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Economics and the Complexity Vision
2009 Summer Project
Greg Pratt
Mesa 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: quot;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.quot;(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, quot;the rules of morality are not the
conclusions of our reason.quot; 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 2002
Photo: 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: quot;The worst of all the multitude did something for the common
good.quot;
• 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 quot;Tell me and I forget, teach me and I remember, involve
me and I learn.quot;
• 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

11. Pratt 11
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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----- Individualism and Economic Order. Chicago: University of Chicago Press.
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-----Law, Legislation, and Liberty. Chicago: University of Chicago Press. 1973.
----“The Use of Knowledge in Society.” American Economic Review 35 (September):
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Lucas, Chris. Quantifying Complexity Theory.
http://www.calresco.org/lucas/quantify.htm
North, Douglass. Nobel Prize Lecture, 1993.
http://nobelprize.org/nobel_prizes/economics/laureates/1993/north-lecture.html
-----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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