The document discusses dealing with uncertainty when making decisions about complex systems. It argues that traditional scientific approaches based on rational calculation and predictive modeling are inadequate for addressing real-world problems involving living systems, people, and interconnected risks. True uncertainty arises from complex, adaptive phenomena that cannot be reduced to simple cause-and-effect relationships or predicted with statistical models. Decision-making must account for the observer's role, embodiment, distributed robustness of living systems, and ethics. Flexible, participatory approaches are needed instead of top-down scientific management.

1. Dealing with uncertainty:
with the observer in the loop
Graham Harris
UOW SMART Infrastructure Facility

2. complexity
Engineering
Physics
Scientific
management
Known
Knowns
Directives
Adaptive
management
Bayesian
statistics
Frequentist
statistics
Precautionary
Principle
Unknown
Unknowns
Robust
Decision
Making
Ecology
Climate
Rivers Complete
Uncertainty
Complete
certainty
THE PHYSICAL STANCE
Causal thickets
“Black Swans”
EXACT SCIENCES
Level 1 Disciplinary science

3. Scientific assumptions
• Cartesian dualism, realism, rationalism, value-free
representation: equilibrium, optimality
– Ergodicity, finality, universal “laws”, predictability, regular
statistical properties, evidence directs actions
• This is physics, but “physics envy” infects all disciplines:
cognitive science, psychology, social science, economics
etc.
– The “senior science” vs the rest: framing issue
• Science is about explanation

4. Risk management
• THE SCIENTIFIC MANAGEMENT APPROACH
Simple, risks
Quantifiable
Risk statistics
Insurance

5. By Junkyardsparkle - Own work, CC BY-SA 3.0,
https://commons.wikimedia.org/w/index.php?curid=31494791
Infrastructure works to reduce variability and risks

6. Effect on systems view
• Since mid 1980s “modernist” view dominates systems
thinking about infrastructure and natural world:
environmental management
• Role of science in decision making, efficiency, pollution
control, “cap and trade”
• Sustainable development, recycling, tech innovation
• Now role of Web, IoT, social media

7. Neoliberal value capture
• Primacy of individuals: Thatcher and Regan
• Smaller governments
• Privatisation of public assets, PPPs
• Growth, wealth and profit before other values
• Innovation: the GUI – capture of public R&D
• Financialisation of everything
• Inequality – dominance by oligopolies

8. Zaha Hadid: Heydar Aliev Centre, Baku, Azerbaijan. Design of the Year, 2014

9. Failures of scientific management
• Failure of major projects
– Delays, cost overruns: engineering, software
– Documented by Flyvberg et al
• EU WFD and major env. management projects
– Murray Darling river system: no outcomes
• Also science papers: retractions, repeatability
– Ioannidis et al – medicine, psychology, genomics
• Need Foucaultian discourse analysis
– History and geography of ideas (Hajer, 1995)

10. Uncertainty: complex risks
• Planetary, climate, environment, disease
• Resources, energy, food, water (Cape Town)
• Economic, markets, inequality (the GFC)
• Political, nuclear warfare (N. Korea)
• Technology innovation, AI, robotics
• Legal, financial, PPPs (Constitutive rules)
– Major issues of Politics and Governance
• Realism: there is a “World in itself” but it runs on different
rules

11. Inflows to Perth water supply 1911-2016: Gl
Stationarity is dead!

12. Allenby & Sarewitz (2011)
• Level 1- low risk, technological fixes, largely isolated from
environment and people
• Level 2 – manageable systemic risk, adaptive management,
evidence
• Level 3 – complex, uncertain, “black swans”, recursive,
complex, life, people, society, economics… the real world
• Science uses Level 1 tools on level 3 problems and makes a
category error

13. Uncertainty: connected systems
• Rational calculation drives to optimal solutions and efficiency
(least cost, highest profitability)
– Not robust: interconnectedness, interaction, low
resilience, fragility (“hard” systems)
• Interaction with living world adds further uncertainty: science
underestimates risks
– Life is different: (“soft” systems)
• Jonah’s 2nd law: the future is unpredictable, but we can
change it (we need to!)

14. complexity
Engineering
Physics
Scientific
management
Known
Knowns
Directives
Adaptive
management
Bayesian
statistics
Frequentist
statistics
Precautionary
Principle
Unknown
Unknowns
Robust
Decision
Making
Ecology
Climate
Rivers Complete
Uncertainty
Complete
certainty
THE PHYSICAL STANCE
Causal thickets
“Black Swans”
EXACT SCIENCES
Level 1 Disciplinary science

15. Kirchner and Neal (2013) PNAS: fractal water chemistry
Non stationarity – normal statistics do not exist

16. Problems with big data
• Uncertainty: Low statistical power (if any!)
– More data = more questions. No trend or outcome
• Much data 1/f power law distributed: no mean or SD
• Cause and effect buried in complex network structures: failure
of predict - act
– Retractions and corrigenda in MS
• No magic bullets: drugs, dementia, big pharma, environmental
“management”
– Life = distributed robustness

17. complexity
Engineering
Physics
Scientific
management
Known
Knowns
Directives
Adaptive
management
Bayesian
statistics
Frequentist
statistics
Precautionary
Principle
Unknown
Unknowns
Robust
Decision
Making
Ecology
Climate
Rivers Complete
Uncertainty
Complete
certainty
Causal thickets
“Black Swans”
SPECIAL SCIENCES
Uncertain, inter-disciplinary level 3 problems
THE LIVING
WORLD
THE HUMAN WORLD

18. Uncertainty (Life 1): cognition
• Put living observer in the loop
• 2nd order cybernetics (von Foerster)
– Leads to reflexive presence of engaged mind (and heart)
feeling and choosing, adapting
• New developments in AI (Andy Clark), heuristics, Bayesian
priors
– Anticipation, but we are limited beings
• Freud: “we are not masters in our own house” – After
Copernicus and Darwin….

19. Uncertainty (Life 2): Embodiment
• Observer is embodied, enactive, embedded, extended (4Es,
Varela, Thompson)
– Unsupervised learning, not representation: Just “Keeping
in touch with the world”
• Not rationalist Cartesian dualism
• So not 3rd person, value free rationalism but 1st person
phenomenology (Merleau Ponty)
– Inter-subjectivity, appearances, expression
– The observer is alive, aware and emotional!!
• Phenomenology is about meaning

20. Uncertainty (Life 3): Distributed robustness
• Life operates via networks.
– Many different pathways, equifinality
• No simple cause = effect
– No one gene, one disease – knockouts
– Same with physiology, organisms, ecology, society
– Shocks can arise internally...
• Try various options, trash what doesn’t work, move on..
• Life is robust, readily exploits the adjacent possible: not
efficient, not optimal, not predictable, brutal

21. Distributed risks: the result of Life 1-3
• Robustness involves the whole of the system, provides long
term security and solutions, is self organising, is only
amenable to indirect management. It exploits uncertainty.
Life is not insured!

22. If living organisms are involved
this puts at risk any attempt at design,
management or restoration
Life has a qualitatively different modus operandi from economics
or “scientific” management
Reflexivity, Embodiment, Evolution
Hence failures in project management and public policy
We are beyond modernist systems engineering and
constructivist sociology…. and a more social kind of science
(both are physics envious)

23. Consequences 1: No universal model
• No universal predictive map or model
• Global constraints, regional and local differences: realism
• Context, contingency – can only be managed from within
• Order, disorder, interaction, development – events are critical
• NO LAWS ENTAIL – we are social animals
• Scaling issue: global uncertainties, local perceptions

24. Consequences 2:
A phenomenological science?
• Natural philosophy is “Science”: universal laws, predictability,
finality, efficiency, optimality
– “scientific management”, evidence, models
• Natural history is phenomenological science: local,
descriptive, equifinal, unpredictable, creative expression. Not
ergodic, contingent
– Perceptions, feelings, appearances
– Local contextuality, place and “genius loci”
• Somehow we have to do both

25. Also: Reasons and reasoning
• Reason is not some kind of Cartesian, dis-embodied, God-
given faculty of humans
• Reasoning is something all organisms do, and life never
exists alone
– (Lone geniuses do not exist)
• Reason (and reasoning) is about decisions, justification,
argument, social interaction
– It is biased, flawed, partial, unreliable
• What do we use for evidence?

26. Consequences 3: Complementarity
• Terminology from particle physics: BBC Reith Lecture by
Robert Oppenheimer, 1953 (recent commentary by Prof
Brian Cox)
– Differing definitions of the same phenomenon depending
on where you are coming from
• Participatory realism (perspectival realism) and complexity
lead to un-decideable propositions
• Challenge for society is to decide how to act and then
actually do something
– Democracy is not about getting everyone to think the
same, it is about getting people who think differently to
act the same – to agree (Walter Lippman)

27. And: Ethical issues arising
• Just because we can do stuff…. Should we?
– How do we live? How are we living?
– Neoliberal efficiency and financialisation, GUI
• Interconnectedness, risk, fragility
– Too much focus on people’s interests, politics
– Emotive argument and neglect of ethics
• Roger Scruton: disinterested interests, taste, style, culture,
aesthetics, beauty
– Money is NOT the only measure of value

28. Constitutive rules: The meta-architecture
• Innovation runs ahead of ethics, governance and constitutive
rules
• Google, Instagram, Facebook, Twitter
– Covert and overt surveillance, privacy issues
• Uber, Amazon, eBay, IoT
– Spreading of risk, destruction of jobs and benefits
• Huge changes afoot: web, mobile comms, disintermediation,
computational justice
– Roles of professionals changing – decreased
• and now the Internet of Things?? Siri (Apple) and Alexa
(Amazon)

29. The power of science
The Panopticon

30. Limited beings
• We are not rational animals: we have emotional needs, we
can be devious and untruthful
– Bubbles and bitcoins, pump and dump
– Innovation Ponzi schemes
– PPPs, Carillion and “market forces”
– Smart grids and smart meters
– Google, Facebook and Instagram
• The world is not stationary, optimal, efficient or fully rational –
go to Venice!!

31. So what does all this mean?
• How does this change what we do and how we do it?
• Expect the unexpected: design democracy, institutions,
constitutive rules to be flexible and resilient
– Design (where you can) for robustness and persistence
• Flexibility under constraints: realism rules
• Less “tell”, more “engage” – discourse and reasoning,
participatory realism
• Focus on global constraints and local context
• Ethics, beauty, other values, perceptions
• Realist representation and embodied expression
• Explanation and meaning

32. What makes us human – can we (should we) put a price on this?

33. Australian examples
• You could not have better examples of what is going on…
failures of governance
• Design of the meta-architecture is critical
• Energy: coal vs renewables, PV, solar-thermal, distributed
grid, NEMCO. No policy!
• Water: Murray Darling conflict between economics,
agriculture and environment: also links to energy through
Snowy 2.0

34. The desert of the real
The map is not the territory

35. Increasing resilience
• Using complementary ideas to increase resilience: having a
diversity of views is critical
• Flexibility, adaptability, nimbleness in policy and politics
• Understand the context, language, reasoning: power, money,
influence: Foucault again
• Who’s in and how we play the game: shared meaning
– Takes time to achieve – but makes us human
• Running up against our own constraints

36. Post-”modernist” systems?
• Existential risks will force change: but how?
– Do we do it? Or do we have it done to us?
– Look to the geography and history of conflict
• Movement now to change the “modernist” mind-set and find
new ways: a middle ground? Between neoliberalism and
socialism? Or something totally different?
– Rise of other nations and cultures: e.g. China
• “A history of half-forgotten kingdoms”

37. So if “modernism” is insufficient for
complex “hard” and “soft” systems…
Then what is?
What kinds of democracy, institutions and constitutive
rules do we need?
And this becomes a highly political question… as it
should be: we’re frozen in neoliberalism
We need to be engaged citizens!
Reflexive modernism? (Ulrich Beck…)