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PERSONALISED EXPERIENCES : WORLD-CLASS RESULTSAN INTERNATIONALLEADER IN APPLIED INFRASTRUCTURERESEARCH
UNSIMPLE TRUTHS…..
Graham Harris
UoW SMART Infrastructure Facility
FBA, Windermere, UK
AN INTERNATIONALLEADER IN APPLIED INFRASTRUCTURERESEARCH
Drawing on experience from
LEC UK, DEFRA and DTC
science evidence policy
plans actions impacts benefits
outputs outcomes
PREDICT ACT BENEFITS
EVIDENCE-BASED
POLICY WANTS THESE
EVERYBODY
REPORTS THESE
? EVIDENCE
“SYSTEM” MODELS EVIDENCE
inputs
THE RATIONALIST
“SCIENCE PUSH”
VIEW
THE KNOWLEDGE-BASED LINEAR MODEL – “The Faustian bargain”
Perverse outcomes with….
• Ecosystems, watersheds, climate change
– Environmental flows, river restoration
• Systems Biology, Genomics, GWAS
– Medicine, health care, big pharma
• Infrastructure, energy, transport, water, IT
• Economics: the GFC, M&A for corporations
• Warfare, strategy, nation states
• In fact anything which has living, reflexive,
behaving, adaptive, evolving components!
complexity
Engineering
Physics
Scientific
management
Known
Knowns
Directives
Adaptive
management
Bayesian
statistics
Frequentist
statistics
Precautionary
Principle
Unknown
Unknowns
Robust
Decision
Making
Ecology
Climate
Rivers
Infrastructure
Complete
Uncertainty
Complete
certainty
Causal thickets
“Black Swans”
EXACT SCIENCES
Science and society 21st Century
• Scientific method reveals axiomatic “laws” of
Nature (process of abstraction – externalities)
– Cause-effect deduced from axiomatic laws:
• Evidence and refutation drive new knowledge:
strong inference: adaptive management
– Rationalist, realist, materialist worldview – science,
engineering, economics
• Physics envy, liberal humanism, sociology,
economics: naïve realism
– Risk assessments: CGE models, finance, the GFC
– Individualism, markets, instrumental reason
– Market-based instruments, biodiversity offsets
The “framing issue”
• Managers and scientists assume can apply an EXACT
SCIENCE; rationalism, mathematics=causes, realism
– Theory, data collection and analysis issues
• Built-in bias in expert opinion (e.g. economists)
• Science-based normative framework, “noise” is averaged
– Universality and transportability
• Philosophical basis is idealised (Wimsatt, Mitchell)
– Not appropriate for complex systems (Ulanowicz)
– Science underestimates uncertainty (Wynne)
• JONAH’S 1ST LAW, world is predictable but can’t be changed
Plans and strategies
• It’s top down planning and strategies that fail
• Largely due to mind-set of naïve realists and
rationalists
• Something – COMPLEXITY - messes up our
mind-set for universality, predict-act
• Planning, strategy, institutions, economics,
politics
complexity
Engineering
Physics
Scientific
management
Known
Knowns
Directives
Adaptive
management
Bayesian
statistics
Frequentist
statistics
Precautionary
Principle
Unknown
Unknowns
Robust
Decision
Making
Ecology
Climate
Rivers
Infrastructure
Complete
Uncertainty
Complete
certainty
Causal thickets
“Black Swans”
Biology
SOCIETY
Systems
Science
NO (few) UNIVERSAL LAWS
THE SPECIAL SCIENCES
The “special sciences” deal with a contextually
different set of problems
(the humanities realise this)
they are “complex” and are about formation – the formation of
structures – and how this formation affects the objects causing it.
Non-equilibrium, reflexivity, context, information
To use tools based on “physics envy” makes what in
philosophy is called a category error
e.g. economics is about rationalist models of allocation
and not the formation of markets
So what is complex?
• Driven “bottom up” by localism
• Plesionic – neighbours, trust, cooperation
• 2nd order interactions – reflexive
• 2nd and 3rd thoughts: “thinking about
thinking”
• No system without observer (Luhmann)
• System defines environment
• Key role for reflexive social interactions
Kirchner and Neal (2013) PNAS: fractal water chemistry
Non stationarity
– normal statistics do not exist
Distributed robustness (Wagner)
• Robustness is like a ball rolling about in a basin of attraction.
• The position of the ball is not static.
• Life exploits the adjacent possible. Equifinality is the norm.
• Many complementary networks and options
• Robustness involves the whole of the system, provides long
term security and solutions, is self-regulating or self
organising, actively exploits variability, is persistent, and is
only amenable to indirect management.
• Monitoring will not provide evidence of outcomes; statistical
power will be low. Surprises will be expected.
• NOT EFFICIENT, NOT OPTIMAL, NOT PREDICTABLE
So we have a problem with “weak
inference”… (low power)
There is no safe methodology of
induction (Goodman, 1978)
The problem with “Black Swans”
Unkown unknowns will always exist
And everyone has a different perspective so
evidence is hard to come by and it debatable
Meaning and intention
• Sampling the statistical properties of their
environment by local agents (hence ABMs)
– No central processor: no central “meaner”
• Sampling requires both randomness and
probabilities to explore and exploit the world
– Meaning in the context of intentional behaviour
• Metabolism, immune system, eco-systems,
society, cars/drivers…
– Uncertainty, noise, information, meaning, context,
design: what is a system??
Life computes
• The questions become:
– How much information is stored?
– In what architecture?
– How is new behaviour produced?
• Big data and statistics will not do:
adaptive recursion (structure 
function) and equifinality
• Life is anti-fragile and “infinitely
stupid”: distributed robustness
• Life computes but is not computable
New physics
• New physics of networks, patterns and
information; limits of predictability
– What information is stored? What does it mean?
• Entirely new approaches to computation
– Agent based models, meta-statistics, meta-models
– Discrete, sequential dynamical systems
• Advances in physics, theoretical computation
– Not being picked up in important areas of biology,
economics, infrastructure (except Wagner)
– New work in systems biology, some in genomics
• Uncertainties, Cal/Val problems, scenarios
So maybe….
• We admit that these problems are complex and
qualitatively different from the “old physics”
– Models and predictions will always be flawed
– We try to explore the limits of knowledge
• We urgently seek new epistemologies, “framings”
that lead to greater understanding
• We must also rethink the social, institutional,
economic, political and moral aspects of this
– A weakening of “scientific” (rationalist) authority and
a collaboration with a new political economy
We have made the mistake of
assuming that social engagement
was all we needed to do…. There’s
more to it that that..
The risks are underestimated and we have
no ethical framework by which to set values
(other than money!)
Contextualisation and meaning imply a
concern for moral philosophy
So we need to rethink our political economy
Rail
Government
science
Society
Trusts, CMAs
NGOsCRCs
Devolution
States
Regional Utilities Water Companies
Local Govts
Commonwealth
Self organisation
Philanthropy
Subsidiarity
Double
democratisation
Community solar PV
Water Trusts
Data for robust local decisions is not the same as data for (realist) modelling and prediction
Road/rail
New infrastructure/technologies
• Exploit the “high frequency wave of the future”
(Kirchner) – self-org, socio-technical systems
• Web-based tools: science in the hands of the
community e.g. OPAL UK, Peta-Jakarta
• e.g. iphones as sensors, GridStix, acoustic sensors,
motes, GPS, RFD tags, cameras
• Distributed expertise, extend and democratize science
• Pluralism trumps expertise when uncertain
• Society is the de facto regulator anyway
– Ulrich Beck “Remodernisation”, sub-politics,
How do we live?
• Predominantly liberal political economy
– Lack of ethics in debate – corporatism,
utilitarianism “whatever works”, market-
based solutions
• Individualism – “rational individuals”
– Predominant emotivism: just preferences
• How to deal with “me and us” and with
“us and them”? A dumbed down debate
– Tragedy of the moral commons – “me” first
– Debate about “how do we live”
• Moral problem solving: imaginative moral
deliberation: new problems, new solutions
Virtue
• Classical idea from Homer, Aristotle
• Virtue linked to the way life is lived in a
social context – the Greek polis and telos
– The concept of “good” and the good life
– Denied by individualism; utilitarian and
instrumental view of life which “uses”
others: “we have lost our virtue”
So virtue is linked to intrinsic values both of Nature
and of human life: and to the telos
– Moral practice: not individuals but social
animals – higher level challenge
Artifactual structure (North)
• Effective use of institutions
• Layered governance
– Self organised communty groups
– Review of incentives, market structures,
– institutional, legal (constitutive) design
– Innovation, subsidiarity, fast failure,
• Define the rules of the game and who can play
– Consider issues of moral philosophy and engagement
• Redefine “reform” – agents and constraints
TACKLING THE ASYMMETRY
Past revolutions: PPE
• Three and a half revolutions
– Thomas Hobbes: the nation state,
Leviathan (1651)
– JS Mill: the liberal state (1859)
– Beatrice Webb: the welfare state
(1910 – followed by 2 World Wars)
– Thatcher & Reagan (c. 1980 – present)
• Throwback to liberalism
• Individuals and their values seen as
formed prior to social interactions
– Perhaps liberalism has “met its
Waterloo” with global constraints?
The 4th revolution
• Balancing material, social, moral and
environmental goods and services
– Recovering the full economic, social,
environmental and aesthetic benefits
– New SO intermediaries are arising from
the bottom up, coupled to technology
(SOST)
– Paying compensation, respecting
constraints, conserving moral goods
– A discourse to tackle to tragedy
of the social and moral commons
• Between “left” and “right”- smaller
government but not none.. Merger of
new science and new political economy
Infrastructure as art?
Beyond “post-normal” science
Beyond “citizen science”
Beyond “anything goes”
Philosophical objects
Beauty, design, values
Evaluation and response
Community engagement
Moral philosophy, aesthetics
Political engagement
Redefine “reform”
We don’t know how the future
will unfold….
But we can change it!!

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  • 1. PERSONALISED EXPERIENCES : WORLD-CLASS RESULTSAN INTERNATIONALLEADER IN APPLIED INFRASTRUCTURERESEARCH UNSIMPLE TRUTHS….. Graham Harris UoW SMART Infrastructure Facility FBA, Windermere, UK AN INTERNATIONALLEADER IN APPLIED INFRASTRUCTURERESEARCH Drawing on experience from LEC UK, DEFRA and DTC
  • 2. science evidence policy plans actions impacts benefits outputs outcomes PREDICT ACT BENEFITS EVIDENCE-BASED POLICY WANTS THESE EVERYBODY REPORTS THESE ? EVIDENCE “SYSTEM” MODELS EVIDENCE inputs THE RATIONALIST “SCIENCE PUSH” VIEW THE KNOWLEDGE-BASED LINEAR MODEL – “The Faustian bargain”
  • 3. Perverse outcomes with…. • Ecosystems, watersheds, climate change – Environmental flows, river restoration • Systems Biology, Genomics, GWAS – Medicine, health care, big pharma • Infrastructure, energy, transport, water, IT • Economics: the GFC, M&A for corporations • Warfare, strategy, nation states • In fact anything which has living, reflexive, behaving, adaptive, evolving components!
  • 5. Science and society 21st Century • Scientific method reveals axiomatic “laws” of Nature (process of abstraction – externalities) – Cause-effect deduced from axiomatic laws: • Evidence and refutation drive new knowledge: strong inference: adaptive management – Rationalist, realist, materialist worldview – science, engineering, economics • Physics envy, liberal humanism, sociology, economics: naïve realism – Risk assessments: CGE models, finance, the GFC – Individualism, markets, instrumental reason – Market-based instruments, biodiversity offsets
  • 6. The “framing issue” • Managers and scientists assume can apply an EXACT SCIENCE; rationalism, mathematics=causes, realism – Theory, data collection and analysis issues • Built-in bias in expert opinion (e.g. economists) • Science-based normative framework, “noise” is averaged – Universality and transportability • Philosophical basis is idealised (Wimsatt, Mitchell) – Not appropriate for complex systems (Ulanowicz) – Science underestimates uncertainty (Wynne) • JONAH’S 1ST LAW, world is predictable but can’t be changed
  • 7. Plans and strategies • It’s top down planning and strategies that fail • Largely due to mind-set of naïve realists and rationalists • Something – COMPLEXITY - messes up our mind-set for universality, predict-act • Planning, strategy, institutions, economics, politics
  • 9. The “special sciences” deal with a contextually different set of problems (the humanities realise this) they are “complex” and are about formation – the formation of structures – and how this formation affects the objects causing it. Non-equilibrium, reflexivity, context, information To use tools based on “physics envy” makes what in philosophy is called a category error e.g. economics is about rationalist models of allocation and not the formation of markets
  • 10. So what is complex? • Driven “bottom up” by localism • Plesionic – neighbours, trust, cooperation • 2nd order interactions – reflexive • 2nd and 3rd thoughts: “thinking about thinking” • No system without observer (Luhmann) • System defines environment • Key role for reflexive social interactions
  • 11. Kirchner and Neal (2013) PNAS: fractal water chemistry Non stationarity – normal statistics do not exist
  • 12. Distributed robustness (Wagner) • Robustness is like a ball rolling about in a basin of attraction. • The position of the ball is not static. • Life exploits the adjacent possible. Equifinality is the norm. • Many complementary networks and options • Robustness involves the whole of the system, provides long term security and solutions, is self-regulating or self organising, actively exploits variability, is persistent, and is only amenable to indirect management. • Monitoring will not provide evidence of outcomes; statistical power will be low. Surprises will be expected. • NOT EFFICIENT, NOT OPTIMAL, NOT PREDICTABLE
  • 13. So we have a problem with “weak inference”… (low power) There is no safe methodology of induction (Goodman, 1978) The problem with “Black Swans” Unkown unknowns will always exist And everyone has a different perspective so evidence is hard to come by and it debatable
  • 14. Meaning and intention • Sampling the statistical properties of their environment by local agents (hence ABMs) – No central processor: no central “meaner” • Sampling requires both randomness and probabilities to explore and exploit the world – Meaning in the context of intentional behaviour • Metabolism, immune system, eco-systems, society, cars/drivers… – Uncertainty, noise, information, meaning, context, design: what is a system??
  • 15. Life computes • The questions become: – How much information is stored? – In what architecture? – How is new behaviour produced? • Big data and statistics will not do: adaptive recursion (structure  function) and equifinality • Life is anti-fragile and “infinitely stupid”: distributed robustness • Life computes but is not computable
  • 16. New physics • New physics of networks, patterns and information; limits of predictability – What information is stored? What does it mean? • Entirely new approaches to computation – Agent based models, meta-statistics, meta-models – Discrete, sequential dynamical systems • Advances in physics, theoretical computation – Not being picked up in important areas of biology, economics, infrastructure (except Wagner) – New work in systems biology, some in genomics • Uncertainties, Cal/Val problems, scenarios
  • 17. So maybe…. • We admit that these problems are complex and qualitatively different from the “old physics” – Models and predictions will always be flawed – We try to explore the limits of knowledge • We urgently seek new epistemologies, “framings” that lead to greater understanding • We must also rethink the social, institutional, economic, political and moral aspects of this – A weakening of “scientific” (rationalist) authority and a collaboration with a new political economy
  • 18. We have made the mistake of assuming that social engagement was all we needed to do…. There’s more to it that that.. The risks are underestimated and we have no ethical framework by which to set values (other than money!) Contextualisation and meaning imply a concern for moral philosophy So we need to rethink our political economy
  • 19. Rail Government science Society Trusts, CMAs NGOsCRCs Devolution States Regional Utilities Water Companies Local Govts Commonwealth Self organisation Philanthropy Subsidiarity Double democratisation Community solar PV Water Trusts Data for robust local decisions is not the same as data for (realist) modelling and prediction Road/rail
  • 20. New infrastructure/technologies • Exploit the “high frequency wave of the future” (Kirchner) – self-org, socio-technical systems • Web-based tools: science in the hands of the community e.g. OPAL UK, Peta-Jakarta • e.g. iphones as sensors, GridStix, acoustic sensors, motes, GPS, RFD tags, cameras • Distributed expertise, extend and democratize science • Pluralism trumps expertise when uncertain • Society is the de facto regulator anyway – Ulrich Beck “Remodernisation”, sub-politics,
  • 21. How do we live? • Predominantly liberal political economy – Lack of ethics in debate – corporatism, utilitarianism “whatever works”, market- based solutions • Individualism – “rational individuals” – Predominant emotivism: just preferences • How to deal with “me and us” and with “us and them”? A dumbed down debate – Tragedy of the moral commons – “me” first – Debate about “how do we live” • Moral problem solving: imaginative moral deliberation: new problems, new solutions
  • 22. Virtue • Classical idea from Homer, Aristotle • Virtue linked to the way life is lived in a social context – the Greek polis and telos – The concept of “good” and the good life – Denied by individualism; utilitarian and instrumental view of life which “uses” others: “we have lost our virtue” So virtue is linked to intrinsic values both of Nature and of human life: and to the telos – Moral practice: not individuals but social animals – higher level challenge
  • 23. Artifactual structure (North) • Effective use of institutions • Layered governance – Self organised communty groups – Review of incentives, market structures, – institutional, legal (constitutive) design – Innovation, subsidiarity, fast failure, • Define the rules of the game and who can play – Consider issues of moral philosophy and engagement • Redefine “reform” – agents and constraints TACKLING THE ASYMMETRY
  • 24. Past revolutions: PPE • Three and a half revolutions – Thomas Hobbes: the nation state, Leviathan (1651) – JS Mill: the liberal state (1859) – Beatrice Webb: the welfare state (1910 – followed by 2 World Wars) – Thatcher & Reagan (c. 1980 – present) • Throwback to liberalism • Individuals and their values seen as formed prior to social interactions – Perhaps liberalism has “met its Waterloo” with global constraints?
  • 25. The 4th revolution • Balancing material, social, moral and environmental goods and services – Recovering the full economic, social, environmental and aesthetic benefits – New SO intermediaries are arising from the bottom up, coupled to technology (SOST) – Paying compensation, respecting constraints, conserving moral goods – A discourse to tackle to tragedy of the social and moral commons • Between “left” and “right”- smaller government but not none.. Merger of new science and new political economy
  • 26. Infrastructure as art? Beyond “post-normal” science Beyond “citizen science” Beyond “anything goes” Philosophical objects Beauty, design, values Evaluation and response Community engagement Moral philosophy, aesthetics Political engagement Redefine “reform”
  • 27. We don’t know how the future will unfold…. But we can change it!!