOVATION •
• Technology Innovation is simply combining existing resources in new and different ways –
in order to create novel and innovative Products and Services. Understanding the impact
of Technology Convergence is the Key to driving Innovation. Many common and familiar
objects in use today exist only as a result of technology convergence - your average,
everyday passenger vehicle or laptop computer is the culmination of a series of technology
consolidation and integration events of a large number of apparently separate, unrelated
technological innovations and advancements. Light-weight batteries were developed to
provide independence from fixed power sockets and hard-disk drives were made compact
enough to be installed in portable devices. Then the smart phone and tablet resulted from
a further convergence of technologies such as cellular telecommunications, mobile
internet, and Smart Apps - mini-applications that do not need an on-board hard-disk drive.
• FUTURE MANAGEMENT •
• Providing future analysis and strategic advice to stakeholders so that they might
understanding how the Future may unfold - in order to anticipate, prepare for and manage
the Future, to resolve challenging business problems, to envision, architect, design and
deliver novel solutions in support of major technology refreshment and business
transformation programmes • Future Analysis • Innovation • Strategic Planning •
Business Transformation • Technology Refreshment •

• GIS MAPPING and SPATIAL DATA ANALYSIS •
• A Geographic Information System (GIS) integrates hardware, software, and data
capture devices for acquiring, managing, analysing, distributing and displaying all
forms of geographically dependant location data – including machine generated
data such as Computer-aided Design (CAD) data from land and building surveys,
Global Positioning System (GPS) terrestrial location data - as well as all kinds of
aerial and satellite image data.
• Spatial Data Analysis is a set of techniques for analysing spatial (Geographic)
location data. The results of spatial analysis are dependent on the locations of the
objects being analysed. Software that implements spatial analysis techniques
requires access to both the locations of objects and their physical attributes. Spatial
statistics extends traditional statistics to support the analysis of geographic data.
Spatial Data Analysis provides techniques to describe the distribution of data in the
geographic space (descriptive spatial statistics), analyse the spatial patterns of the
data (spatial pattern or cluster analysis), identify and measure spatial relationships
(spatial regression), and create a surface from sampled data (spatial interpolation,
usually categorized as geo-statistics).

Thinking about the Future Framework

Thinking about the Future Framework
Professors Peter Bishop and Andy Hines at the
University of Texas Futures Studies School at the
Houston Clear Lake site have developed a
definitive Strategic Management Framework –
Thinking About the Future

Professors Peter Bishop and Andy Hines at the University of Texas Futures
Studies School at the Houston Clear Lake site have developed a definitive
Strategic Foresight Framework –
Thinking About the Future
1. FRAMING AND SCOPING •
• This important first step enables organizations to define the purpose. focus, scope and
boundaries of the Political, Legal, Economic, Cultural, Business and Technology problem
/ opportunity domains requiring resolution. Taking time at the outset of a project, the
Strategic Foresight Team defines the Futures Study domain, outlines the required
outcomes, goals and objectives and determines how best to achieve them. •
• Strategy Study Definition – Problem / Opportunity Domains: -
– Definition - Focus, Scope, Purpose and Boundaries
– Approach - What – How – Why – Who – When – Where?
– Justification - Cost, Duration and Resources v. Future Benefits and Cash Flows

2. ENGAGING •
• This second phase is about stakeholder management - developing action agendas
for mobilising the Programme and opening stakeholders communications
channels, soliciting collaborative participation and input.
• This may involve staging a wide range of Programme kick-off Events , organising
Stakeholder Strategy Communications, Target-setting and Action Planning,
establishing mechanisms for reporting actual achievement against targets – in
order that the Strategic Foresight Team engage a wide range of stakeholders,
presents a future-oriented, customer-focussed approach and enables the efficient
delivery of Strategy Study artefacts & benefits in planned / managed work streams.
•
• Strategy Study Mobilisation – Stakeholder Engagement: -
– Communication Strategy
– Benefits Realisation Strategy
– Strategy Study Programme Plan
– Stakeholder, SME and TDA Strategy Study Launch Events

3. RESEARCH – HORIZON SCANNING, MONITORING AND TRACKING: •
• Once the Strategic Foresight Team is clear about the engagement boundaries,
purpose, problem / opportunity domains and scope of a Strategy Study - they can
begin to scan both internal and external environments for all relevant input content –
information and data describing extrapolations, patterns and trends – or indicating
global transformations, emerging and developing factors and catalysts of change –
and to search for, seek out and identify any Weak Signals indicating the potential for
disruptive Wild Card or Black Swan events. •
• Strategy Investigation – Content Capture: -
– Factors and Catalysts of Change
– Extrapolations, Patterns and Trends
– Internal and External Content, Information and Data
– Horizon Scanning, Monitoring and Tracking Systems and Infrastructure

4. STRATEGY DISCOVERY – STAKEHOLDER EVENTS & STRATEGY THEMES •
• Here we begin to identify and extract useful information from the mass of Research
Content that we have collected. Critical Success Factors, Strategy Themes and Value
Propositions begin to emerge from Data Set “mashing”, Data Mining and Analytics
against the massed Research Data – and all supplemented via the very human
process of Cognitive Filtering and Intuitive Assimilation of selected information -
through Discovery Workshops, Strategy Theme Forums, Value Chain Seminars,
Special Interest Group Events and one-to-one Key Stakeholder Interviews. •
• Strategy Discovery – Content Analysis: -
– Data Set “mashing”, Data Mining and Analytics
– Stakeholder, SME and TDA Strategy Discovery Events
– Discovered Assumptions, Critical Success Factors, Strategy Themes and Value
Propositions

5. STRATEGIC RISK MANAGEMENT •
• The underlying premise of Strategic Risk Management is that every enterprise exists
to provide value for its stakeholders. All entities face uncertainty and the possibility of
chaos and disruption. Risk Management is the evaluation of uncertainty. The
challenge is to determine how much risk we are able to accept as we strive to grow
stakeholder value. Uncertainty presents both opportunity and risk with the possibility
of either erosion or enhancement of value. Strategic Foresight enables stakeholders to
deal effectively with uncertainty and associated risk and opportunity - thus enhancing
the capability of the Enterprise to build long-term value. •
• Risk Management – Value Chain Building: -
– Risk Research and Identification
– Uncertainty, Chaos and Disruption
– Identified Assumptions, Critical Success Factors, Strategy Themes and Value
Propositions

Strategic Risk Management
• Systemic Risk (external threats)
– Political Risk – Political Science, Futures Studies and Strategic Foresight
– Economic Risk – Fiscal Policy, Economic Analysis, Modelling and Forecasting
– Wild Card Events – Horizon Scanning, Tracking and Monitoring – Weak Signals
– Black Swan Events – Scenario Planning & Impact Analysis – Future Management
• Market Risk (macro-economic threats)
– Equity Risk – Traded Instrument Product Analysis and Financial Management
– Currency Risk – FX Curves and Forecasting
– Commodity Risk – Price Curves and Forecasting
– Interest Rate Risk – Interest Rate Curves and Forecasting
• Trade Risk (micro-economic threats)
– Credit Risk – Debtor Analysis and Management
– Liquidity Risk – Solvency Analysis and Management
– Insurance Risk – Underwriting Due Diligence and Compliance
– Counter-Party Risk – Counter-Party Analysis and Management

• Operational Risk (internal threats)
– Legal Risk – Contractual Due Diligence and Compliance
– Statutory Risk – Legislative Due Diligence and Compliance
– Regulatory Risk – Regulatory Due Diligence and Compliance
– Competitor Risk – Competitor Analysis, Defection Detection / Churn Management
– Reputational Risk – Internet Content Scanning, Intervention / Threat Management
– Corporate Responsibility – Enterprise Governance, Reporting and Controls
– Digital Communications and Technology Risk
• Security Risk – Security Principles, Policies and Architecture
• Process Risk – Business Strategy and Architecture
• Information Risk – Information Strategy and Architecture
• Technology Risk – Technology Strategy and Architecture
• Stakeholder Risk – Benefits Realisation Strategy and Communications Management
• Vendor / 3rd Party Risk – Strategic Vendor Analysis and Supply Chain Management

6. THREAT ANALYSIS •
• In most organizations, many stakeholders, if unchallenged, tend to believe that threat
scenarios - as discovered in various SWOT / PEST Analyses - are going to play out
pretty much the same way as they have always done so in the past. When the Strategic
Foresight Team probes an organization’s view of the future, they usually discover an
array of unexamined, unexplained assumptions tending to either maintain the current
status quo – or converging around discrete clusters of small, linear, incremental future
changes •
• Threat Analysis – Value Chain Analysis: -
– Threat Analysis, Assessment and Prioritisation
– Global Transformations, Factors and Catalysts of Change
– Analysed Assumptions, Critical Success Factors, Strategy Themes and Value
Propositions

Risk Management
• Risk Management is a structured approach to managing uncertainty through
foresight and planning. A risk is related to a specific threat (or group of related
threats) managed through a sequence of activities using various resources: -
Risk Research – Risk Identification – Scenario Planning & Impact Analysis – Risk
Assessment – Risk Prioritization – Risk Management Strategies – Risk Planning –
Risk Mitigation
• Risk Management strategies may include: -
– Transferring the risk to another party
– Avoiding the risk
– Reducing the negative effect of the risk
– Accepting part or all of the consequences of a particular risk .
• For any given set of Risk Management Scenarios, a prioritization process
ranks those risks with the greatest potential loss and the greatest probability of
occurrence to be handled first – and those risks with a lower probability of
occurrence and lower consequential losses are then handled subsequently in
descending order of impact. In practice this prioritization can be challenging.
Comparing and balancing the overall threat of risks with a high probability of
occurrence but lower loss -versus risks with higher potential loss but lower
probability of occurrence -can often be misleading.

Risk Management
• Scenario Panning and Impact Analysis: - In any Opportunity / Threat Assessment
Scenario, a prioritization process ranks those risks with the greatest potential loss and the
greatest probability of occurring to be handled first - subsequent risks with lower probability
of occurrence and lower consequential losses are then handled in descending order. As a
foresight concept, Wild Card or Black Swan events refer to those events which have a
low probability of occurrence - but an inordinately high impact when they do occur.
– Risk Assessment and Horizon Scanning have become key tools in policy making
and strategic planning for many governments and global enterprises. We are now
moving into a period of time impacted by unprecedented and accelerating
transformation by rapidly evolving catalysts and agents of change in a world of
increasingly uncertain, complex and interwoven global events.
– Scenario Planning and Impact Analysis have served us well as a strategic planning
tools for the last 15 years or so - but there are also limitations to this technique in this
period of unprecedented complexity and change. In support of Scenario Planning
and Impact Analysis new approaches have to be explored and integrated into our
risk management and strategic planning processes.
• Back-casting and Back-sight: - “Wild Card” or “Black Swan” events are ultra-extreme
manifestations with a very low probability of, occurrence - but an inordinately high impact
when they do occur. In any post-apocalyptic “Black Swan Event” Scenario Analysis,
we can use Causal Layer Analysis (CLA) techniques in order to analyse and review our
Risk Management Strategies – with a view to identifying those Weak Signals which may
have predicated subsequent appearances of unexpected Wild Card or Black Swan events.

7. STRATEGIC FORESIGHT •
• The prime activity in the Strategic Foresight Process is, therefore, to challenge the
status quo viewpoint and provoke the organisation into thinking seriously about the
possibility that things may not continue as they always have done - and in fact, rarely
do so.
• Strategic Foresight processes should therefore include searching for and identifying
any potential Weak Signals predicating future Wild Card and Black Swan events – in
doing so, revealing previously hidden factors and catalysts of change – thus exposing
a much wider range of challenges, issues, problems, threats, opportunities and risks
than may previously have been considered. •
• Strategic Foresight – Value Chain Management: -
– Risk Planning, Mitigation and Management
– Weak Signals, Wild Cards and Black Swan Events
– Managed Assumptions, Critical Success Factors, Strategy Themes and Value
Propositions

8. SCENARIO FORECASTING •
• Scenarios are stories about how the future may unfold – and how that future will
impact on the way that we work and do business with our business partners,
customers and suppliers. The Strategy Study considers a broad spectrum of
possible scenarios as the only sure-fire way to develop robust strategic responses
that will securely position the Strategic Foresight Programme to deal with every
opportunity and threat domain that may transpire.
• The discovery of multiple scenarios and their associated opportunity / threat impact
assessments, along with their probability of materialising – covers a wide range of
possible and probable Opportunity / Threat situations – describing a rich variety of
POSSIBLE, PROBABLE and ALTERNATIVE FUTURES •
• Scenario Forecasting – Impact Analysis: -
– Possible, Probable and Alternative Future Scenarios
– Clustered Assumptions, Critical Success Factors, Strategy Themes
– Possible Future Business Models and Value Propositions, Products and Services

9. STRATEGY VISIONING, FORMULATION AND DEVELOPMENT •
• After forecasting has laid out a range of potential Future Scenarios, visioning comes
into play — generating a pragmatic view of our “preferred” Future Environment – thus
starting to suggest stretch goals for moving towards our “ideal” Strategy Models -
using the Strategic Principles and Policies to drive out the “desired” Vision, Missions,
Outcomes, Goals and Objectives •
• Strategy Visioning, Formulation and Development: -
– Strategic Principles and Policies, Guidelines and Best Practices
– Strategy Models and desired Vision, Missions, Outcomes, Goals and Objectives
– Proposed Future Business Models and Value Propositions, Products and Services

10. PLANNING: the bridge between the VISION and the ACTION – the “ACTION LINK” •
• Here, the Strategy team transforms the desired Vision, Missions, Outcomes, Goals
and Objectives into the Strategic Master Plan, Enterprise Landscape Models, Strategic
Roadmaps and Transition Plans for organisational readiness and mobilisation –
maintaining Strategic Foresight mechanisms (Horizon Scanning, Monitoring and
Tracking) to preserve the capability to quickly respond to fluctuations in internal and
external environments •
• Strategy Enablement and Delivery Planning: -
– Planned Future Business Models and Value Propositions, Products and Services
– Strategic Master Plan, Enterprise Landscape Models, Roadmaps, Transition Plans

11. ACTING •
• This penultimate phase is about communicating results and developing action
agendas for mobilising strategy delivery – thus launching Business Programmes
that will drive forwards to the realisation of Strategic Master Plans and Future
Business Models through Business Transformation, Enterprise Portfolio
Management, Technology Refreshment and Service Management - via Cultural
Change, innovative multi-tier and collaborative Business Operating Models,
Emerging Technologies (Smart Devices, the Smart Grid and Cloud Services)
Business Process Re-engineering and Process Outsource - Onshore / Offshore. •
• Strategy Enablement and Delivery Programmes: -
– Launched Future Business Models and Value Propositions, Products and Services
– Enterprise Portfolio Management - Technology Refreshment • System Management •
– Business Transformation – Organisational Re-structuring • Cultural Change •
Business Process Management • Operating Models • Programme Planning & Control
– DCT Models - Demand / Supply Models • Shared Services.• Business Process
Outsource •
– Emerging Technologies – Real-time Analytics • Smart Devices • Smart Grid • Mobile
Computing • Cloud Services •
– Service Management - Service Access • Service Brokering • Service Provisioning •
Service Delivery •

12. REVIEWING •
• In this final phase, we focus on Key Lessons Learned and maintaining the
flow of useful information from the Strategic Foresight mechanisms and
infrastructure – in order to support an ongoing lean and agile capability to
continually and successfully respond to the volatile and dynamic internal and
external environment - through Futures Studies, Strategy Reviews, Business
Planning and long-range Forecasting. •
We also prepare for the next round of the Strategy Cycle, beginning again
with Phase 1 – Framing and Scoping.
• Strategy Review: -
– Reviewed Business Models and Value Propositions, Products and Services
– Horizon Scanning, Monitoring and Tracking Systems, Infrastructure and Data
– Futures Studies, Strategy Reviews, Business Planning and long-range Forecasting
Peter Bishop and Andy Hines – University of Houston

13.The Crystal Ball Report
The Crystal Ball Report is a comprehensive document that aggregates the results from all of
the phases of strategic analysis. The findings from the technical analysis of SWAT, PEST and
5 Forces elements – along with an assessment of Business and Technical (non-functional)
Drivers / Requirements – taking into account your desired outcomes, goals and objectives.
Recommendations for Strategy Implementation – Organisational Change and Business
Transformation – is contained in the Strategic Roadmap are grouped together in The Crystal
Ball Report. SWAT, PEST and 5 Forces elements are highlighted. Stakeholder Groups, roles
and responsibilities are defined, a Strategy Programme Plan is generated and an Architecture
Roadmap is produced and elaborated. The Crystal Ball Report includes a detailed System
Dependency Map – outlining application system and platform candidates for Technology
Refreshment – COTS integration, Application Consolidation, Application Re-hosting in the
Cloud – or complete Application Renovation and Renewal based on new Enterprise Platforms.
The Crystal Ball Report is designed to become the “shared vision” reference point, where all
stakeholders can see how their needs and functions are both addressed and add value to the
overall corporate plan, keeping everyone “in the boat, and rowing in the same direction.”

Future Management
Methods and Techniques
Many Economists and Economic Planners have arrived at
the conclusion that most organizations have not yet
widely developed sophisticated Economic Modelling
systems and integrated their outputs into the strategic
planning process.
The objective of this paper is to shed some light into the
practical state of business and economic environmental
scanning, tracking, monitoring and forecasting function in
organizations Impacted by Business Cycles.

Professors Peter Bishop and Andy Hines at the University of Texas Futures
Studies School at the Houston Clear Lake site have developed a definitive
Strategic Foresight Framework - Thinking About the Future
1. FRAMING AND SCOPING • This important first step enables organizations
to define the purpose. focus, scope and boundaries of the Political, Legal,
Economic, Cultural, Business and Technology problem / opportunity
domains requiring resolution. Taking time at the outset of a project, the
Strategic Foresight Team defines the Futures Study domain, outlines the
required outcomes, goals and objectives and determines how best to
achieve them. •
• Strategy Study Definition – Problem / Opportunity Domains: -
– Definition - Focus, Scope, Purpose and Boundaries
– Approach - What – How – Why – Who – When – Where?
– Justification - Cost, Duration and Resources v. Future Benefits and Cash Flows

2. ENGAGING • This second phase is about stakeholder management -
developing action agendas for mobilising stakeholders and opening
communications channels, soliciting collaborative participation and
input. This may involve staging a wide range of Stakeholder Events ,
organising Strategy Communications, Target-setting and Action
Planning, establishing mechanisms for reporting actual achievement
against targets – in order that the Strategic Foresight Team engage a
wide range of stakeholders, presents a future-oriented, customer-
focussed approach and enables the efficient delivery of Strategy
Study artefacts & benefits in planned / managed work streams. •
• Strategy Study Mobilisation – Stakeholder Engagement: -
– Communication Strategy
– Benefits Realisation Strategy
– Strategy Study Programme Plan
– Stakeholder, SME and TDA Strategy Study Launch Events

3. RESEARCH – HORIZON SCANNING, MONITORING AND TRACKING: •
Once the Strategic Foresight Team is clear about the engagement
boundaries, purpose, problem / opportunity domains and scope of a
Strategy Study - they can begin to scan both internal and external
environments for all relevant input content – information and data
describing extrapolations, patterns and trends – or indicating global
transformations, emerging and developing factors and catalysts of
change – and to search for, seek out and identify any Weak Signals
indicating the potential for disruptive Wild Card or Black Swan events. •
• Strategy Investigation – Content Capture: -
– Factors and Catalysts of Change
– Extrapolations, Patterns and Trends
– Internal and External Content, Information and Data
– Horizon Scanning, Monitoring and Tracking Systems amd Infrastructure

4. STRATEGY DISCOVERY – STAKEHOLDER EVENTS & STRATEGY THEMES •
Here we begin to identify and extract useful information from the mass
of Research Content that we have collected. Critical Success Factors,
Strategy Themes and Value Propositions begin to emerge from Data Set
“mashing”, Data Mining and Analytics against the massed Research Data
– and all supplemented via the very human process of Cognitive Filtering
and Intuitive Assimilation of selected information - through Discovery
Workshops, Strategy Theme Forums, Value Chain Seminars, Special
Interest Group Events and one-to-one Key Stakeholder Interviews. •
• Strategy Discovery – Content Analysis: -
– Data Set “mashing”, Data Mining and Analytics
– Stakeholder, SME and TDA Strategy Discovery Events
– Discovered Assumptions, Critical Success Factors, Strategy Themes and Value
Propositions

5. STRATEGIC RISK MANAGEMENT • The underlying premise of Strategic
Risk Management is that every enterprise exists to provide value for its
stakeholders. All entities face uncertainty and the possibility of chaos
and disruption. Risk Management is the evaluation of uncertainty. The
challenge is to determine how much risk we are able to accept as we
strive to grow stakeholder value. Uncertainty presents both opportunity
and risk with the possibility of either erosion or enhancement of value.
Strategic Foresight enables stakeholders to deal effectively with
uncertainty and associated risk and opportunity - thus enhancing the
capability of the Enterprise to build long-term value. •
• Risk Management – Value Chain Building: -
– Risk Research and Identification
– Uncertainty, Chaos and Disruption
– Identified Assumptions, Critical Success Factors, Strategy Themes and Value
Propositions

• Operational Risk (internal threats)
– Legal Risk – Contractual Due Diligence and Compliance
– Statutory Risk – Legislative Due Diligence and Compliance
– Regulatory Risk – Regulatory Due Diligence and Compliance
– Competitor Risk – Competitor Analysis, Defection Detection / Churn
Management
– Reputational Risk – Internet Content Scanning, Intervention / Threat
Management
– Corporate Responsibility – Enterprise Governance, Reporting and Controls
– Digital Communications and Technology Risk
• Security Risk – Security Principles, Policies and Architecture
• Process Risk – Business Strategy and Architecture
• Information Risk – Information Strategy and Architecture
• Technology Risk – Technology Strategy and Architecture
• Stakeholder Risk – Benefits Realisation Strategy and Communications Management
• Vendor / 3rd Party Risk – Strategic Vendor Analysis and Supply Chain Management

6. THREAT ANALYSIS • In most organizations, many stakeholders, if
unchallenged, tend to believe that threat scenarios - as discovered in
various SWOT / PEST Analyses - are going to play out pretty much the
same way as they have always done so in the past. When the Strategic
Foresight Team probes an organization’s view of the future, they usually
discover an array of unexamined, unexplained assumptions tending to
either maintain the current status quo – or converging around discrete
clusters of small, linear, incremental future changes •
• Threat Analysis – Value Chain Analysis: -
– Threat Analysis, Assessment and Prioritisation
– Global Transformations, Factors and Catalysts of Change
– Analysed Assumptions, Critical Success Factors, Strategy Themes and Value
Propositions

7. STRATEGIC FORESIGHT • The prime activity in the Strategic Foresight
Process is, therefore, to challenge the status quo viewpoint and provoke
the organisation into thinking seriously about the possibility that things
may not continue as they always have done - and in fact, rarely do so.
Strategic Foresight processes should therefore include searching for and
identifying any potential Weak Signals predicating future Wild Card and
Black Swan events – in doing so, revealing previously hidden factors and
catalysts of change – thus exposing a much wider range of challenges,
issues, problems, threats, opportunities and risks than may previously
have been considered. •
• Strategic Foresight – Value Chain Management: -
– Risk Planning, Mitigation and Management
– Weak Signals, Wild Cards and Black Swan Events
– Managed Assumptions, Critical Success Factors, Strategy Themes and Value
Propositions

8. SCENARIO FORECASTING • Scenarios are stories about how the future may
unfold – and how that future will impact on the way that we work and do
business with our business partners, customers and suppliers. The Strategy
Study considers a broad spectrum of possible scenarios as the only sure-fire
way to develop robust strategic responses that will securely position the
Strategic Foresight Programme to deal with every opportunity and threat
domain that may transpire. The discovery of multiple scenarios and their
associated opportunity / threat impact assessments, along with their
probability of materialising – covers a wide range of possible and probable
Opportunity / Threat situations – describing a rich variety of POSSIBLE,
PROBABLE and ALTERNATIVE FUTURES •
• Scenario Forecasting – Impact Analysis: -
– Possible, Probable and Alternative Future Scenarios
– Clustered Assumptions, Critical Success Factors, Strategy Themes
– Possible Future Business Models and Value Propositions, Products and Services

9. STRATEGY VISIONING, FORMULATION AND DEVELOPMENT • After
forecasting has laid out a range of potential Future Scenarios, visioning
comes into play — generating a pragmatic view of our “preferred” Future
Environment – thus starting to suggest stretch goals for moving towards our
“ideal” Strategy Models - using the Strategic Principles and Policies to drive
out the “desired” Vision, Missions, Outcomes, Goals and Objectives •
• Strategy Visioning, Formulation and Development: -
– Strategic Principles and Policies, Guidelines and Best Practices
– Strategy Models and desired Vision, Missions, Outcomes, Goals and Objectives
– Proposed Future Business Models and Value Propositions, Products and Services

10. PLANNING: = the bridge between the VISION and the ACTION – the
“ACTION LINK” • Here, the Strategy team transforms the desired Vision,
Missions, Outcomes, Goals and Objectives into the Strategic Master Plan,
Enterprise Landscape Models, Strategic Roadmaps and Transition Plans
for organisational readiness and mobilisation – maintaining Strategic
Foresight mechanisms (Horizon Scanning, Monitoring and Tracking) to
preserve the capability to quickly respond to fluctuations in internal and
external environments •
• Strategy Enablement and Delivery Planning: -
– Planned Future Business Models and Value Propositions, Products and
Services
– Strategic Master Plan, Enterprise Landscape Models, Roadmaps and
Transition Plans

11. ACTING • This penultimate phase is about communicating results and developing
action agendas for mobilising strategy delivery – thus launching Business
Programmes that will drive forwards to the realisation of Strategic Master Plans and
Future Business Models through Business Transformation, Enterprise Portfolio
Management, Technology Refreshment and Service Management - via Cultural
Change, innovative multi-tier and collaborative Business Operating Models,
Emerging Technologies (Smart Devices, the Smart Grid and Cloud Services) Business
Process Re-engineering and Process Outsource - Onshore / Offshore. •
• Strategy Enablement and Delivery Programmes: -
– Launched Future Business Models and Value Propositions, Products and Services
– Enterprise Portfolio Management - Technology Refreshment • System Management •
– Business Transformation – Organisational Re-structuring • Cultural Change • Business
Process Management • Operating Models • Programme Planning & Contrl
– DCT Models - Demand / Supply Models • Shared Services.• Business Process Outsource •
– Emerging Technologies – Real-time Analytics • Smart Devices • Smart Grid • Mobile
Computing • Cloud Services •
– Service Management - Service Access • Service Brokering • Service Provisioning •
Service Delivery •

12. REVIEWING • In this final phase, we focus on Key Lessons Learned and
maintaining the flow of useful information from the Strategic Foresight
mechanisms and infrastructure – in order to support an ongoing lean and
agile capability to continually and successfully respond to the volatile and
dynamic internal and external environment - through Futures Studies,
Strategy Reviews, Business Planning and long-range Forecasting. •
We also prepare for the next round of the Strategy Cycle, beginning again
with Phase 1 – Framing and Scoping.
• Strategy Review: -
– Reviewed Business Models and Value Propositions, Products and Services
– Horizon Scanning, Monitoring and Tracking Systems, Infrastructure and Data
– Futures Studies, Strategy Reviews, Business Planning and long-range Forecasting
Peter Bishop and Andy Hines – University of Houston

TECHNICAL APPENDICES
Mechanical Processes –
Wave Mechanics (String Theory) – integrates the behaviour of every size and type of object

Future Management
Research Philosophy
randomness is as a result of Unknown Forces.....
Classical Mechanics (Newtonian Physics) – governs the behaviour of everyday objects – any
apparent randomness is as a result of Unknown Forces.....
Quantum Mechanics – governs the behaviour of unimaginably small sub-atomic particles – all
events are truly and intrinsically both symmetrical and random.....
Relativity Theory – governs the behaviour of impossibly super-massive cosmic structures – any
apparent randomness or asymmetry is as a result of Quantum Dynamics.....
Wave Mechanics (String Theory) – integrates the behaviour of every type of object –randomness
and asymmetry is a result of Unknown Forces and Quantum Dynamics.....

Research Philosophies, Paradigms,
Investigative and Analytic Methods
PROBABILISTIC versus DETERMINISTIC PARADIGMS
Rationalism – “blue-sky” pure research - the stance of the natural scientist
Rationalism can be defined as “probabilistic research approaches that employ forensic and
analytical methods, make extensive use of both qualitative and quantitative analysis - free from
any pre-determined behavioral models - in order to discover hidden or unknown truths”
Positivism – goal seeking - the stance of the applied scientist
Positivism can be defined as “deterministic research approaches that employ empirical methods,
and make extensive use of quantitative analysis, or develop logical calculi in order to develop
hypotheses and build conceptual models in support of formal explanatory theory”

Future
Research
Methods
• When undertaking
any research of
either a Scientific
or Humanistic
nature, it is most
important for the
researcher and
supervisor to
consider, compare
and contrast all of
the varied and
diverse Research
Philosophies and
Paradigms, Data
Analysis Methods
and Techniques
available - along
with the express
implications of
their treatment of
ontology and
epistemology
issues....,

Qualitative and Quantitative Methods
Qualitative Methods - tend to be deterministic, interpretive and subjective in nature.
• When we wish to design a research project to investigate large volumes of unstructured data
producing and analysing graphical image and text data sets with a very large sample or set of
information – “Big Data” – then the quantitative method is preferred. As soon as subjectivity -
what people think or feel about the world - enters into the scope (e.g. discovering Market
Sentiment via Social Media postings), then the adoption of a qualitative research method is vital.
If your aim is to understand and interpret people’s subjective experience and the broad range of
meanings that attach to it, then interviewing, observation and surveying a range of non-
numerical data (which may be textual, visual, aural) are key strategies you will consider.
Research approaches such as using focus groups, producing case studies, undertaking
narrative or content analysis, participant observation and ethnographic research are all important
qualitative methods. You will also want to understand the relationship of qualitative data to
numerical research. Any qualitative methods pose their own problems with ensuring the
research produces valid and reliable results (see also: Analytics - Working with “Big Data”).
Quantitative Methods - tend to be probabilistic, analytic and objective in nature.
• When we want to design a research project to tests a hypothesis objectively by capturing and
analysing numerical data sets with a large sample or set of information – then the quantitative
method is preferred. There are many key issues to consider when you are designing an
experiment or other research project using quantitative methods, such as randomisation and
sampling. Also, quantitative research uses mathematical and statistical means extensively to
produce reliable analysis of its results (see also: Cluster Analysis and Wave-form methods).

Ontology
• Ontology is concerned with nature of reality. Ontology raises questions about the
assumptions that researchers have about the way the world operates - and their
commitment to particular viewpoints. The two aspects of ontology we describe here will
both have their devotees among business and management researchers. In addition,
both are likely to be accepted as producing valid knowledge by many researchers
– The first aspect of ontology that we need to discuss is objectivism - which supports a
position that social entities and phenomena exist in reality - independent from, and
external to, the various social actors who are concerned with their existence.
– The second aspect, subjectivism , holds that social phenomena are created from the
perceptions and consequent actions of those social actors concerned with their existence
• Blaikie (1993) describes the root definition of ontology as ‘the science or study of being’
and develops this description for the social sciences to encompass ‘claims and
assumptions, both valid and invalid, about what does and does not exist, what it may or
may not look like, what units make it up and how these units interact with each other’. In
short, ontology encompasses both reality and the intimate and very personal viewpoint of
reality held by ourselves and our fellow researchers (valid or invalid, learned, claimed or
assumed) - along with our opinions as to the true nature of reality.

Ontology
• Hatch and Cunliffe (2006) use both an everyday example, and a social science example
to illustrate this point. For the everyday example, they use the example of a workplace
report – asking the subject to question whether it describes what is really going on in the
workplace - or only what the author thinks may be going on. They go on to highlight the
complexity that is introduced when considering phenomena such as personality types,
social class, culture, education, power and control, and what really exists and what is
simply an illusion. Further extending the discussion as to how individuals (and groups)
determine these realities – does this reality exist only through experience of it
(subjectivism), or does it exist independently of those who live with it (objectivism).
• Specifically, we need to ask if this is an objective reality that really exists, or is it only a
subjective reality created in our own minds. We all have a number of deeply embedded
ontological assumptions which will affect our view on what is real and whether we
attribute existence to one set of things over another set of things. If these underlying
assumptions are not identified, exposed and considered, then the researcher may be
blinded to certain aspects of the inquiry or certain phenomena - since those aspects or
phenomena may be somewhat taken for granted - implicit, assumed, thus not opened up
for question, consideration - or even discussion.
Ontology is concerned with nature of reality - and raises questions about assumptions

Epistemology
• Closely coupled with ontology and its consideration of what constitutes reality - epistemology
considers views about the most appropriate ways of enquiring into the nature of the world
(Easterby-Smith, Thorpe and Jackson, 2008) and ‘what is knowledge and what are the sources
and limits of knowledge’ (Eriksson and Kovalainen, 2008). Questions of epistemology begin to
consider the research method, and Eriksson and Kovalainen go on to discuss how
epistemology defines how knowledge can be produced and argued for.
• Blaikie (1993) describes epistemology as ‘the theory or science of the method or grounds of
knowledge’ - expanding this into a set of claims or assumptions about the ways in which it is
possible to gain knowledge of reality, how what exists may be known, what can be known, and
what criteria must be satisfied in order to be described as knowledge.
• Chia (2002) describes epistemology as ‘how and what it is possible to know’ and the need to
reflect on methods and standards through which reliable and verifiable knowledge is produced.
• Hatch and Cunliffe (2006) summarise epistemology as ‘knowing how you can know’ and
expand this by asking how is knowledge generated, what criteria discriminate good knowledge
from bad knowledge, and how should reality be represented or described. They go on to
highlight the inter-dependent relationship between epistemology and ontology, and how one
both informs, and depends upon, the other.
Epistemology concerns what constitutes acceptable knowledge in a field of study.

DETERMINISTIC Versus PROBABILISTIC FUTURES
STRATEGIC MANAGEMENT tends to be Deterministic in nature - 12 Deterministic Futures.....
DETERMINISTIC FUTURE VIEWPOINTS
• Humanist (Instructional) Paradigm - Sceptic Futurism
• Dogmatic (Theosophical) Paradigm - Reactionary Futurism
• Utilitarian (Consequential) Paradigm – Egalitarian Futurism
• Steady State (La meme chose - same as it ever was) Paradigm – Constant Futurism
• Hellenistic (Classical) Paradigm – Future of Human Ethics, Morals, Values and Beliefs
• Empirical (Scientific Determinism, Theoretical Positivism) Paradigm – Hypothetical Futurism
• Predictive (Ordered, Systemic, Mechanistic, Enthalpy) Paradigm – Deconstructionist Futurism

DETERMINISTIC Versus PROBABILISTIC FUTURES
FUTURES STUDIES tends to be Probabilistic in nature - 12 Probabilistic Futures.....
PROBABILISTIC FUTURE VIEWPOINTS
• Polemic (Rational) Paradigm - Enlightened Futurism
• Postmodernism (Reactionary) Paradigm - Structural Futurism
• Mystic (Gnostic, Sophistic, Esoteric, Cathartic) Paradigm – Contemplative Futurism
• Uncertainty (Random, Chaotic, Disorderly, Enthalpy) Paradigm - Disruptive Futurism
• Experiential (Forensic, Deductive, Realist, “Blue Sky”) Paradigm – Pragmatic Futurism
• Interpretive (Ordered, Systemic, Mechanistic, Entropic) Paradigm – Constructive Futurism

Future Management
Methods and Techniques
Throughout eternity, all that is of like form comes around
again – everything that is the same must return again in
its own everlasting cycle.....
• Marcus Aurelius – Emperor of Rome •
Introduction

Futures Studies Research
Changement est vieux comme le monde….. le changement est aussi vieux que le temps.

Research Paradigms
Metaphysical
Philosophy
metaphysical
philosophy
Related: -
Alchemy
Ontology
Taxonomy
Classification
Natural History
Natural Philosophy
Systemic
Methodology
Rationalism can be defined as “probabilistic research
approaches that employ forensic and analytical
methods, make extensive use of both qualitative and
quantitative analysis - free from any pre-determined
behavioural models - in order to discover the secrets
of hidden or “unknown” truths
Positivism can be defined as “deterministic research
approaches that employ empirical methods, and
make extensive use of quantitative analysis, or
develop logical calculi in order to develop hypotheses
and build conceptual models in support of formal
explanatory theory”
The essence of Realism is that what the senses
show us as reality is the truth; that objects have an
existence independent of the human mind.
Interpretation advocates the necessity for
researchers to understand differences between
Pragmatism holds that the most important
determinant of the epistemology, ontology, axiology
adopted is the question posed by the research
Rationalism – “blue-sky” research -
the natural stance of the free and
unencumbered “pure” scientist
Positivism – goal seeking - the
natural stance of the restricted and
constrained “applied” scientist
Realism – the direct, critical and
objective science of realism
Interpretation – scientific
researchers as “social actors”
Pragmatism – studies subjective
judgements about questions of
ethics, values and beliefs

Futures Research Philosophies
and Investigative Methods
Qualitative and Quantitative Investigative Methods
Qualitative Methods: –
tend to be deterministic, interpretive and subjective in nature.
Quantitative Methods: –
tend to be probabilistic, analytic and objective in nature.....

Futures
Research
Methods
• When undertaking
any research of
either a Scientific
or Humanistic
nature, it is most
important for the
researcher and
supervisor to
consider, compare
and contrast all of
the varied and
diverse Research
Philosophies and
Paradigms, Data
Analysis Methods
and Techniques
available - along
with the express
implications of
their treatment of
ontology and
epistemology
issues....,

Research Study Roles and Responsibilities
• Supervisor – authorises and directs the Futures Research Study.
• Project Manager – plans and leads the Futures Research Study.
• Moderator – reviews and mentors the Futures Research Study.
• Researcher – undertakes the detailed Futures Research Tasks.
• Research Aggregator – examines hundreds of related Research
papers - looking for hidden or missed Findings and Extrapolations.
• Author – compiles, documents and edits the Research Findings.

randomness is as a result of Unknown Forces.....
Classical Mechanics (Newtonian Physics) – governs the behaviour of everyday objects – any
apparent randomness is as a result of Unknown Forces.....
Quantum Mechanics – governs the behaviour of unimaginably small sub-atomic particles – all
events are truly and intrinsically both symmetrical and random.....
Relativity Theory – governs the behaviour of impossibly super-massive cosmic structures – any
apparent randomness or asymmetry is as a result of Quantum Dynamics.....
Wave Mechanics (String Theory) – integrates the behaviour of every type of object –randomness
and asymmetry is a result of Unknown Forces and Quantum Dynamics.....

Quantitative
Paradigm
In the Quantitative Paradigm, the design of a
research study begins with the selection of a research
topic (subject) and a research paradigm (method).
Quantitative Research refers to the systematic
empirical investigation of social and scientific
phenomena through system modelling and statistical
analysis - via direct observation and careful collection
of mathematical, numerical or biometric datasets, and
thorough analysis and interpretation of the data.
Scientific Research observes and
collects data on the behaviour of a
system, formulates a hypothesis to
explain the observed behaviour, and
then designs and executes an
experiment to test how well his
hypothesis predicts the actual and
real observations and outcomes.
Qualitative
Paradigm
Qualitative Paradigm. Most qualitative research
texts identify three primary types of research:-
1. Exploratory – research on a concept, people, or
situation that the researcher knows little about.
2. Descriptive (Narrative) – research on a concept,
people, or situation that the researcher knows
something about, but just wants to describe the
findings that he/she has found or observed.
3. Explanatory – involves deriving a hypothesis
from existing theories and available models, then
testing that hypothesis through a process of
experimental observation and data collection.

• Blue skies research (also called blue sky science) is pure scientific research
in those domains where "real-world" applications are not always immediately
apparent. This has been defined as "research without a clear goal" and
"curiosity-driven science." Blue skies research may sometimes be used
interchangeably with the term "basic research“ or “fundamental research”.
• Proponents of this probabilistic mode of science argue that unanticipated
scientific breakthroughs are sometimes more valuable than the outcomes of
agenda-driven (empirical, deterministic) research – thus heralding advances
in genetics and stem cell biology as examples of the unforeseen benefits of
Human Genome Mapping research - which was originally intended as being
purely theoretical in scope.
• The inherently uncertain return on investment of blue-sky projects render them
both politically and commercially unpopular - for example SETI, the Search for
Extra-Terrestrial Intelligence – thus they tend to lose funding to more reliably
practical, pragmatic and profitable research. The specific name "blue skies" to
describe this kind of research is mainly used in Great Britain.

• This section aims to discuss Futures Research Philosophies in detail, in order to
develop a general awareness and understanding of the options - and to describe a
rigorous approach to Research Methods and Scope as a mandatory precursor to the
full Research Design. Denzin and Lincoln (2003) and Kvale (1996) highlight how
different Research Philosophies can result in much tension amongst stakeholders.
• When undertaking any research of either a Scientific or Humanistic nature, it is most
important to consider, compare and contrast all of the varied and diverse Research
Philosophies and Paradigms that are available to the researcher and supervisor -
along with their respective treatments of ontology and epistemology issues.
• Since Research Philosophies and paradigms often describe dogma, perceptions,
beliefs and assumptions about the nature of reality and truth (and knowledge of that
reality) - they can radically influence the way in which the research is undertaken,
from design through to outcomes and conclusions. It is important to understand and
discuss these contrasting aspects in order that approaches congruent to the nature
and aims of the particular study or inquiry in question, are adopted - and to ensure
that researcher and supervisor biases are understood, exposed, and mitigated.

Futures Studies
• Futures Studies, Foresight, or Futurology is the practice and art of postulating possible,
probable, and preferable future outcomes. Futures studies (colloquially referred to as
"Futures" by many of the field's practitioners) seeks to understand what is likely to
continue, what is likely to change, and what will be completely new and novel. Part of the
discipline seeks to develop a systematic and pattern-based understanding of the past,
present and future, and thus attempts to determine both the content (description) and
probability (likelihood of occurrence) of a wide range of possible, probable and alternative
future outcomes, scenarios, events and trends.
• Futures is an interdisciplinary curriculum, studying yesterday's and today's changes,
through aggregating and analysing both lay and professional strategies, views and
opinions about the future with respect to what may happen tomorrow. This includes
analysing the sources, patterns, and causes of change and stability in an attempt to
develop foresight and to map possible futures. Futures Studies has been greatly
enhanced by the recent arrival of “Big Data” technologies – which automates the process
of Horizon (human domains) and Environment (natural domains) futures research -
scanning, monitoring and tracking massive volumes of global Internet Content, Social
Media Postings, RSS News Feeds and other Data Streams in order to discover “Weak
Signals” and “Wild Cards” – predicators of future change.
• Around the world the field is variously referred to as futures studies, strategic foresight,
futurology, futuristics, futures thinking, futuring, futuribles (in France, the latter is
also the name of the important 20th century foresight journal published only in French),
and prospectiva (in Spain and Latin America). Futures studies (and one of its sub-
disciplines, strategic foresight) are the academic field's most commonly used terms.

Forecasting and Prediction
• Forecasting is the process of logical estimation of events in unknown
future situations. Prediction is a similar, but less rigorous term. Both may
refer to estimation of time series, cross-sectional or longitudinal datasets.
• Usage can differ between areas of application: for example in hydrology,
the terms "forecast" and "forecasting" are sometimes reserved for the
logical projection of probable values at certain specific future times, while
the term "prediction" may be used for more general estimates - such as
the number of times flooding will occur over a given (longer) period.
• Risk and uncertainty are central to forecasting and prediction. Scenarios
and Mathematical models are both used in the practice of Forecasting for
every day events such as weather forecasting for agriculture and shipping,
and business performance forecasting for industry and commerce. The
discipline of demand planning, also sometimes referred to as supply chain
forecasting, embraces both statistical forecasting and scenario analysis.
• Forecasting is commonly used in discussion of time-series data where the
timeline extends over historic (past), current (present) and future events.

Goal-seeking and Back-casting
• Back-casting starts with defining a desirable future and then works backwards to identify
policies and programs that will connect that desired future to the present situation. The
empirical question of back-casting asks: - "if we want to attain a certain goal or set of
objectives, what actions must be taken in order to facilitate our journey and arrive there?“
• Forecasting is the process of predicting the future based on extrapolating current patterns
and trends. Back-casting approaches the challenge of describing the future from the
opposite direction: - “a method in which the future desired conditions are envisioned
and steps are then defined to attain those conditions - rather than taking steps that
are merely a continuation of present methods extrapolated into the future”
• Goal-seeking and Back-casting are a key component of the Soft Path, a concept
developed by Amory Lovins in response to the shock of the 1973 energy crisis in the
United States. Goal-seeking and Back-casting has been further developed, refined and
deployed by various Future Research Groups e.g. “The Natural Step” (TNS) Framework.
• Back-casting is increasingly used in urban planning and resource management of water
and energy. In 2006, the Capital Regional District Water Services, which services the
greater Victoria area in British Columbia, Canada, committed to back-casting from the year
2050 as a formal element of all future strategic water planning initiatives.
• The POLIS Water Sustainability Project has developed a soft path planning calculator that
uses a back-casting framework http://www.poliswaterproject.org/toolkit which was released
in the Autumn of 2009.

Goal-seeking and Back-casting
• Research Groups that deploy Back-casting Frameworks: –
– Global Scenario Group
– Institute for Sustainable Futures http://www.isf.uts.edu.au/
– Pacific Institute
– POLIS Project on Ecological Governance
– POLIS Water Sustainability Project - The POLIS Water Sustainability Project
developed a soft path planning calculator that uses a back-casting framework -
http://www.poliswaterproject.org/toolkit - which was released in Autumn 2009.
– Tellus Institute
• environmental research group that uses back-casting techniques to
develop sustainability strategies
– The Natural Step” (TNS) Framework http://www.forumforthefuture.org
• sustainability research group that uses a back-casting framework to
develop strategies for the environment
– Thinking portal

Foresight
• In Futures Studies, the term "Foresight " embraces: -
– Influencing public policy and strategic direction (“Shaping the future”)
– Critical thinking concerning long-term policy development (planning)
– Debate and consultation to create wider stakeholder participation (networking)
• Foresight is being applied to strategic activities in both the public and the private
sector, and stresses the need to link every activity or project with any kind of future
dimension towards taking action today (the action link) in order to make a planned,
integrated future impact (“shaping the future”) possible.
• Foresight differs from much futures research and strategic planning, as it combines
a range of approaches that encompasses the three key components highlighted
above, which may be recast as: -
– futures (forecasting, forward thinking, perspectives) tools and methods
– planning (strategic analysis, priority setting) timelines and roadmaps
– networking (participatory, dialogic) inclusion and orientation
• Much futures research has been academic, but many Foresight programmes were
designed to research Risk and influence Public Policy or explore Disruptive Change
and influence Research and Development policy in industry. In the past some
technology policy research has been very highly focused. Foresight attempts to go
beyond the normal boundaries and gather much more widely distributed intelligence.

Foresight
• Foresight draws on traditions of work in long-range forecasting and strategic
planning horizontal policymaking and democratic planning, horizon scanning and
futures studies (Aguillar-Milan, Ansoff, Feather, van der Hijden, Slaughter et all) -
but was also highly influenced by systemic approaches to innovation studies,
global design, massive change, science and technology futures, economic, social
and demographic policy, fashion and design - and the analysis of "weak signals"
and "wild cards", "future trends“ "critical technologies“ and “cultural evolution".
– The longer-term: - futures that are usually at least 10 years away (though
there are some exceptions to this, especially in its use in private business).
Since Foresight is an action-oriented discipline (via the planning link) it will
rarely be applied to perspectives beyond a few decades out. Where major
infrastructure decisions such as petrology reservoir exploitation, aircraft design,
power station construction, transport hubs and town master planning decisions
are concerned - then the planning horizon may well be half a century.
– Alternative futures: - it is helpful to examine alternative paths of development,
not just what is currently believed to be most likely or business as usual. Often
Foresight will construct multiple scenarios. These may be an interim step on
the way to creating what may be known as positive visions, success scenarios
or aspirational futures. Sometimes alternative scenarios will be a major part of
the output of a Foresight study, with the decision about what preferred future to
build being left to other mechanisms (Planning and Strategy).

Foresight and Back-sight
• Foresight is the process of understanding the future based on extrapolation of
current patterns and trends along with the analysis of the casual agents of random
events and the contributory factors towards disruptive change.
• Back-sight approaches the challenge of examining the current state from the
opposite direction - a method in which the current adverse conditions and its
causes are analysed and then steps are identified that may have prevented those
adverse conditions arising, mitigated the impact of those adverse conditions or
simply avoided the consequences of those adverse conditions.
• Back-sight. In a post-apocalyptic Black Swan Event Scenario, we can use
Causal Layer Analysis (CLA) techniques to review our Risk Analysis and
Management Strategies in order to identify those Weak Signals which may have
indicated subsequent Wild Cards – risk events which have a very low probability of
occurring, but an inordinately high impact when they do happen – in order to
determine future improvements and enhancements to Enterprise Risk
Management Frameworks.
• Back-sight examines a Black Swan Event or Wild Card Scenario and then works
backwards to identify those actions, policies, agents for change and events that
connected the past to the present. The fundamental question of back-casting
asks: "if we want to mitigate undesirable outcomes, what future actions could have
be taken to avoid it happening or to reduce its impact?“

Weak Signals and Wild Cards
• “Wild Card” and "Black Swan" manifestations are extreme and unexpected
events which have a very low probability of occurrence, but an inordinately high
impact when they do happen. Trend-making and Trend-breaking agents or
catalysts of change may predicate, influence or cause wild card events which are
very hard - or even impossible - to anticipate, forecast or predict.
• In any chaotic, fast-evolving and highly complex global environment, as is
currently developing and unfolding across the world today, the possibility of any
such "Wild Card” or "Black Swan" events arising may, nevertheless, be suspected
- or even expected. "Weak Signals" are subliminal indicators or signs which may
be detected amongst the background noise - that in turn point us towards any
"Wild Card” or "Black Swan" random, chaotic, disruptive and / or catastrophic
events which may be on the horizon, or just beyond......
• Back-casting and Back-sight: - In any post-apocalyptic Black Swan Event
Scenario, we can use Causal Layer Analysis (CLA) techniques to analyse and
review our Risk Management Strategies – in order to identify those Weak Signals
which may have predicted, suggested, pointed towards or indicated subsequent
Wild Cards or Black Swan Events – and so discover changes and improvements
to strengthen and enhance our Enterprise Risk Management Frameworks.

Horizon Scanning, Tracking and
Monitoring
• Are you all at sea over your future.....?

Horizon Scanning
• Horizon Scanning is an important technique for establishing a sound knowledge
base for planning and decision-making. Anticipating and preparing for the future –
uncertainty, threats, challenges, opportunities, patterns, trends and extrapolations – is
an essential core component of any organisation's long-term sustainability strategy.
• What is Horizon Scanning ?
Horizon Scanning is defined by the UK Government Office for Science as: -
“the systematic examination of potential threats, opportunities and likely
future developments, including (but not restricted to) those at the margins
of current thinking and planning”.
• Horizon Scanning may explore novel and unexpected issues as well as persistent
problems or trends. The government's Chief Scientific Adviser is encouraging
Departments to undertake horizon scanning in a structured and auditable manner.
• Horizon Scanning enables organisations to anticipate and prepare for new risks and
opportunities by looking at trends and information in the medium- to long-term future.
• The government's Horizon Scanning Centre of Excellence, part of the Foresight
Directorate in the Department for Business, Innovation and Skills, has the role of
supporting Departmental activities and facilitating cross-departmental collaboration.

Monitoring Processes
• Horizon Scanning, Tracking and Monitoring is a systematic search and examination of
global internet content – “BIG DATA” – information which is gathered, processed and
used to identify potential threats, risks, emerging issues and opportunities in the Human
World - allowing for the incorporation of mitigation and exploitation into in policy making
process - as well as improved preparation for contingency planning and disaster response.
• Horizon Scanning is used as an overall term for discovering and analysing the future of
the Human World – Politics, Economics, Sociology, Religion Culture and War –
considering how emerging trends and developments might potentially affect current policy
and practice. This helps policy makers in government to take a longer-term strategic
approach, and makes present policy more resilient to future uncertainty. In developing
policy, Horizon Scanning can help policy makers to develop new insights and to think
about “outside of the box” solutions to human threats – and opportunities.
• In contingency planning and disaster response, Horizon Scanning helps to manage risk
by discovering and planning ahead for the emergence of unlikely, but potentially high
impact Black Swan events. There are a range of Futures Studies philosophical
paradigms, and technological approaches – which are all supported by numerous
methods, tools and techniques for developing and analysing possible, probable and
alternative future scenarios.

• Scenario Planning and Impact Analysis is the archetypical method for futures studies
because it embodies the central principles of the discipline:
– The future is uncertain - so we must prepare for a wide range of possible, probable
and alternative futures, not just the future that we desire, or hope, will happen.....
– At the same time, it is vitally important that we think deeply and creatively about the
future, or else we run the risk of being either unprepared or surprised – or both.....
• Scenarios contain the stories of these multiple futures - from the hoped for to the expected
and from the wild-card to the Black Swan - in forms which are analytically coherent and
imaginatively engaging. A good scenario grabs our attention and says, ‘‘Take a good look
at this future. This could be your future - are you prepared ?’’
• As consultants and organizations have come to recognize the value of scenarios, they
have also latched onto one scenario technique – a very good one in fact – as the default
for all their scenario work. That technique is the Royal Dutch Shell / Global Business
Network (GBN) matrix approach, created by Pierre Wack in the 1970s and popularized by
Schwartz (1991) in the Art of the Long View and Van der Heijden (1996) in Scenarios: The
Art of Strategic Conversations. In fact, Millett (2003, p. 18) calls it the ‘‘gold standard of
corporate scenario generation.’’

1. War, terrorism and insecurity
2. Layers of power
3. Economic and financial stability
4. BRICs and emerging powers
• Brazil
• Russia
• India
• China
5. The Five Flows of Globalisation
• Ideas
• Goods
• People
• Capital
• Services
6. Intellectual Property and Knowledge
7. Health, Wealth and Wellbeing
8. Transhumanism – Geo-demographics,
Ethnographics and Social Anthropology
9. Population Drift, Migration and Mobility
10. Market Sentiment, Trust and Reputation
11. Human Morals, Ethics, Values and Beliefs
12. History, Culture, Religion and Human Identity
13. Consumerism and the rise of the Middle Classes
14. Social Media, Networks and Connectivity
15. Space - the final frontier
• The Cosmology Revolution - String Theory
16. Science and Technology Futures
• The Nano Revolution
• The Quantum Revolution
• The Information Revolution
• The Bio-Technology Revolution
• The Energy Revolution • Oil Shale Fracking •
• Kerogen • Tar Sands • Methane Hydrate •
• The Hydrogen Economy • Nuclear Fusion •
17. Science and Society – the Social Impact of
Disruptive Technology and Convergence
18. Natural Resources – availability, scarcity and
control – Food, Energy and Water (FEW) crisis
19. Climate Change
• Global Massive Change – the Climate Revolution
20. Environmental Degradation & Mass Extinction
21. Urbanisation and the Smart Cities of the Future

• Outsights Strategy Scenarios create a shared context, clarity and vision over
challenging issues shaping the future in which decision makers can take better
informed decisions on opportunity exploitation and risk management strategies.
• Managing Change Scenario thinking can compel a wide range of people to
open up to new options and change their own images of reality by sharing and
discussing assumptions on what is shaping the world.
• The Outsights Technique translates what is learnt into action in the following
ways to achieve sustainable change and risk management : -
– Providing the content and insight needed to understand changes in the
outside world (Drivers of Change, Scenario Building, Risk Categories)
– Designing and executing processes to devolve organisational change,
business transformation and risk management down from the segment and
business unit level to the individual responsible manager level – delivering
personal accountability for Strategy & Planning, Budgeting & Forecasting,
Change Management, Risk Management, Performance Management and
Standards Compliance with Enterprise Governance, Reporting and Controls

• Outsights Strategy Scenarios supports a shared resource pool covering
those issues shaping the future in which decision makers can make difficult
choices about opportunity exploitation and risk management strategies.
• The Outsights Technique helps stakeholders stand back, take stock and
seek fresh points of view: -
– Facilitation of the internal debate exploring stakeholder value,
opportunity exploitation and risk management
– Sounding board for business innovation and strategy
– Stakeholder engagement and the communication of the process with
the wider partner, stakeholder and employee community
– Review of specific opportunity exploitation and risk management
agendas
– Surfacing diverse opinions from internal and external stakeholders to
identify needs for strategic content, clarity, perspective and action

• The insights discovered by Scenario Planning and Impact Analysis can provide the basis
for prioritising research and development programmes, gathering business intelligence,
designing organisational scorecard objectives and establishing visions and strategies.
Steps
1. Participants are given a scope, focus and time horizon for the exercise.
2. Horizon Scanning, Monitoring and Tracking and Monte Carlo Simulations provide
sources of information. These data sets can come from internal or external sources
– Data Scientists, Domain Experts and Researchers, “Big Data” Analysts, the project
team, or from prior studies and data collection exercises from the individual team
participants. These should cover a broad external analysis, such as STEEP.
3. Individuals review the sources and spot items that cause personal insights on the
focus given. These insights and their sources are captured in the form of abstracts.
4. Abstracts are discussed and themed to indicate wave-forms over the time horizon
concerned. Scenarios are stacked, racked and prioritised by impact and probability.
5. The participants agree on how to address the resulting Scenarios, Waves, Cycles,
Patterns and Trends with supporting information for further futures analysis.
• More information about tools and uses of horizon scanning in Central Government can be
found on the Foresight Horizon Scanning Centre website.

Horizon and Environment Scanning,
Tracking and Monitoring Processes
• Horizon and Environment Scanning, Tracking and Monitoring processes exploit the
presence and properties of Weak Signals – their discovery, analysis and interpretation
were first described by Stephen Aguilar Milllan in the 1960’s, and later popularised by
Ansoff in the 1970’s. Horizon Scanning is defined as “a set of information discovery
processes which data scientists, environment scanners, researchers and analysts use
to prospect, discover and mine the truly massive amounts of internet global content -
innumerable news and data feeds - along with the vast quantities of information stored
in public and private document libraries, archives and databases.”
• All of this external data is found widely distributed across the internet as Global Content
– RSS News Feeds and Data Streams, Academic Research Papers and Datasets - is
processed in order to detect and identify the possibility of unfolding random events and
clusters – “to systematically reduce the level of exposure to uncertainty, to reduce risk
and gain future insights in order to prepare for adverse future conditions – or to exploit
novel and unexpected opportunities for innovation" (LESCA, 1994). As a management
support tool for strategic decision-making, horizon and environment scanning process
have some very special challenges that need to be taken into account by environment /
horizon scanners, researchers, data scientists and analysts - as well as stakeholders.

• Horizon Scanning (Human Activity Phenomena) and Environment Scanning (Natural
Phenomena) are the broad processes of capturing input data to drive futures projects and
programmes - but they also refer to specific futures studies tool sets, as described below.
• Horizon Scanning, Tracking and Monitoring is a highly structured evidence-gathering
process which engages participants by asking them to consider a broad range of input
information sources and data sets - typically outside the scope of their specific expertise.
This may be summarised as looking back for historic Wave-forms which may extend into
the future (back-casting), looking further ahead than normal strategic timescales for wave,
cycle, pattern and trend extrapolations (forecasting), and looking wider across and beyond
the usual strategic resources (cross-casting). A STEEP structure, or variant, is often used.
• Individuals use sources to draw insights and create abstracts of the source, then share
these with other participants. Horizon scanning lays a platform for further futures activities
such as scenarios or roadmaps. This builds strategic analysis capabilities and informs
strategy development priorities. Once uncovered, such insights can be themed as key
trends, assessed as drivers or used as contextual information within a scenario narrative.
• The graphic image below illustrates how horizon scanning is useful in driving Strategy
Analysis and Development: -

Strategy versus Horizon Scanning

• Horizon Scanning, Tracking and Monitoring is the major input for unstructured “Big Data” to
be introduced into the Scenario Planning and Impact Analysis process (along with Monte
Carlo Simulation and other probabilistic models providing structured data inputs). In this
regard, Scenario Planning and Impact Analysis helps to create a conducive team working
environment. It allows consideration of a broad spectrum of input data – beyond the usual
timescales and sources – drawing information together in order to identify future challenges,
opportunities and trends. It looks for evidence at the margins of current thinking as well as in
more established trends. This allows the collective insights of the group to be integrated -
demonstrating the many differing ways which diverse sources contribute to these insights.
• Horizon Scanning, Tracking and Monitoring is ideal as an initial activity for collecting Weak
Signal data input into the Horizon Scanning, Tracking and Monitoring process to kick-off
major futures studies projects and future management programmes. Scenario Planning and
Impact Analysis is also useful as a sense-making and interaction tool for an integrated
future-focused team. Horizon Scanning, Tracking and Monitoring combined with Scenario
Planning and Impact Analysis works best if people external to the organisation are included
in the team - and are encouraged to help bring together new and incisive perspectives.
• The graphic image below illustrates how horizon scanning is useful in spotting weak signals
that might be otherwise difficult to see – and so risk being overlooked: -

Monitoring Processes
• Horizon Scanning, Tracking and Monitoring is a systematic search and examination
of global internet content – “BIG DATA” – information which is gathered, processed and
used to identify potential threats, risks, emerging issues and opportunities as a result of
Human Activity - allowing for the incorporation of mitigation and exploitation themes into
in the policy making process – as well as improved preparation for business continuity,
contingency planning and disaster response, and enterprise risk management events.
• Horizon Scanning is used as an overall term for discovering and analysing the unfolding
future of the Human World – Politics, Economics, Sociology, Religion Culture and War –
considering how emerging trends and developments might potentially affect current policy
and practice. This helps policy makers in government to take a longer-term strategic
approach, and makes present policy more resilient to future uncertainty. In developing
policy, Horizon Scanning can help policy makers to develop new insights and to think
about “outside of the box” solutions to human activity threats – and opportunities.
• In contingency planning and disaster response, Horizon Scanning helps to manage risk
by discovering and planning ahead for the emergence of unlikely, but potentially high
impact Black Swan events. There is a wide range of Futures Studies philosophical
paradigms, and technology approaches – which are all supported by numerous methods,
tools and techniques for exploring possible, probable and alternative future scenarios.

• Horizon and Environment Scanning Event Types – refer to Weak Signals of any unforeseen,
sudden and extreme Global-level transformation or change Future Events in either the military,
political, social, economic or environmental landscape - having an inordinately low probability of
occurrence - coupled with an extraordinarily high impact when they do occur (Nassim Taleb).
• Horizon Scanning Event Types
– Political, Economic and Social Waves
– Religion, Culture and Human Identity Waves
– Art, Architecture, Design and Fashion Waves
– Global Conflict – War, Terrorism, and Insecurity Waves
• Environment Scanning Event Types
– Natural Disasters and Catastrophes
– Human Activity Impact on the Environment - Global Massive Change Events
• Weak Signals – are messages, subliminal temporal indicators of ideas, patterns, trends or
random events coming to meet us from the future – or signs of novel and emerging desires,
thoughts, ideas and influences which may interact with both current and pre-existing patterns
and trends to predicate impact or effect some change in our present or future environment.

“Big Data”
Normal, daily routine activities from our everyday life generates vast
amounts of data. Who owns this data, who has access to it, and what
they can do with it - is largely unknown, undisclosed and un-policed.....
Little-by-little, more and more aspects of our daily life are being
monitored - meaning intimate details of what we do, where we go, and
who we see is now watched and recorded

“Big Data” Global Content Analysis
• “Big Data” refers to those aggregated datasets whose size and scope is
beyond the capability of conventional transactional Database Management
Systems and Enterprise Software Tools to capture, store, analyse and
manage. This definition of “Big Data” is of necessity subjective and qualitative
– “Big Data” is defined as a large collection of unstructured information,
which, when initially captured, contains sparse or undiscovered internal
references, links or data relationships.
• Data Set Mashing or “Big Data” Global Content Analysis – supports Strategic
Foresight Techniques such as Horizon Scanning, Monitoring and
Tracking by taking numerous, apparently un-related RSS and other
Information Streams and Data Feeds, loading them into Very large Scale
(VLS) DWH Structures and Unstructured Databases and Document
Management Systems for interrogating using Data Mining and Real-time
Analytics – that is, searching for and identifying possible signs of hidden data
relationships (Facts/Events) – in order to discover and interpret previously
unknown “Weak Signals” indicating emerging and developing Scenarios,
Patterns and Trends - in turn predicating possible, probable and alternative
transformations, catalysts and agents of change which may develop and
unfold as future “Wild Card” or “Black Swan” events.

Wave-form Analytics in “Big Data”
• Wave-form Analytics is a new analytical tool “borrowed” from spectral wave
frequency analysis in Physics – and is based on Time-frequency analysis – a
technique which exploits the wave frequency and time symmetry principle. This is
introduced here for the first time in the study of human activity waves, and in the
field of economic cycles business cycles, patterns and trends.

• A fundamental challenge found everywhere in business cycle theory is how to
interpret very large scale / long period compound-wave (polyphonic) time series data
sets which are dynamic (non-stationary) in nature. Wave-form Analytics is a new
analytical too based on Time-frequency analysis – a technique which exploits the
wave frequency and time symmetry principle. The role of time scale and preferred
reference from economic observation are fundamental constraints for Friedman's
rational arbitrageurs - and will be re-examined from the viewpoint of information
ambiguity and dynamic instability.
• The Wigner-Gabor-Qian (WGQ) spectrogram demonstrates a distinct capability for
revealing multiple and complex superimposed cycles or waves within dynamic, noisy
and chaotic time-series data sets. A variety of competing deterministic and
stochastic methods, including the first difference (FD) and Hodrick-Prescott (HP)
filter - may be deployed with the multiple-frequency mixed case of overlaid cycles
and system noise. The FD filter does not produce a clear picture of business cycles
– however, the HP filter provides us with strong results for pattern recognition of
multiple co-impacting business cycles. The existence of stable characteristic
frequencies in large economic data aggregations (“Big Data”) provides us with strong
evidence and valuable information about the structure of Business Cycles.

Wave-form Analytics in Natural Cycles
• Solar, Oceanic and Atmospheric Climate Forcing systems demonstrate Complex
Adaptive System (CAS) behaviour - behaviour of ecologies which are more similar to an
organism than that of random and chaotic “Stochastic” systems. The remarkable long-
term stability and sustainability of cyclic climatic systems contrasted with random and
chaotic short-term weather systems are demonstrated by the metronomic regularity of
climate pattern changes driven by Milankovich, the 1470-year Dansgaard-Oeschger and
Bond Cycles – regular and predictable Solar and Oceanic Forcing Climate Sub-systems.
Wave-form Analytics in Human Activity Cycles
• Economic systems also demonstrate Complex Adaptive System (CAS) behaviour -
more similar to an ecology than chaotic “Random” systems. The capacity of market
economies for cyclic “boom and bust” – financial crashes and recovery - can be seen
from the impact of Black Swan Events causing stock market crashes - such as the
failure of sovereign states (Portugal, Ireland, Greece, Iceland, Italy and Spain) and
market participants (Lehman Brothers) due to oil price shocks, money supply shocks
and credit crises. Surprising pattern changes occurred during wars, arm races, and
during the Reagan administration. Like microscopy for biology, non-stationary time
series analysis opens up a new space for business cycle studies and policy diagnostics.

in nature to biological behaviour in an organism than to Disorderly, Chaotic,
Stochastic Systems (“Random” Systems). For example, the remarkable
shocks (1970-72) and credit supply shocks (1927- 1929 and 2008 onwards).
Unexpected and surprising Cycle Pattern changes have historically occurred
during regional and global conflicts being fuelled by technology innovation-driven
arms races - and also during US Republican administrations (Reagan and Bush -
why?). Just as advances in electron microscopy have revolutionised biology -
non-stationary time series wave-form analysis has opened up a new space for

Big Data Analytics Goes Big Time
Big Data Analytics Goes Big Time
• Organizations around the globe and across
industries have learned that the smartest
business decisions are based on fact, not gut
feel. That means they're based on analysis of
data, and it goes way beyond the historical
information held in internal transaction
systems. Internet click-streams, sensor data,
log files, mobile data rich with geospatial
information, and social-network comments
are among the many forms of information
now pushing information stores into the big-
data league above 10 terabytes.
• Trouble is, conventional data warehousing
deployments can't scale to crunch terabytes
of data or support advanced in-database
analytics. Over the last decade, massively
parallel processing (MPP) platforms and
column-store databases have started a
revolution in data analysis. But technology
keeps moving, and we're starting to see
upgrades that are blurring the boundaries of
known architectures. What's more, a whole
movement has emerged around NoSQL (not
only SQL) platforms that take on semi-
structured and unstructured information.
This info-graph presents from 2011 to 2013 update on
what's available, with options including ExtremeData xdb,
EMC's Greenplum appliance, Hadoop and MapReduce, HP's
recently acquired the Autonomy and Vertica platforms,
IBM's separate DB2-based Smart Analytic System and
Netezza offerings, and Microsoft's Parallel Data Warehouse.
Smaller, niche database players include Infobright, Kognitio
and ParAccel. Teradata reigns at the top of the market,
picking off high-end defectors from industry giant Oracle.
SAP's Sybase unit continues to evolve Sybase IQ, the original
column-store database. In short, there's a platform for every
scale level and analytic focus

“Big Data”
Normal, daily routine
activities from our
everyday life generates
vast amounts of data.
Who owns this data,
who has access to it,
and what they can do
with it - is largely
unknown, undisclosed
and un-policed.....
Little-by-little, more and
more aspects of our
daily life are being
monitored - meaning
intimate details of what
we do, where we go,
and who we see is now
watched and recorded.

The Emerging “Big Data” Stack
Targeting – Map / Reduce
Consume – End-User Data
Data Acquisition – High-Volume Data Flows
– Mobile Enterprise Platforms (MEAP’s)
Apache Hadoop Framework
HDFS, MapReduce, Metlab “R”
Autonomy, Vertica
Smart Devices
Smart Apps
Smart Grid
Clinical Trial, Morbidity and Actuarial Outcomes
Market Sentiment and Price Curve Forecasting
Horizon Scanning,, Tracking and Monitoring
Weak Signal, Wild Card and Black Swan Event Forecasting
– Data Delivery and Consumption
News Feeds and Digital Media
Global Internet Content
Social Mapping
Social Media
Social CRM
– Data Discovery and Collection
– Analytics Engines - Hadoop
– Data Presentation and Display
Excel
Web
Mobile
– Data Management Processes
Data Audit
Data Profile
Data Quality Reporting
Data Quality Improvement
Data Extract, Transform, Load
– Performance Acceleration
GPU’s – massive parallelism
SSD’s – in-memory processing
DBMS – ultra-fast database replication
– Data Management Tools
DataFlux
Embarcadero
Informatica
Talend
– Info. Management Tools
Business Objects
Cognos
Hyperion
Microstrategy
Biolap
Jedox
Sagent
Polaris
Teradata
SAP HANA
Netezza (now IBM)
Greenplum (now EMC2)
Extreme Data xdg
Zybert Gridbox
– Data Warehouse Appliances
Ab Initio
Ascential
Genio
Orchestra

“Big Data” Applications
• Science and Technology
– Pattern, Cycle and Trend Analysis
– Horizon Scanning, Monitoring and Tracking
– Weak Signals, Wild Cards, Black Swan Events
• Multi-channel Retail Analytics
– Customer Profiling and Segmentation
– Human Behaviour / Predictive Analytics
• Global Internet Content Management
– Social Media Analytics
– Market Data Management
– Global Internet Content Management
• Smart Devices and Smart Apps
– Call Details Records
– Internet Content Browsing
– Media / Channel Selections
– Movies, Video Games and Playlists
• Broadband / Home Entertainment
– Call Details Records
– Internet Content Browsing
– Media / Channel Selections
– Movies, Video Games and Playlists
• Smart Metering / Home Energy
– Energy Consumption Details Records
• Civil and Military Intelligence
 Digital Battlefields of the Future – Data Gathering
 Future Combat Systems - Intelligence Database
 Person of Interest Database – Criminal Enterprise,
Political organisations and Terrorist Cell networks
 Remote Warfare - Threat Viewing / Monitoring /
Identification / Tracking / Targeting / Elimination
 HDCCTV Automatic Character/Facial Recognition
• Security
 Security Event Management - HDCCTV, Proximity
and Intrusion Detection, Motion and Fire Sensors
 Emergency Incident Management - Response
Services Command, Control and Co-ordination
• Biomedical Data Streaming
 Care in the Community
 Assisted Living at Home
 Smart Hospitals and Clinics
• SCADA Operational Technology
 SCADA Remote Sensing, Monitoring and Control
 Smart Grid Data (machine generated data)
 Vehicle Telemetry Management
 Intelligent Building Management
 Smart Homes Automation

Exploitation – “Big Data”
• There has been much speculation
about how industries will cash in on
“Big Data” In a nutshell “Big Data”
occurs in volumes or structures that
exceeds the functionality / capacity
of conventional hardware, database
platforms and analytical tools.
• Social media and search are leading
the way with big data applications.
As “Big Data” tools and methods
enter the mainstream we will see
businesses make use of the "data
exhaust" that today doesn't get
exploited To put it bluntly, most
companies are failing to leverage
their data assets by failing to realise
the benefits of the huge volumes of
data they are already generating.

Big Data Partnership
Training - For more information on Big Data Partnership’s training offerings, please visit the Training page.
Feel free to Contact Us directly to discuss your specific needs.

Big Data Partnership 3D Approach
• Discovery - As enterprises move into this new age for data analytics, companies can often struggle to
identify where in their large data architecture, big data software and techniques can be utilised. Big Data
Partnership can help those organisations understand where those use cases are through short workshop
engagements. These are typically 2-5 days long and will help not only identify where Big Data Analytics
could help drive more customer insight and ROI but also educate on what tools are in the eco-system.
• Develop - Even with solid use cases and a good understanding of how Big Data software and techniques
could help businesses, it is not always easy to prove the model and commit to the necessary investment
to really make the positive transformation in an organisation. One way of doing this is to take a single use
case and develop a Proof of Concept to prove the expected ROI and business benefit and also validate the
technology. This level of engagement can typically be a month long and can help businesses not only take
the big step towards big data but also help them understand whether the expected ROI is there.
• Deliver - Big Data Partnership are able to assist enterprises in fully realising their Big Data initiatives
through offering fixed price and day-based consultancy to help deliver full data analytics projects. We
understand that each customer has differing needs, therefore we tailor our approach specific to each
client. Effective big data is not just about predetermined buckets or templates for business intelligence; it
is about meaningful analysis and processing of information in a way that is highly relevant to the
business. We have highly skilled Data Scientists as well as deep rooted Big Data Engineers who can help
you fully make the most of your implementations and ensure success of your Big Data projects.

“Big Data”
• Put yourself in the
Big Data driver’s seat.
• Today, companies are
generating massive amounts
of data—everything from
web clicks, to customer
transactions, to routine
business events—and
attempting to mine that data
for trends that can inform
better business decisions.
• Quantivo enables a new
analytics experience that is
bound only by imagination of
the user - it’s a full stack for
turning raw data into
intelligence. The Quantivo
platform features patented,
pattern-based technology
that efficiently integrates
event data across multiple
sources, in hours not weeks.
Your query quest starts here.

“Big Data” Analytics
Quantivo sifts through mountains of data—and spots the patterns that matter.
• When faced with overwhelming amounts of data, looking for the big “aha” can be
next to impossible. That is, unless you’ve got Quantivo on your side. Unlike the other
vendors that overpromise and under-deliver, Quantivo hits the mark with pattern-
based analytics that brings Big Data down to size by tracking relationships between
attributes and ignoring redundancies. With easy-to-use tools, users can zero in on
predictive and repeatable patterns and trends—without losing any of the original
data. In addition, Quantivo pattern-based analytics: -
– Creates behavioural segments derived from a combination of contextually specific
attributes and online/offline detailed event data
– Uncovers buried relationships that link attributes to behaviours
– Tracks how behaviors change over time—and identifies the trigger for these changes
– Helps you “learn what you don’t know” by intelligently auto-compiling lists of
patterns existing in your data

“Big Data” – Analysing and Informing
• “Big Data” is now a torrent raging through every aspect of the global
economy – both the public sector and private industry. Global enterprises
generate enormous volumes of transactional data – capturing trillions of
bytes of information from their extended supply chain – global markets,
customers and suppliers – and from their own internal business operations.
– SENSE LAYER – Remote Monitoring and Control – WHAT and WHEN?
– GEO-DEMOGRAPHIC LAYER – People and Places – WHO and WHERE?
– INFORMATION LAYER – “Big Data” and Data Set “mashing” – HOW and WHY?
– SERVICE LAYER – Real-time and Predictive Analytics – WHAT / WHEN NEXT ?
– COMMUNICATION LAYER – Mobile Enterprise Platforms
– INFRASTRUCTURE LAYER – Cloud Service Platforms

• SENSE LAYER – Remote Monitoring and Control – WHAT and WHEN?
– Remote Sensing – Sensors, Monitors, Detectors, Smart Appliances / Devices
– Remote Viewing – Satellite. Airborne, Mobile and Fixed HDCCTV
– Remote Monitoring, Command and Control – SCADA
• GEO-DEMOGRAPHIC LAYER – People and Places – WHO and WHERE?
– Person and Social Network Directories - Personal and Social Media Data
– Location and Property Gazetteers - Building Information Models (BIM)
– Mapping and Spatial Analysis – Landscape Imaging & mapping, Global Positioning (GPS) Data
– Temporal / Geospatial data feeds –Weather and Climate, Land Usage, Topology / Topography
• INFORMATION LAYER – “Big Data” and Data Set “mashing” – HOW and WHY?
– Content – Structured and Unstructured Data and Content
– Information – Atomic Data, Aggregated, Ordered and Ranked Information
– Transactional Data Streams – Smart Devices, EPOS, Internet, Mobile Networks

• SERVICE LAYER – Real-time and Predictive Analytics – WHAT / WHEN NEXT?
– Global Mapping and Spatial Analysis - GIS
– Service Aggregation, Intelligent Agents and Alerts
– Data Analysis, Data Mining and Statistical Analysis
– Optical and Wave-form Analysis and Recognition, Pattern and Trend Analysis an Extrapolation
• COMMUNICATION LAYER – Mobile Enterprise Platforms and the Smart Grid
– Connectivity - Smart Devices, Smart Apps, Smart Grid
– Integration - Mobile Enterprise Application Platforms (MEAPs)
– Backbone – Wireless and Optical Next Generation Network (NGE) Architectures
• INFRASTRUCTURE LAYER – Cloud Service Platforms
– Public, Mixed / Hybrid, Enterprise, Private, Secure and G-Cloud Cloud Models
– Infrastructure – Network, Storage and Servers
– Applications – COTS Software, Utilities, Enterprise Services
– Security – Principles, Policies, Users, Profiles and Directories, Data Protection

What Google Searches about the
Future tell us about the Present...
• Internet Research published recently demonstrates how Internet Searches about future topics
have a significant link to the economic success of the native country of the Search Requester.
• Google (GOOG) search data have become a statistical gold mine for academics, scientists,
and number crunchers, who have used it for everything from predicting flu outbreaks to
determining to what extent racial prejudice robbed Barack Obama of otherwise certain votes.
• Two academics in the U.K., Warwick Business School associate professor Tobias Preis and Dr.
Helen Susannah Moat of University College London, analyzed more than 45 billion Google
searches performed during 2012 and calculated the national ratio between searches that
included “2013” and those that included “2011” for the native country of the Search Requester,
• They found that countries where “Internet users … search for more information about the future
tend to have a higher per-capita GDP,” says Preis, who created a stir in 2010 when he used a
similar data-crunching approach to quantify and model stock price fluctuations of companies on
the Standard & Poor’s 500 index. “The more a country is looking to the future using Internet
Searches, then the more successful economically the country is.”
• The rational is, when the economy is humming along nicely, it is easier to be optimistic—to plan
vacations, buy season tickets, investigate investment opportunities, etc.
• Of all nations, the Germans are the most forward-looking, knocking Britons from the top spot.
Preis explained that the U.K. scored so highly a year earlier because of the high national
expectation around the forthcoming 2012 London Olympic Games. This year, the Germans are
looking forward to a pivotal federal election. Preis, a German national, declined to comment on
whether Germany’s exuberance for the future bodes well for incumbent Angela Merkel.
• Interestingly, the U.S. ranks 11th, up from 15th a year earlier. The 2012 findings showed that
entering an election year, more Americans were looking backward to 2010. Preis says that this
year, Americans as a whole are more optimistic about 2013 than they were a year earlier,.
• Economic laggards Pakistan, Vietnam, and Kazakhstan round out the bottom of the list.

From sports to scientific research, a surprising range
of industries will begin to find value in big data.....

Clustering in “Big Data”
“A Cluster is a grouping of the same, similar and equivalent, data
elements containing values which are closely distributed – or
aggregated – together”
Clustering is a technique used to explore content and understand
information in every business and scientific field that collects and
processes verify large volumes of data
Clustering is an essential tool for any “Big Data” problem

• “Big Data” refers to vast aggregations (super sets) consisting of numerous individual
datasets (structured and unstructured) - whose size and scope is beyond the capability of
conventional transactional (OLTP) or analytics (OLAP) Database Management Systems
and Enterprise Software Tools to capture, store, analyse and manage. Examples of “Big
Data” include the vast and ever changing amounts of data generated in social networks
where we maintain Blogs and have conversations with each other, news data streams,
geo-demographic data, internet search and browser logs, as well as the ever-growing
amount of machine data generated by pervasive smart devices - monitors, sensors and
detectors in the environment – captured via the Smart Grid, then processed in the Cloud –
and delivered to end-user Smart Phones and Tablets via Intelligent Agents and Alerts.
• Data Set Mashing and “Big Data” Global Content Analysis – drives Horizon Scanning,
Monitoring and Tracking processes by taking numerous, apparently un-related RSS and
other Information Streams and Data Feeds, loading them into Very large Scale (VLS)
DWH Structures and Document Management Systems for Real-time Analytics – searching
for and identifying possible signs of relationships hidden in data (Facts/Events)– in order to
discover and interpret previously unknown Data Relationships driven by hidden Clustering
Forces – revealed via “Weak Signals” indicating emerging and developing Application
Scenarios, Patterns and Trends - in turn predicating possible, probable and alternative
global transformations which may unfold as future “Wild Card” or “Black Swan” events.
“Big Data”

Clustering Phenomena in “Big Data”

• The profiling and analysis of very large aggregated datasets in order to determine a
‘natural’ or implicit structure of data relationships or groupings – in order to discover
hidden data relationships driven by unknown factors where no prior assumptions
are made concerning the number or type of groups discovered or Cluster / Group
relationships, hierarchies or internal data structures – is a critically important starting
point – and forms the basis of many statistical and analytic applications.
• The subsequent explicit Cluster Analysis of discovered data relationships is an
important and critical technique which attempts to explain the nature, cause and
effect of unknown clustering forces driving implicit profile similarities, mathematical
or geographic distributions. Geo-demographic techniques are frequently used in
order to profile and segment Demographic and Spatial data by ‘natural’ groupings –
including common behavioural traits, Clinical Trial, Morbidity or Actuarial outcomes –
along with numerous other shared characteristics and common factors Cluster
Analysis attempt to understand and explain those natural group affinities and
geographical distributions using methods such as Causal Layer Analysis (CLA).....

“A Cluster is a group of profiled data similarities aggregated closely together”
• Clustering is an essential tool for any “Big Data” problem. Cluster Analysis of both
explicit (given) or implicit (discovered) data relationships in “Big Data” is a critical
technique which attempts to explain the nature, cause and effect of the forces which drive
clustering. Any observed profiled data similarities – geographic or temporal aggregations,
mathematical or statistical distributions – may be explained through Causal Layer Analysis.
• Cluster Analysis is a technique used to explore content and information in order to
understand very large volumes of data in every business and scientific field that collects
and processes vast quantities of machine generated (automatic) data
– Choice of clustering algorithm and parameters are processes and data dependent
– Approximate Kernel K-means provides a good trade-off between clustering accuracy
and data volumes, throughput, performance and scalability
– Challenges include homogeneous and heterogeneous data (structured versus
unstructured data), data quality, streaming, scalability, cluster cardinality and validity

Cluster Types
Deep Space Galactic Clusters
Hadoop Cluster – “Big Data” Servers
Molecular Clusters
Geo-Demographic Clusters
Crystal Clusters

Cluster Types
DISCIPLINE CLUSTER TYPE CLUSTERS DIMENSIONS DATA TYPE DATA SOURCE CLUSTERING
FACTORS / FORCES
Astrophysics Distribution of Matter
through the Universe
across Space and Time
Star Systems
Stellar Clusters
Galaxies
Galactic Clusters
Mass / Energy
Space / Time
Astronomy Images Optical Telescope
Infrared Telescope
Radio Telescope
X-ray Telescope
Gravity
Dark Matter
Dark Energy
Climate Change Temperature Changes
Precipitation Changes
Ice-mass Changes
Hot / Cold
Dry / Wet
More / Less ice
Temperature
Precipitation
Sea / Land Ice
Average Temperature
Average Precipitation
Greenhouse Gases %
Weather Station Data
Ice Core Data
Tree-ring Data
Solar Forcing
Oceanic Forcing
Atmospheric Forcing
Actuarial Science
Morbidity
Epidemiology
Place / Date of birth
Place / Date of death
Cause of Death
Birth / Death
Longevity
Cause of Death
Medical Events
Geography
Time
Biomedical Data
Demographic Data
Geographic data
Register of Births
Register of Deaths
Medical Records
Health
Wealth
Demographics
Price Curves
Economic Modelling
Long-range Forecasting
Economic growth
Economic recession
Bull markets
Bear markets
Monetary Value
Geography
Time
Real (Austrian) GDP
Foreign Exchange Rates
Interest Rates
Price movements
Daily Closing Prices
Government
Central Banks
Money Markets
Stock Exchange
Commodity Exchange
Business Cycles
Economic Trends
Market Sentiment
Fear and Greed
Supply / Demand
Business Clusters Retail Parks
Digital / Fin Tech
Leisure / Tourism
Creative / Academic
Retail
Technology
Resorts
Arts / Sciences
Company / SIC
Geography
Time
Entrepreneurs
Start-ups
Mergers
Acquisitions
Investors
NGAs
Government
Academic Bodies
Capital / Finance
Political policy
Economic policy
Social policy
Elite Team Sports
Performance Science
Winners
Loosens
Team / Athlete
Sport / Club
League Tables
Medal Tables
Sporting Events
Team / Athlete
Sport / Club
Geography
Time
Performance Data
Biomedical Data
Sports Governing Bodies
RSS News Feeds
Social Media
Hawk-Eye
Pro-Zone
Technique
Application
Form / Fitness
Ability / Attitude
Training / Coaching
Speed / Endurance3
Future Management Human Activity
Natural Events
Random Events
Waves, Cycles,
Patterns, Trends
Random Events
Geography
Time
Weak Signals
Wild Card Events
Black Swan Events
Global Internet Content /
Big Data Analytics -
Horizon Scanning,
Tracking and Monitoring
Random Events
Waves, Cycles,
Patterns, Trends,
Extrapolations

World-wide Visitor Count – GIS Mapping

Geo-Demographic Profile Data
GEODEMOGRAPHIC INFORMATION – PEOPLE and PLACES
Age Dwelling Location / Postcode
Income Dwelling Owner / Occupier Status
Education Dwelling Number-of-rooms
Social Status Dwelling Type
Marital Status Financial Status
Gender / Sexual Preference Politically Active Indicator
Vulnerable / At Risk Indicator Security / Threat Indicator
Physical / Mental Health Status Security Vetting / Criminal Record Indicator
Immigration Status Profession / Occupation
Home / First language Professional Training / Qualifications
Race / ethnicity / country of origin Employment Status
Household structure and family members Employer SIC
Leisure Activities / Destinations Place of work / commuting journey
Mode of travel to / from Leisure Activities Mode of travel to / from work

Geo-Demographic Profile Clusters

Star
Clusters
• New and
improved
understanding
of star cluster
physics brings
us within reach
of answering a
number of
fundamental
questions in
astrophysics,
ranging from
the formation
and evolution
of galaxies –
to intimate
details of the
star formation
process itself.

Hertzsprung
Russell
• The Hertzsprung
Russell diagram is a
scatter plot Cluster
Diagram which shows
the Main Sequence
Stellar Lifecycles.
• A Hertzsprung Russell
diagram is a scatter
plot Stellar Cluster
Diagram which
demonstrates the
relationship between a
stars temperature and
luminosity over time –
using red to blue colour
to indicate the mean
temperature at the
surface of the star.

Star
Clusters
• The Physics of star
clustering leads us
to new questions
related to the
make-up of stellar
clusters and
galaxies, stellar
populations in
different types of
galaxy, and the
relationships
between high-
stellar populations
and local clusters –
overall, resolved
and unresolved –
the implications
for their relative
formation times
and galactic star-
formation histories.

Cluster Analysis
• Data Representation
– Metadata - identifying common Data Objects, Types and Formats
• Data Taxonomy and Classification
– Similarity Matrix (labelled data)
– Grouping of explicit data relationships
• Data Audit - given any collection of labelled objects.....
– Identifying relationships between discrete data items
– Identifying common data features - values and ranges
– Identifying unusual data features - outliers and exceptions
• Data Profiling and Clustering - given any collection of unlabeled objects.....
– Pattern Matrix (unlabelled data)
– Discover implicit data relationships
– Find meaningful groupings (Clusters)
– Predictive Analytics – Event Forecasting
– Wave-form Analytics – Periodicity, Cycles and Trends
– Explore hidden relationships between discrete data features
Many big data problems feature unlabeled objects

Distributed
Clustering Models
Number of
processors
Speedup Factor
- K-means
Speedup Factor
- Kernel K-means
2 1.1 1.3
3 2.4 1.5
4 3.1 1.6
5 3.0 3.8
6 3.1 1.9
7 3.3 1.5
8 1.2 1.5
K-means
Kernel K -means
Clustering 100,000 2-D points with 2 clusters on 2.3 GHz quad-core
Intel Xeon processors, with 8GB memory in intel07 cluster
Network communication cost increases with the no. of processors

Cluster Analysis
Clustering Algorithms
Hundreds of spatial, mathematical and statistical clustering algorithms are available –
many clustering algorithms are “admissible” – but no single algorithm alone is “optimal”
• K-means
• Gaussian mixture models
• Kernel K-means
• Spectral Clustering
• Nearest neighbour
• Latent Dirichlet Allocation
Challenges in “Big Data” Clustering
• Data quality
• Volume – number of data items
• Cardinality – number of clusters
• Synergy – measures of similarity
• Values – outliers and exceptions
• Cluster accuracy - validity and verification
• Homogeneous versus heterogeneous data (structured and unstructured data)

k-means/Gaussian-Mixture Clustering of Audio Segments

Distributed Clustering Model Performance
Clustering 100,000 2-D points with 2 clusters on 2.3 GHz quad-core
Intel Xeon processors, with 8GB memory in intel07 cluster
Network communication cost increases with the no. of processors
K-means Kernel K -means

HPCC Clustering Models
High Performance / High Concurrence Real-time Delivery (HPCC)

Distributed Clustering Model Performance
Distributed Approximate Kernel K-means
2-D data set with 2 concentric circles
2.3 GHz quad-core Intel Xeon processors, with 8GB memory in intel07 cluster
Run-time
Size of
dataset
(no. of
Records)
Benchmark
Performance
(Speedup
Factor )
10K 3.8
100K 4.8
1M 3.8
10M 6.4

Informatica / Hortonworks Vibe

Big Data – Products
The MapReduce technique has spilled over into many other disciplines that process vast
quantities of information including science, industry, and systems management. The Apache
Hadoop Library has become the most popular implementation of MapReduce – with
framework implementations from Cloudera, Hortonworks and MAPR

Split-Map-Shuffle-Reduce Process
Big Data
Consumers
Split Map Shuffle Reduce
Key / Value Pairs Actionable InsightsData Provisioning Raw Data

Apache Hadoop Component Stack
HDFS
MapReduce
Pig
Zookeeper
Hive
HBase
Oozie
Mahoot
Hadoop Distributed File System (HDFS)
Scalable Data Applications Framework
Procedural Language – abstracts low-level MapReduce operators
High-reliability distributed cluster co-ordination
Structured Data Access Management
Hadoop Database Management System
Job Management and Data Flow Co-ordination
Scalable Knowledge-base Framework

Data Management Component Stack
Informatica
Drill
Millwheel
Informatica Big Data Edition / Vibe Data Stream
Data Analysis Framework
Data Analytics on-the-fly + Extract – Transform – Load Framework
Flume
Sqoop
Scribe
Extract – Transform - Load
Talend Extract – Transform - Load
Pentaho Extract – Transform – Load Framework + Data Reporting on-the-fly

Big Data Storage Platforms
Autonomy
Vertica
MongoDB
HP Unstructured Data DBMS
HP Columnar DBMS
High-availability DBMS
CouchDB
Couchbase Database Server for Big Data with NoSQL / Hadoop
Integration
Pivotal Pivotal Big Data Suite – GreenPlum, GemFire, SQLFire, HAWQ
Cassandra
Cassandra Distributed Database for Big Data with NoSQL and
Hadoop Integration
NoSQL NoSQL Database for Oracle, SQL/Server, Couchbase etc.
Riak
Basho Technologies Riak Big Data DBMS with NoSQL / Hadoop
Integration

Big Data Analytics Engines and Appliances
Alpine
Karmasphere
Kognito
Alpine Data Studio - Advanced Big Data Analytics
Karmasphere Studio and Analyst – Hadoop Customer Analytics
Kognito In-memory Big Data Analytics MPP Platform
Skytree
Redis
Skytree Server Artificial Intelligence / Machine Learning Platform
Redis is an open source key-value database for AWS, Pivotal etc.
Teradata Teradata Appliance for Hadoop
Neo4j Crunchbase Neo4j - Graphical Database for Big Data
InfiniDB Columnar MPP open-source DB version hosted on GitHub
Big Data Analytics Engines / Appliances

Big Data Analytics and Visualisation Platforms
Tableaux Tableaux - Big Data Visualisation Engine
Eclipse Symentec Eclipse - Big Data Visualisation
Mathematica Mathematical Expressions and Algorithms
StatGraphics Statistical Expressions and Algorithms
FastStats Numerical computation, visualization and programming toolset
MatLab
R
Data Acquisition and Analysis Application Development Toolkit
“R” Statistical Programming / Algorithm Language
Revolution Revolution Analytics Framework and Library for “R”

Hadoop / Big Data Extended Infrastructure Stack
SSD Solid State Drive (SSD) – configured as cached memory / fast HDD
CUDA CUDA (Compute Unified Device Architecture)
GPGPU GPGPU (General Purpose Graphical Processing Unit Architecture)
IMDG IMDG (In-memory Data Grid – extended cached memory)
Vibe
Splunk
High Velocity / High Volume Machine / Automatic Data Streaming
High Velocity / High Volume Machine / Automatic Data Streaming
Ambari High-availability distributed cluster co-ordination
YARN Hadoop Resource Scheduling
Big Data Extended Architecture Stack

Cloud-based Big-Data-as-a-Service and Analytics
AWS
Amazon Web Services (AWS) – Big Data-as-a-Service (BDaaS)
Elastic Compute Cloud (ECC) and Simple Storage Service (S3)
1010 Data Big Data Discovery, Visualisation and Sharing Cloud Platform
SAP HANA SAP HANA Cloud - In-memory Big Data Analytics Appliance
Azure Microsoft Azure Data-as-a-Service (DaaS) and Analytics
Anomaly 42 Anomaly 42 Smart-Data-as-a-Service (SDaaS) and Analytics
Workday Workday Big-Data-as-a-Service (BDaaS) and Analytics
Google Cloud
Google Cloud Platform – Cloud Storage, Compute Platform,
Firebrand API Resource Framework
Apigee Apigee API Resource Framework

Gartner Magic Quadrant for BI and Analytics Platforms

Hadoop Framework Distributions
FEATURE Hortonworks Cloudera MAPR Pivotal
Open Source Hadoop Library Yes Yes Yes Pivotal HD
Support Yes Yes Yes Yes
Professional Services Yes Yes Yes Yes
Catalogue Extensions Yes Yes Yes Yes
Management Extensions Yes Yes Yes
Architecture Extensions Yes Yes
Infrastructure Extensions Yes Yes
Library
Support
Services
Catalogue
Job Management
Library
Support
Services
Catalogue
Hortonworks Cloudera MAPR
Library
Support
Services
Catalogue
Job Management
Resilience
High Availability
Performance
Pivotal
Library
Support
Services
Catalogue
Job Management
Resilience
High Availability
Performance

Data Warehouse Appliance / Real-time
Analytics Engine Price Comparison
Manufacturer
Server
Configuration
Cached Memory
Server
Type
Software
Platform
Cost (est.)
SAP HANA
(BI, BO, BW)
32-node (4
Channels x 8 CPU)
1.3 Terabytes SMP Proprietary $ 6,000,000
Teradata 20-node (2
Channels x 10 CPU)
1 Terabyte MPP Proprietary $ 1,000,000
Netezza
(now IBM)
20-node (2
Channels x 10 CPU)
1 Terabyte MPP Proprietary $ 180,000
IBM ex5 (non-HANA
configuration)
32-node (4
Channels x 8 CPU)
1.3 Terabytes SMP Proprietary $ 120,000
Greenplum (now
Pivotal)
20-node (2
Channels x 10 CPU)
1 Terabyte MPP Open Source $ 20,000
XtremeData xdb 20-node (2
Channels x 10 CPU)
1 Terabyte MPP Open Source $ 18,000
Zybert Gridbox 48-node (4
Channels x 12 CPU)
20 Terabytes SMP Open Source $ 60,000

• Abiliti: Origin Automation is part of a global consortium of Digital Technologies Service
Providers and Future Management Strategy Consulting firms – Digital Marketing and
Multi-channel Retail / Cloud Services / Mobile Devices / Big Data / Social Media
• Graham Harris Founder and MD @ Abiliti: Future Systems
– Email: Abiliti@Originautomation.com (Office)
– Telephone: +44 (0) 121 445 4614 (Office)
• Nigel Tebbutt 奈杰尔泰巴德
– Future Business Models & Emerging Technologies @ Abiliti: Future Systems
– Telephone: +44 (0) 7832 182595 (Mobile)
– +44 (0) 121 445 5689 (Office)
– Email: Nigel-Tebbutt@hotmail.com (Private)
• Ifor Ffowcs-Williams CEO, Cluster Navigators Ltd & Author, “Cluster Development”
– Address : Nelson 7010, New Zealand (Office)
– Email : e4@clusternavigators.com
Abiliti: Origin Automation Strategic Enterprise Management (SEM) Framework ©
Cluster Theory - Expert Commentary: -

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