1) The document discusses the limitations of rationalist, linear models for understanding complex systems like infrastructure, ecosystems, health care and economics that have adaptive, evolving components.
2) It argues that these systems cannot be fully understood or predicted using reductionist, "exact science" approaches and notes problems that have arisen from assuming universality and transportability of models.
3) The author calls for new approaches that acknowledge complexity, uncertainty, context and local interactions, including new epistemologies, agent-based models, and engagement with moral philosophy and political economy.
Physics Serway Jewett 6th edition for Scientists and Engineers
SMART Seminar Series: "Unsimple truths..."
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
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!!