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Economics and the Complexity Vision2009 Summer Project

Greg PrattMesa Community College

The static neo classical model of economics typically found in the economics

classroom (and professional journals) belies the reality of human interaction. While

models demand a set of simplifying assumptions, the quantitative approach that swept

economics in the post WW II era to dominate the profession does as great a disservice to

society as a service. However, looking back to classical economics as well as the

Austrian school a small group of scholars have begun to examine what has been called

economic behavior from another perspective. While these scholars are a tiny part of the

profession, their view has more than passing importance. David Colander in The

Complexity Vision and the Teaching of Economics observes: “. . . the field of economics

displayed many of the characterize a complex system. It had a self-organized quality to

it, and it dealt with interdependent agents. Indeed it has along history of explanations

involving the invisible hand and spontaneous order. “(1).

So the examination of the emerging work in complexity, (2) centered at the Santa

Fe Institute (SFI), is an appropriate and compelling allocation of time. The Santa Fe

Institute is a private, not-for-profit, independent research and education center founded in

1984, for multidisciplinary collaborations in the physical, biological, computational, and

social sciences. Understanding of complex adaptive systems is critical to addressing key

environmental, technological, biological, economic, and political challenges. Complexity

itself is centre-stage, rather than an emergent property of research in particular

disciplines, at the Santa Fe Institute. (http://www.santafe.edu/about/ ) At SFI, set up in

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1984 as an independent research centre, scientists (some of them eminent) from a range

of disciplines – physics, biology, psychology, mathematics, immunology and more - have

engaged with computing expertise to conduct interdisciplinary work on the behaviour of

complex adaptive systems. They have built models which can be interpreted as

representing biological, ecological and economic phenomena. (Rosenhead )Colander’s

edited work in The Complexity Vision and the Teaching of Economics, the inspiration

for this summer project, is based upon the work of SFI scholars and covers a series of

topics ranging from bioeconomics to the Austrian school of economics.

This complexity work is tied to two of the giants in Austrian thought – Ludwig

von Mises and F. A. Hayek as well as Adam Smith. Hayek underscored the importance

of complexity to his body of work in his 1974 Nobel Prize Lecture: “This brings me to

the crucial issue. Unlike the position that exists in the physical sciences, in economics

and other disciplines that deal with essentially complex phenomena, the aspects of the

events to be accounted for about which we can get quantitative data are necessarily

limited and may not include the important ones. . . ., in the study of such complex

phenomena as the market, which depend on the actions of many individuals, all the

circumstances which will determine the outcome of a process, for reasons which I shall

explain later, will hardly ever be fully known or measurable.” The major problem for

any economy Hayek argued is how people’s actions are coordinated. He noticed, as

Adam Smith had, that the price system—free markets—did a remarkable job of

coordinating people’s actions, even though that coordination was not part of anyone’s

intent. The market, said Hayek, was a spontaneous order. By spontaneous Hayek meant

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unplanned—the market was not designed by anyone but evolved slowly as the result of

human actions. Roger Koppl writes of the connection between Austrian and complexity:

Austrian economists (of the Hayekian variety at least) share common elements and a common past with complexity theory. Complexity theorists trace their origins in part to Ludwig von Bertalanffy’s work on systems theory and Norbert Wiener, creator of the related field of cybernetics. Hayek had a series knowledge of and interest in systems theory and cybernetics. . . . The most characteristic feature of Hayek’s system of thought is probably his notion of ‘spontaneous order. . . A spontaneous order is a complex adaptive system. It is Adam Smith’s idea of the ‘invisible hand’. (Colander 139-140)

The tie then between classical economics, modern Austrian thought and complexity

theory can be found both in the work the SFI as well as the sources of classical and

modern economic thought.

This tie between complexity theory and the Hayekian notion of spontaneous order

(3) is not widely communicated in social sciences and is arguably one of the most

important contributions that is lacking in contemporary social sciences education. The

connection between the two is both clear and compelling. Koppl goes on to clarify this

connection when he points out: “. . . they are complex; for spontaneous orders, the

‘degree of complexity is not limited to what a human mind can master. Second, they are

abstract . . . Third, they have no purpose, ‘not having been made’ by any designing

minds,” (140). This final point is critical, complexity is adaptive, emergent and

evolutionary and the work of the Austrians builds on the insight of Adam Smith to

indicate the implications of these set of conditions which seem to inform behavior. (2)

Harvard economist and former Harvard University President Lawrence Summers

explains Hayek's place in modern economics this way: "What's the single most important

thing to learn from an economics course today? What I tried to leave my students with is

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the view that the invisible hand is more powerful than the [un]hidden hand. Things will

happen in well-organized efforts without direction, controls, and plans. That's the

consensus among economists. That's the Hayek legacy."(quoted in The Commanding

Heights: The Battle Between Government and the Marketplace that Is Remaking the

Modern World pp. 150-151.)

While Hayek is neglected or often unknown in current economic education, Adam

Smith remains a central, if misunderstood, element of both instructor generated

instruction and textbook analysis of the markets. His invisible hand (mentioned only 3

times in his collected works) is frequently emphasized although misinterpreted. Vernon

Smith, an advocate of the constructivist or complex vision points to Smith’s contributions

to his work in experimental economics. He writes that this vision is:

an undesigned ecological system that emerges out of cultural and biological evolutionary processes: home grown principles of action, norms, traditions, and morality. Thus, "the rules of morality are not the conclusions of our reason." According to Hume, who was concerned with the limits of reason and the boundedness of human understanding, rationality was a phenomena that reason discovers in emergent institutions. Adam Smith expressed the idea of emergent order in both The Wealth of Nations and The Theory of Moral Sentiments. According to this concept of rationality, truth is discovered in the form of the intelligence embodied in rules and traditions that have formed, inscrutably, out of the ancient history of human social interactions. (Vernon Smith)

Vernon Smith argues for the view that Adam Smith in both the well known Wealth of

Nations and virtually unknown Theory of Moral Sentiments finds the emergent and

evolutionary view of human activity persuasive. Thus, the invisible hand metaphor

acquires a deeper meaning as a symbol for what Hayek would call spontaneous order that

is emergent, in the words of Vernon Smith, over the extended period of the “ancient

History of human social interactions. These interactions are the informal institutions –

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norms and conventions that Douglass North sees shaping formal institutions and

incentive structures that impact behavior. This process is one that is complex in nature,

has not been fully understood or modeled, in spite of the impression given by

introductory texts in economics that see markets and outcomes as fait accompliat. North

writes: Informal constraints (norms, conventions and codes of conduct) favorable to

growth can sometimes produce economic growth even with unstable or adverse political

rules. So North agrees with Vernon Smith and writes: “It is necessary to dismantle the

rationality assumption underlying economic theory in order to approach constructively

the nature of human learning. History demonstrates that ideas, ideologies, myths,

dogmas, and prejudices matter; and an understanding of the way they evolve are

necessary for further progress in developing a framework to understand societal

change.”(Nobel Lecture)

As Douglass North reminds us, the complicating factor in our study and

instruction of economics is change. In works ranging from Structure and Change in

Economic History to Understanding the Process of Economic Change North reiterates the

centrality challenge of understanding the forces that lead to change. In his 1993 Nobel

lecture North explicates what he calls the non ergodic nature of change. “increasing our

understanding of the historical evolution of economies” can “contribute to our

understanding of the complex interplay between institutions, technology, and

demography in the overall process of economic change.” He concludes much of his

recent work with the twin observations that it is “adaptive rather than allocative

efficiency that is key to our understanding of complex economic process and path

dependence, one of the remarkable regularities of history. . . . Pioneering work on this

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subject is beginning to give us insights into the sources of path dependence (Arthur, 1989

and David, 1985). But there is much that we still do not know.” (Nobel) In a subsequent

Nobel ceremony, Vernon Smith recognized this limitation in his banquet toast. (4)

So the line of thought from Smith to Hayek to North is clear – there is much we

do not know, the modern models used in economics mask or ignore this ignorance and

complexity theory is a vision or prism that can allow a more realized view of human

behavior. All three provide a rationale for their vision; the economic growth or change

has allowed humanity to dramatically increase the standard of living in the 21st century.

While an understanding of the limitation of contemporary quantitative economic

modeling, institutions such as the Santa Fe Institute seem to argue for a Hayekian

recognition of the limits to understanding of spontaneous orders or complex systems.

Complexity theory views behavior over time as informed by a series of certain

kinds of complex systems. The systems of interest are dynamic systems – systems

capable of changing over time and economics is concerned with change. Hayek points

out that: “It is, perhaps, worth stressing that economic problems arise always and only in

consequence of change.”(Use of Knowledge). So the underlying vision of complexity

theory is well positioned to examine the processes that shape and motivate behavior.

Chris Lucas points that that once goal of theory is to view complexity in a self-organizing

context. (5) The key to this goal is the realization that, as Hayek titled his Nobel Lecture,

knowledge is a pretense.

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Notes

(1) Because it is undesigned and not the product of conscious reflection, the spontaneous order that emerges of itself in social life can cope with the radical ignorance we all share of the countless facts on knowledge of which society depends. This is to say, to begin with, that a spontaneous social order can utilize fragmented knowledge, knowledge dispersed among millions of people, in a way a holistically planned order (if such there could be) cannot. “This structure of human activities” as Hayek puts it “consistently adapts itself, and functions through adapting itself, to millions of facts which in their entirety are not known to everybody. The significance of this process is most obvious and was at first stressed in the economic field.”34 It is to say, also, that a spontaneous social order can use the practical knowledge preserved in men's habits and dispositions and that society always depends on such practical knowledge and cannot do without it.

http://oll.libertyfund.org/?option=com_staticxt&staticfile=show.php%3Ftitle=1305&chapter=100481&layout=html&Itemid=27

(2) Complexity and chaos theory have already generated an impressive literature, and a specialised vocabulary to match. This introduction can, at most, sketch in the general area of intellectual activity, and hope to draw the sting of the terminology. The works cited above are possible starting points for those wishing to pursue the subject in more depth.

The more general name for the field is complexity theory (within which ‘chaos’ is a particular mode of behaviour). It is concerned with the behaviour over time of certain kinds of complex systems. Over the last 30 years and more, aspects of this behaviour became the focus of attention in a number of scientific disciplines. These range as widely as astronomy, chemistry, evolutionary biology, geology and meteorology. Indeed there is no unified field of complexity theory, but rather a number of different fields with intriguing points of resemblance, overlap or complementarity. While some authors refer to the field as ‘the science of complexity’, others more modestly and appropriately use the phrase in the plural.

The systems of interest to complexity theory, under certain conditions, perform in regular, predictable ways; under other conditions they exhibit behaviour in which regularity and predictability is lost. Almost undetectable differences in initial conditions lead to gradually diverging system reactions until eventually the evolution of behaviour is quite dissimilar. The most graphic example of this is the

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oft-quoted assertion that the flapping of a butterfly’s wing can in due course decisively affect weather on a global scale.

The systems of interest are dynamic systems – systems capable of changing over time – and the concern is with the predictability of their behaviour. Some systems, though they are constantly changing, do so in a completely regular manner. For definiteness, think of the solar system, or a clock pendulum. Other systems lack this stability: for example, the universe (if we are to believe the ‘big bang’ theory), or a bicyclist on an icy road. Unstable systems move further and further away from their starting conditions until/unless brought up short by some over-riding constraint – in the case of the bicyclist, impact with the road surface.

Stable and unstable behaviour as concepts are part of the traditional repertoire of physical science. What is novel is the concept of something in between – chaotic behaviour. For chaos here is used in a subtly different sense from its common language usage as ‘a state of utter confusion and disorder’. It refers to systems which display behaviour which, though it has certain regularities, defies prediction. Think of the weather as we have known it. (That is, I will leave possible future global climate change out of the picture.) Despite immense efforts, success in predicting the weather has been quite limited, and forecasts get worse the further ahead they are pitched. And this is despite vast data banks available on previous experience. Every weather pattern, every cold front is different from all its predecessors. And yet…the Nile doesn’t freeze, and London is not subject to the monsoon.

Systems behaviour, then, may be divided into two zones, plus the boundary between them. There is the stable zone, where if it is disturbed the system returns to its initial state; and there is the zone of instability, where a small disturbance leads to movement away from the starting point, which in turn generates further divergence. Which type of behaviour is exhibited depends on the conditions which hold: the laws governing behaviour, the relative strengths of positive and negative feedback mechanisms. Under appropriate conditions, systems may operate at the boundary between these zones, sometimes called a phase transition, or the ‘edge of chaos’. It is here that they exhibit the sort of bounded instability which we have been describing – unpredictability of specific behaviour within a predictable general structure of behaviour. http://www.human-nature.com/science-as-culture/rosenhead.html

(3) One idea propounded by Hayek is central for the understanding of the Social Sciences: the notion of complex phenomena. This notion was originally introduced in his paper “The theory of the complex phenomena” published in Studies in 1967. He proposes that the degree of complexity of a phenomenon depends upon “the minimum number of elements of which an instance of the

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pattern must consist in order to exhibit all the characteristic attributes of the class of pattern in question…”

http://evolucionismoycomplejidad.wordpress.com/2008/09/18/discussion-on-the-theory-of-complex-phenomena/

(4) Vernon L. Smith's speech at the Nobel Banquet, December 10, 2002

Copyright © Nobel Web AB 2002Photo: Hans Mehlin

 

Toast

I wish to celebrate:

• The Royal Family for their grace and charm in this magnificent affirmation of the dignity of humankind.

• Daniel Kahneman for his ingenuity in the study and understanding of human decision and its associated cognitive processes demonstrating that the logic of choice and the ecology of choice can be divergent.

• The pioneering influence of Sidney Siegel, Amos Tversky, Martin Shubik, and Charles Plott on the intellectual movement that culminated in the economics award for 2002.

• Humanity's most significant emergent creation: markets.

• Mandeville who said: "The worst of all the multitude did something for the common good."

• The ancient Judeo Commandments: Thou shalt not steal or covet the possessions of thy neighbor, which provide the property right foundations for markets, and warned that petty distributional jealousy must not be allowed to destroy them. Neither shalt thou

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commit murder, adultery or bear false witness, which provide the foundations for cohesive social exchange.

• David Hume who declared the three laws of human nature: The right of possession, its transference by consent, and the performance of promises, and taught that the rules of morality are not the conclusions of reason.

• F.A. Hayek for teaching us that an economist who is only an economist cannot be a good economist; that fruitful social science must be very largely a study of what is not; that reason properly used recognizes its own limitations; that civilization rests on the fact that we all benefit from knowledge that we do not possess (as individuals).

• Benjamin Franklin who said "Tell me and I forget, teach me and I remember, involve me and I learn."

• And to Kahlil Gibran who reminds us that work is love made visible.

Copyright © The Nobel Foundation 2002

(5) Complexity Theory states that critically interacting components self-organize to form potentially evolving structures exhibiting a hierarchy of emergent system properties.

This theory takes the view that systems are best regarded as wholes, and studied as such, rejecting the traditional emphasis on simplification and reduction as inadequate techniques on which to base this sort of scientific work. Such techniques, whilst valuable in investigation and data collection, fail in their application at system level due to the inherent nonlinearity of strongly interconnected systems - the causes and effects are not separate and the whole is not the sum of the parts. The approaches used in complexity theory are based on a number of new mathematical techniques, originating from fields as diverse as physics, biology, artificial intelligence, politics and telecommunications, and this interdisciplinary viewpoint is the crucial aspect, reflecting the general applicability of the theory to systems in all areas.

Static Complexity (Type 1)

The simplest form of complexity, and that generally studied both by mathematicians and scientists, is that related to fixed systems. Here we make the assumption that the structure we are interested in does not change with time, so that we can approach analysis of the system analogously to a photograph. For example, we can look at a computer chip and see that it is complex (in the popular sense), we can relate this to a circuit diagram of the electronics and compare alternative systems to determine relative or computational complexity (e.g. number of transistors). Similarly, we can do the same with lifeforms, making measurements in terms of the number of cells, genes and so on. All these quantitative aspects fail however to address one of the main problems of complexity

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thinking, that of defining just what complexity is, why one system of, say, 100 components differs from another of the same size.

To approach such questions we need to look for patterns as well as the statistics of quantity. It is clear that an arrangement of 50 white then 50 black balls is less complex than 5 black, 17 white, 3 black, 33 white, 42 black, yet the significance of such a pattern is unclear - is it random or meaningful ? When we expand this sort of analysis to 3 dimensional solids, and include more than one property of each part (e.g. adding size, density, shape), we get a combinatorial explosion of possible complexity that strains the analytical (pattern recognition) ability of current mathematics, even for relatively trivial systems. We have concentrated so far on just visual modalities, and views at a single magnification, yet we should be aware also that in nature multiple levels of structure exist in all systems, and this added fractal complication (e.g. complexity of molecule, plus cell, plus organism, plus ecosystem, plus planet etc.) makes even this static simplification mathematically difficult to quantify.

Dynamic Complexity (Type 2)

Adding the fourth dimension, that of time, both improves and worsens the situation. On the positive side, we can perhaps recognise function in temporal patterns more easily than in spatial ones (e.g. seasons, heartbeat), but conversely by allowing components to change we can lose those spatial patterns we originally identified, categories and classifications alter with time (e.g. leaves are green - except in autumn when they are yellow, and winter when they don't exist !). Function is one of the main modes of analysis we utilise in science, we ask the question 'what does the system do?', followed by 'how does it do it?', and both these presuppose actions in time (cyclic processes), an intrinsic meaning to the structures encountered.

Given our obsession with experimental repeatability in science, it is interesting to note that the property of being either static or cyclic is at the heart of our classification of phenomena as either being scientific or not. Science relies heavily on testing or confirmation, and this presupposes that we have multiple samples (either spatially or temporally). The forms of mathematical description that we employ will therefore have to be such that we obtain the same answers each time, and this has major implications for complexity theory. We are forced, currently, to artificially reduce the complexity of the phenomena we study to meet this constraint. A person has many aspects, but we describe them only by those that do not change with time (or do so predictably), e.g. name, skin colour, nationality (or address, job, age, height). Complexity theory however requires that we treat the system as a whole, and thus have a description that includes all aspects (as far as practical). In this we go far beyond conventional scientific and mathematical treatments, by including also one-off or variable aspects (e.g. actions, moods).

Evolving Complexity (Type 3)

Going beyond repetitive thinking takes us to a class of phenomena usually described as organic. The best known examples of this relate to the neo-Darwinian theory of Natural

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Selection, where systems evolve through time into different systems (e.g. an aquatic form becomes land dwelling). This open ended form of change proves to be far more extensive than previously thought, and the same concept of non-cyclic change can be applied to immune systems, learning, art and galaxies, as well as to species. Classification of complexity thus takes another step into the dark, since if we cannot count on there being more than one example of any form how can we even apply the term science to it ?

The answer to this question comes back to pattern. In any complex system many combinations of the parts are possible, so many in fact that we can show that most combinations have not yet occurred even once, during the entire history of the universe. Yet not all systems are unique, there are symmetries present in the arrangements that allow us to classify many systems in the same way. By examining a large number of different systems we can recognise these similarities (patterns) and construct categories to define them (this is, in essence, what the Linnean taxonomy scheme for living organisms is based upon). These statistical techniques are fine, and give useful general guidelines, but fail to provide one significant requirement for scientific work, and that is predictability. In the application of science (in technology) we require to be able to build or configure a system to give a specific function, something not usually regarded as possible from an evolutionary viewpoint.

Self-Organizing Complexity (Type 4)

Our final form of complex system is that believed to comprise the most interesting type and the one most relevant to complexity theory. Here we combine the internal constraints of closed systems (like machines) with the creative evolution of open systems (like people). In this viewpoint we regard a system as co-evolving with its environment, so much so that classifications of the system alone, out of context, are no longer regarded as adequate for a valid description. We must describe the system functions in terms of how they relate to the wider outside world. From the previous categories of discrete and self-contained systems we seem to have arrived at a complexity concept that cannot now even qualify a separate system, let alone quantify it, yet this misses an important point.

Co-evolutionary systems, like ecologies and language, are extremely adept at providing functionality, and if this is a requirement of science (the what question) we may be able to side-step the how question and tackle the desired predictability in another way. This methodology moves the design process from inside the system under consideration to outside. We can design the environment (constraints) rather than the system itself, and let the system evolve a solution to our needs, without trying to impose one. This is a very new form of organic technology, yet one already beginning to show results in such fields as genetic engineering, circuit design and multiobjective optimization. From the point of view of complexity theory we wish to be able to predict which emergent solutions will occur from differing configurations and constraints.

Quantification Preliminaries

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If we allow that traditional quantification in terms of static parameters or formulae is (at best) inadequate to fully deal with complex systems, then what other options do we have ? Specifically, how do we deal with variables and constants that swap places over a system lifetime (the edge of chaos interplay of barriers and innovation) ? In essence we need to allow that all the parameters in our system are variables (operating at differing timescales perhaps), and also allow for the number of parameters to increase or reduce dynamically (simulating birth or death). This again is a break from tradition in science, and requires what Kuhn called a scientific revolution - a new paradigm or set of initial axioms. This is what Complexity Theory provides.

Having set out the considerable problems we face in the analysis of complex systems, we can now turn to more positive matters. Much work has already been done as a preliminary to the quantification of complexity theory, and we can build on some 50 years of work in general systems theory or cybernetics, in linguistics, dynamics and ecology, as well as in modern genetics, cognitive science and artificial intelligence. The mistakes and successes of this inheritance can help steer our path towards more productive assumptions, those relating to the common features we find across the subject matter of all these disciplines, and related areas.

Assumptions and Objectives

In complexity thought we look for global measures that can apply in all fields. This assumption, along with others related to unpredictability, non-equilibria, causal loops, nonlinearity and openness means that our world view is in many ways the opposite of traditional science. Yet all these assumptions are valid for the organic style systems being considered here. A new type of quantification may well be needed in consequence.

Many objectives can be proposed for Complexity Theory itself, e.g. :

Explain emergent structures (self-organization) Measure relative complexity (hierarchical multi-parameter) Provide control methods for complex systems (steering points) Generate effective models (abstractions) Give statistical predictors (constraints) Solve outstanding problems (breakthroughs) Demonstrate possible new applications (novelty) Quantify the laws of order and information (if any)

http://www.calresco.org/lucas/quantify.htm

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Bibliography

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Elgar, Northampton, MA, USA. 2000.

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----“The Use of Knowledge in Society.” American Economic Review 35 (September):

519–530. Available online at: http://www.econlib.org/library/Essays/hykKnw1.html.

Lucas, Chris. Quantifying Complexity Theory.

http://www.calresco.org/lucas/quantify.htm

North, Douglass. Nobel Prize Lecture, 1993.

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-----Structure and Change in Economic History, 1981

-----Understanding the Process of Economic Change

Rosenhead, Jonathan. COMPLEXITY THEORY AND MANAGEMENT PRACTICE

http://www.human-nature.com/science-as-culture/rosenhead.html

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