Complexity Science and The Public Policy Landscape Dr Wale Fawehinmi Centre for Public Policy Alternatives (CPPA), Lagos June 18 th 2014

Complexity Science and The Public Policy Landscape

Dr Wale Fawehinmi

Centre for Public Policy Alternatives (CPPA), Lagos June 18th 2014

Profile: Dr Fawehinmi

• BDS (University of Lagos, Nigeria; 1983)

• MBA (University of Leicester, UK; 2010)

• Msc (Merit) Public Policy & Management ; University of London, 2011)

• Phd Candidate (Public Policy, De Montfort University, Leicester, UK) 2012-till date

Centre for Public Policy Alternatives (CPPA), Lagos

Degrees Executive Education

• Certificate in Complexity Science; Santa Fe Institute, New Mexico USA; 2014

In Progress:• MIT Sloan Executive Certificate

(Management & Leadership) - Massachusetts Institute of Technology, USA (2009-2014)

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Today’s Discussion Part 1

• Introduction to Complexity

• Old Science Reductionism and the paradigm of Order

• The Transdisciplinary branches of Complexity Sciences

• Defining Complexity

• Characteristics of Complex Systems

• Complexity as a new way of thinking

Centre for Public Policy Alternatives (CPPA), Lagos 3

Today’s Discussion Part 2

• Complexity in Public Policy

• Complexity Science in Public Policy :The rationale

• Applying Complexity in Pubic Policy

• Economics: A Complexity Science Perspective

• Complexity Science in Public Policy: Dissenting voices

• Complexity Science in Public Policy: Making the case

• Parting thoughts


Science has explored the microcosmos and the macrocosmos ; we have a good sense of the lay of the land The great unexplored frontier is complexity.

― Heinz Pagels, The Dreams of Reason, 1988

I think the next century will be the century of complexity —Stephen Hawking, 200000

Complexity Science: The Clarion call


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Source: ladypushing80wordpress.com, mylovelyquotes.com

So What? Is Complexity Science relevant to Public Policy

Public Policy thinkers as social scientists: • Should embrace complexity science as new way of thinking.• Recognise that with globalization the world has become a complex system• That Complexity science offers insights into the dynamic setting of

policymaking This view builds from the proposition by theoretical biologist E.O Wilson that:

People expect from the social sciences —anthropology , sociology, economics, and political science―the knowledge to understand their lives and control their future. They want the power to predict, not the preordained unfolding of events, which does not exist, but what will happen if society selects one course of action over another. E.O Wilson : Consilience; 1998.


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Source: natureandculture.org

Old Science: Reductionism and the Paradigm of Order (I)

This paradigm is often referred to as the Newtonian reductionism • The grand pursuit of this order began in the age of enlightenment • Rene Descartes was the first to make the case for reductionism • Isaac Newton drew on Descartes’ reductionism to confer on us, immutable

laws of physics based on the notion of a clockwork universe• Newtonian epistemology influenced much of the contemporary ideas in

social sciences, public policy and human organization in the 19th/20th century• The paradigm shaped the economic doctrine of Adam Smith, Ricardo, Marx

and philosophy of John Stuart Mill


Source: thenaturalhistorian.com, u.osu.edu

Old Science: Reductionism and the Paradigm of Order (II)• Pierre Simon Laplace (1749-1827) extended the logic of Newtonian reductionism

• He argued for a perspective of scientific determinism

• He postulated that through mathematics, scientific predictions will be possible for everything for all times, if initial conditions were known earlier in time.

In his words:


If at one time we knew the positions and motion of all particles in the universe, then we could calculate their behaviour at any other time, in the past or future.

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Source: answers.com

The Paradigm of Order: The Rules

The central planks of the paradigm are:

• Order: Cause leads to known effects

• Reductionism: A system can be understood by observing the behaviour of its parts

• Predictability: Future course of events can be predicted by applying the right inputs into the model

• Determinism: Processes flow along orderly and predictable paths

― Geyer & Rihani, 2010


Anti-Reductionism: Scientists of the Revolution

• ‘’Successive transition from one paradigm to another via revolution is the usual developmental pattern of mature science ‘’

―Thomas Khun, The structure of Scientific Revolutions (1962)

• Following Poincare’s rejection of Laplacian determinism and predictability , other notable scientists whose bodies of work profoundly questioned ex-ante assumptions about reductionism were:

• Albert Einstein (1879-1955): Theory of relativity challenged Newtonian mechanics

• Neils Bohr (1885-1962): Contribution to Quantum mechanics• Erwin Schrodinger (1887-1961): Quantum measurement problem

• Werner Heisenberg (1901-1976): Uncertainty principle in Physics • Paul Dirac (1902-1984): Quantum field theoryCentre for Public Policy Alternatives (CPPA), Lagos 10

What is Complexity Science?

According to the New England Complex Systems Institute :

• Complexity science is about how parts of a system give rise to the collective behaviour of the system and how the system interacts with its environment. Complexity science is about understanding indirect effects. Problems that are difficult to solve are often hard to understand because the causes and effects are not obviously related

• Complexity science has became a discipline in its own right. Advances in computing and simulations has increased the depth of knowledge in the field.

• A wide range of phenomena have been labelled as complex systems

In natural systems ( Brains, insect colonies, immune systems, ecologies, societies, economies, the stock market , traffic, & weather/ climate )

In artificial systems (Computing systems, artificial intelligence systems, artificial neural networks & evolutionary programs)


Anti-reductionism: The Seismic Shift • Physicist Henri Poincare in 1887, was the first to raise doubts

about the notion of predictability and implicitly , the paradigm of order — the belief upheld by reductionists

• Poincare whilst attempting to predict the motion of a hurricane advanced the view that :

…………….it may happen that small differences in the initial conditions produce very great ones in the final phenomenon. A small error in the former will produce an enormous error in the latter. Prediction becomes impossible • Poincare’s assertion is that minute uncertainties in the

calculation of initial conditions can amplify errors in predictions of future events. This ‘’ sensitive dependence on initial conditions ‘’ determined by Poincare is the hallmark of chaotic systems.


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Source: famous-mathematicians.com

Complex Adaptive Systems


Source: slideplayer.us

Complexity Science: The Predecessors

1940’s — 50’s• Norbet Wiener: Cybernetics - Mathematics

• W Ross Ashby: Cybernetics of the Mind

• Ludwig von Bertalanffy: Systems Biology & Systems Theory Founder


Source: math.tufts.edu, blogerma.ru, optimizacionlinealsergioblogspots.com

Transdisciplinary introduction to the Complexity Sciences


Source: Castellani, Brian. 2013 http://scimaps.org

The Challenges of Defining Complexity: A Chinese Box Phenomenon

• A generally accepted definition of complexity is elusive because of the phenomenon’s wide scope and variability

• All proposed definitions suffer from incompleteness• Per Bak (1996) has referred to complexity as a Chinese box with surprises in

each box• A plethora of definitions of complexity have been proposed focusing on

• System size• Entropy• Algorithmic information content (AIC)• Logical depth • Thermodynamic depth• Fractal dimension• Computational capacity • Statistical complexity • Degree of hierarchy


A Broad Definition of Complexity (I)Goldin & Mariathasan (2014) ; elaborate on Richard Day’s definition , to offer an an expansive and multilevel definition of complexity.In Richard Day’s words:

‘’Complexity describes ‘’phenomena‘’ generated by interacting parts, all of whose causal connections are not easily discernible, [and] whose behaviour over time exhibits disorder and behaves unpredictably or chaotically’’ —Richard H Day, 2011

• This definition according to the authors can be unpacked into three levels: Small-tent Complexity ― Agent based modelling often referred to as the Santa Fe complexity.

• Its features include:


• Locally interacting agents • No central/global controller

• Cross-cutting hierarchical

organization

• Continual adaptation

• Perpetual novelty

• Little tendency to global equilibrium

A Broad Definition of Complexity (II)

Big-tent Complexity — this is broader in scope. It includes: • Small - tent complexity as described above • Cybernetics • Catastrophe theory • Chaos theory

Meta- Complexity • Includes every other definition (the 45 definitions catalogued by Seth

Lloyd) and in theory can cover several distinct definitions

• Goldin & Mariathasan, The Butterfly Effect: How Globalization creates Systemic Risks and What to do about it (2014) Princeton University Press.


Characteristics of Complex Systems


Source: necsi.edu

Complex Systems: Self Organization, Emergence & Adaptive Behaviour


Source: en.wikipedia.org

Characteristics of Complex [Adaptive] Systems (I)

• In complexity science, the distinction is often made between complex systems with an adaptive component and non- adaptive systems. In public policy our particular interest is in complex adaptive systems

• Adaptability: Agents interact and change their behaviour in reaction to the behaviour of other agents

• Emergence: System exhibits patterns of continuous unpredictable novelty. A ‘’process whereby the global system results from the actions and interaction of agents ‘’(Sawyer; 2005)

• Self-organized criticality/Phase transition: The concept describes a self-organizational mechanism of abrupt transitions in large scale systems that may be triggered by small events (earthquakes, stock market crashes avalanches etc). ‘’The Sandpile metaphor’’


Characteristics of Complex [Adaptive] Systems (II)

• Chaos: Complex systems exhibit constantly changing hard to predict behaviour[chaotic dynamics].The defining feature of such a system is ‘’sensitive dependence on initial conditions’’. A classic [metaphoric ] example of sensitivity to initial conditions is the ‘’ butterfly effect ‘’ ― the action of a butterfly flapping its wings somewhere on the planet resulting in a hurricane elsewhere. Due to an underlying attractor, order is often seen in chaotic systems. This chaotic effect has been mathematically proven

• Non–linearity: Due to positive and negative feedbacks in combination, Complex systems processes are not linear ―causality is not proportional to effect. Effects may be larger than causes.

• Power laws: Complex systems sometimes exhibit probability distributions that obey a decreasing mathematical function known as a power law. The probability of events such as earthquakes, floods storms follow power laws.


Emergence in Complex Systems


Source: http://www.tcd.ie/futurecities/research/energy/adaptations-complex-systems.php

Self-Organized Criticality: The Sandpile Metaphor

Bak’s Sandpile


Source: Amazon.com

Illustrating Characteristics of Complex Systems (I) - The Butterfly Effect

Edward N Lorenz, a mathematician, was the first to demonstrate random (Chaotic) behaviour due to sensitivity to initial conditions. He is credited with coining the term ‘’Butterfly Effect‘’

• He found that rounding up decimal points in a weather forecast model led to a thunderstorm forecast instead of a sunny day forecast

• His finding was a major breakthrough for chaos and complexity theory. More importantly , he validated Poincarẽ ‘s half a century old prediction about uncertainties in the calculation of initial conditions amplifying errors in predictions of future events.


Source: nytimes.com

The Butterfly Effect

―Fluvio Mazzocchi, 2008


Non–linearity & Chaos (Sensitivity to initial conditions)

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Source: xxxxxxxxxxxxxxxxxxxxx

Illustrating Characteristics of Complex Systems (II) Non-linearity & Chaos: The Logistic Equation

Robert May a mathematical biologist developed a [logistic] model based on population ecology to capture the essence of chaos and sensitive dependence to initial conditions , by combining the effects of birth and death rates as a variable parameter r in the equation xn+1= rxn ( 1―xn)

Feigenbaum extended May’s Model employing mathematical scenarios to demonstrate a range of properties common to chaotic systems as well as the transition from regular periodic behaviour to chaos


Source: zoo.ox.ac.uk, wolffund.org.il

A model of Chaos & sensitive dependence to initial conditions: The Logistic Equation

• xn represents the population at year n e.g. x0 represents the initial population

• r represents a parameter of population growth rate

• When r is between 1 and 3, xn+1 will converge at a fixed point

• When r is between 3 and 3.4, xn+1 will oscillate between 2 values

• When r is between 3.4 and 3.5, xn+1 will oscillate between 4 values

• When r is approximately 3.57, the values of xn+1 will become chaotic

Population Variables Behaviour of xn+1 for Values of r



Order in Chaos: The Bifurcation Diagram

The bifurcation diagram demonstrates a universal property of large sets of chaotic systems i.e ‘’period-doubling’’ to chaos. By iterating the logistic map for a given value of r , a fixed point is first recorded followed by a period-two oscillation, then period four, eight, etc. These abrupt transitions are bifurcations. Cumulative bifurcations result in chaos. Feigenbaum discovered that the rate at which bifurcations got closer (i.e r value-convergence) was 4.6692016 known a Feigenbaum’s constant.



Behaviour of the logistic map for varying values of r


Source: learner.org

Complexity Science: To What End?• Complexity is a new way of thinking

• We are hard-wired to see and think rigidly about organizations in mechanical terms than in terms adaptive loose couplings. We increasingly drawn towards thinking in complexity because it is a more accurate depiction of reality

• The machine[metaphor] like mode of thinking is a ‘blunt weapon’ against fast-paced socio-economic challenges of the 21st century

• We know about information systems failure and markets self-organizing (some into oligopolies and monopolies). Strategies to manage these complex systems can come from a knowledge of complex adaptive systems.

• In the information age, the bottom-up approach that the complex adaptive system metaphor advocates is arguably a better recipe for the challenges of human organization


Introduction: Complexity in Public Policy

• Morҫӧl,G (2012) has suggested that public policymaking should be re-conceptualized as a complex systems

• In complexity terms the definition he puts forward is :

• ‘’ Public policy is an emergent, self-organizational, and dynamic complex system.The relations among the actors of this complex system are non-linear and its relations with its elements and with other systems are co-evolutionary ’’ .

•


Source:Morcol , G (2012) p266

Rationale for Applying Complexity Science in Public Policy (I)

There is much ‘outcome unknowabilty’ in public policy that is analogous to characteristics of complex systems.

• Adaptability: Public policymakers are typically challenged by problems involving a diversity of interacting parts or agents within systems. A city is complex system with individual interacting and adjusting continuously on socio-economic, political and physical levels in the environment

• Emergence: Systems are not static, they are constantly changing [evolving]

and reinterpreting themselves resulting in novel patterns. Resilience or vulnerability may result from socio-economic relations in communities

• Self-organization: Mutually adapting agents in complex systems self-

organize without any direct control. Financial markets automatically adapt to changing conditions including policy interventions Centre for Public Policy Alternatives (CPPA), Lagos 33

Rationale for Applying Complexity Science in Public Policy (II)

• Self-Organized Criticality: In public policy as in complex systems, there are abrupt phase transitions of varying intensities. As in the sand-pile metaphor, a policy environment is normally resilient to perturbations; but at critical times [Tipping points], an additional grain of sand may result in an avalanche — equivalent to a policy intervention resulting in a massive systemic change in the socio-political environment

• Chaos: Defined earlier as sensitive dependence to initial conditions in complex systems , is akin to a small change in socio-economic conditions having a huge impact on business. Businesses such as banking are seemingly always at the edge of chaos.

• Non-Linearity: In non-linear interactions the whole can be more than the sum of the parts. Case in point ―one particular media report triggering huge public interest while public interest may be passive to some other important report


Black Swan Events

• Complexity science can offer helpful insights for solving so-called wicked problems and dealing with low probability high impact events(Black Swan events). These events include : Stock market crashes, power outages and pandemics


Source: mrcapwebpage.com, wedte.com, anh-europe.org

http://www.google.co.uk/url?sa=i&rct=j&q=&esrc=s&source=images&cd=&cad=rja&uact=8&docid=smI0QwU7QN7CxM&tbnid=kbTUrGWuOeoYXM:&ved=0CAUQjRw&url=http://manninglive.com/2014/02/15/santee-electric-still-helping-4k-customers-without-power/&ei=-U6DU7PlJeyS7Qb7vIHQDg&bvm=bv.67720277,d.ZGU&psig=AFQjCNEHNSOA_SUhMS1-1LZWOoLVtCSLHA&ust=1401200455084909

Potential Applications of Complexity in Policymaking (I)

Complexity highlights the flaws in rationally crafted policies. Many of the underpinning ideas of complexity science are well suited to complex decision making in the realm of public policymaking.

Complexity Science approaches have been explored in the following domains:• Traffic Control — Predicting surprises such as traffic jams

• Public Health — Epidemiology and Contagion • Data Mining for indicators of violence and political instability

• Climate Change and its impact • Crowd dynamics — Identifying patterns before stampedes• Complexity economics — Alternative approaches to equilibrium modelling

• Identification of Tipping points in human-earth issues (disruptions such drought)Centre for Public Policy Alternatives (CPPA), Lagos 36

Potential Applications of Complexity in Policymaking (II)

• Agent - based simulations ― insights about undesirable/desirable states • Network Analyses —may predict failure in real-life networks, e.g. Counter-

terrorism

• Sensitivity Analysis ― Measures the degree of variation to changing system parameters

• Scenario Modelling —Anticipation/mitigation of fallouts of disasters

• Dynamic Systems Modelling ― Modelling of alternative intervention options and possible negative consequences


Complexity Economics: Rethinking Economics

“The neoclassical era in economics has ended and has been replaced by the complexity era.“

— R. P. Holt, J.B. Rosser, and D.Colander (2010)

• Complexity Economics proposes an alternative ontology to Economics which suggests that the economy is a complex adaptive system (CAS) with many non-linearly interacting parts. It is concerned with economic theory at an out-of equilibrium level

Complexity Economics suggests that: • The orthodoxy of conventional economics about equilibrium is unrealistic —Its

assumptions about rational people making decisions in a static equilibrium world is wrong

• Behavioural economics has established that assumptions about rationality are wrong

• Complex systems naturally arise in the economy (agents, banks, consumers, investors, etc) Models that can capture their interactions realistically are needed


Complexity & Connectivity: The Global Financial Market 1985-2005


Source: Shin, 2008

Complexity Economics: Perspectives

• The camp of Complexity Economists who rejected and argued against the notion of equilibrium in conventional economics, and postulated that all economic phenomena are complex [non-linear] ; include:

• Eric Beinhocker — Widely acclaimed for his contribution to the perspective of the economy as a complex adaptive system.

• Brian Arthur — Non-linearities ( in form of Positive feedbacks) arise from increasing returns in economies

• Ian Goldin ― Globalization & Systemic Risks (in economics, finance, pandemics)

• Ricardo Hausmann — Developed an Economic Complexity index (ECI) using complexity theory and trade data

• Thomas Homer-Dixon ― Economic instability & inequality


Economic Complexity index

Ricardo Hausman, Cesar Hidalgo, et al have proposed an ECI with an approach based on complexity theory and trade data.

• The model’s central thesis is that the economy is based on units of productive knowledge or capabilities

• The ECI is derived from the complexity of a country’s exports that gives it comparative advantages ― it takes into account the diversity and ubiquity of its products

• A high ECI tends to correlate with higher growth levels of growth

• It is a strong indicator/predictor of future economic growth

• The index suggests that highly complex economies with a lower anticipated GDP can expect rapid growth the opposite holds for countries with high GDP relative to complexity


Economic Complexity Index


Source: atlas.media.mit.edu

Criticisms of the Application of Complexity Science in Public Policy

The is no shortage of critics who questioned the practical value of the complexity paradigm in public policy. They suggest that it is yet another academic fad driven by metaphor in the main

Pollitt (2009) in a critique raised the following questions:• What is the ontological and epistemological basis of complexity theory ? • What are the appropriate methods for espousing complexity ? • What alternative explanations does complexity theory offer in the realm of

public policy ?

A concrete response to the first question has been articulated by Morҫol (2012) who suggested that complexity theory is best described as a ‘’ Metatheoretical ‘’ language. Methods for espousing complexity as previously discussed , rely on cutting edge computer simulations.


Complexity Science in Public Policy: The Case for

• The 2008 global financial crisis exposed the limitations of current economic and financial models.

• The threat of Systemic risk is heightened by globalization .

• Increasing global integration and cross-border interactions have made the world a complex adaptive system

• The pay-off of complexity science is already evident. Two Nobel prizes have been awarded for seminal academic contributions grounded on complexity science

• Thomas Schelling (2005 Nobel Prize Winner) — For his simulation of neighbourhood segregation patterns using Agent-based modelling

• Paul Krugman (2008 Nobel Prize Winner) — Premising his work on economies as complex adaptive systems, Krugman supplied explanations for the existence of clusters of economic activity and extended this work to provide compelling reasons for regional growth disparities. His approach drew upon network theory in complexity science Centre for Public Policy Alternatives (CPPA), Lagos 44

Concluding thoughts

• The field of complexity invites criticisms due to the sheer range of phenomena under the heading of complex systems .

• A rigorous lexicon is required to integrate the disparate concepts of self –organization, emergence, chaos, non-linearity, etc.

• Some complexity theorists such as Steven Strogatz have suggested that complexity:

• ‘’may be missing the conceptual equivalent of calculus, as a way of seeing the consequences of myriad interactions that define a complex system’’


Complexity Science: Is a Paradigm Shift in Sight?

• It is increasingly clear that complexity science is shifting the central assumptions of old science reductionist–mechanistic paradigm and yielding ground to new thinking anchored on non-linear dynamics, systems and evolution. A paradigm shift in the strict sense ,however, will be a slow birth.

• Paradigm shifts are never easily accepted —as Max Planck remarked :

A scientific truth does not triumph by convincing its opponents and making them see the light, but rather because the opponents eventually die, and a new generation grows up that is familiar with it

—Max Planck, Scientific Authobiography and other papers, 1949


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References

• Hidalgo, C, Bailey, K ,Albert-Laszlo , B & Hausmann, R (2007) The product Space Conditions the development of Nations. Science 317 (5837) 482 487 Khun, T. S (1962) The Structure of Scientific Revolutions. University of Chicago Press

• Mitchell, M (2009) Complexity: A Guided Tour. Oxford University Press, New York

• Morcol , G (2012) A Complexity Theory for Public Policy. Routledge

• OECD Global Science Forum(2009) Applications of Complexity for Public Policy: New Tools for finding unanticipated Consequences and Unrealized Opportunities www.oecd.org/sti/gsf

• Waldrop, M.M(1992) Complexity: The Emerging Science at the Edge of Order and Chaos

• Wilson, E.O (1998) Consilience: The Unity of Knowledge. Abacus. London


References continued

• Bak , Per 1996 `How nature works: the science of self-organized criticality. Springer-Verlag. New York Beinhocker, E(2007) The Origin of Wealth: Evolution, Complexity, and the Radical Remaking of Economics, New York Random House.

• Fulvio, M (2008) Complexity in Biology: Exceeding the limits of reductionism and determinism using Complexity Theory. European Molecular Biology Organization. Vol 9 No. 1

• Gleick, James (1987) Chaos: The amazing Science of the unpredictable

• Geyer, R & Rihani , S (2010) Complexity and Public Policy: A new approach to 21st century politics, policy and society

• Goldin , I & Mariathasan , M (2014) The Butterfly Defect: How Globalization Creates Systemic Risks, and What to do about it. Princeton N.J Hidalgo, C, Bailey , K ,Albert-Laszlo , B & Hausmann , R (2007) The product Space Conditions the development of Nations. Science 317 (5837) 482 487


Documents

Complexity Science and The Public Policy Landscape Dr Wale Fawehinmi Centre for Public Policy Alternatives (CPPA), Lagos June 18 th 2014