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Complexity
Economics and Alan Greenspan
Lewis L. Smith (USA)
© Copyright 2004 Lewis L. Smith
Complexity
Dynamic systems are ubiquitous throughout the universe. They range
from nanomachines to the universe itself. And in
their celestial form, they have been a subject of human inquiry for at least
six thousand years. As a result, many of the themes of concern to complexity
researchers have already been studied in astronomy, biology, cardiology,
chemistry, computer science, demography, economics, electricity, game theory,
mathematics, meteorology, physics, et cetera, albeit in each case from the
perspective of a particular scientific discipline.
But it is only recently that we have come to recognize that many
dynamic systems long considered “independent” actually constitute a single
family, one which we now call complex systems. Examples of the latter are
biological species, cardiovascular systems, economies, human societies,
neural systems and securities markets. What ties these seemly diverse systems
together and how their common features came to be recognized are the subjects
of this section.
For example, “the invisible hand”, first noted by Adam Smith in 17761
is a classic example of the
“emergent properties” so characteristic of complex systems. Whenever the
potential buyers and sellers for a particular good or service reach a
critical mass in terms of number, they may spontaneously organize into a
decentralized, competitive market which exhibits a coherent set of prices.
Moreover, this set may remain in equilibrium for a considerable period of time.
As this market coevolves with other markets and with its cultural, ecological
and institutional environments, it may not only exhibit types of dynamics
which are either unique or those which are common to markets as a class, but
also some or all of those which are common to complex systems.
But it was not until the 1970’s that researchers in these diverse
fields began to talk to one another sufficiently to overcome the
interdisciplinary barriers of concepts, jargon and pride and achieve a
painfully won breakthrough in mutual understanding. At some point, some of
them came to realize that the apparently distinct objects of their affections
had something in common, enough for these features to be fruitfully studied
within the scope of a new discipline, which came to be known as “complexity”,
among other names. Like the famous blind men, they realized that they were
all feeling the same animal (or at least related versions of the same animal)
only this “animal” was a good deal more complicated and complex than an
elephant!
Today, with a proliferation of business advisory groups,
conferences, consultants, fellowships, journals, research institutes,
seminars, workshops, et cetera, complexity is here to stay, despite
occasional expressions of doubt from within and without the discipline.2
Moreover, the students of
complexity have already been able to give useful advice to economists,
managers, politicians and others.3
In this, they resemble the “mature” discipline of biology, which
continuously contributes to the invention of medicines which cure people and
save lives, even though it still cannot tell us what constitutes “life” or
how life came about.
Nevertheless, the goals of many complexity researchers seem to have
become more modest. Whereas once some of them dreamed of uncovering the
equivalent of Newton’s mechanics for all complex systems, many would be
content to find for each type, a set of laws governing its dynamics and then
some general principles underlying all of these sets. These principles would
then become the foundations of a mature discipline of complexity.4
Moreover, as a young discipline, complexity still has many issues to
resolve. These include a lack of consensus on basic definitions, on metrics,
taxonomy and terminology. There is also an urgent need to improve theoretical
constructs and develop new ones, and to perform many more empirical
validations.
Some of the open questions yet to be answered include — What exactly
do we mean by “complexity” and “complex systems”? What is an “emergent property” and how does
it “emerge”? How do we model and
explain the dynamics of systems which are capable of manifesting such diverse
behavior as “lockins”, “multimodal” behavior, “path dependence” and “branch jumps on the
possibility tree”, all within the planning horizon of the
observer/participant?5
Some outstanding characteristics of complex systems are the following.
- Once a critical
mass of potential participants has been reached, they spontaneously self-organize
into a dynamic system of successive hierarchies. This is done by a
process of mutual accommodation, without central direction, planning or
programming.6
With only modest intelligence, local information and simple rules for
interaction, the participants in these systems can generate very complex
system behavior. This is due in part to the
positive feedbacks which may occur from events in the life of the system. In
turn, the latter are due (in part) to the existence of increasing returns to
scale, such as those which may be provided by network effects. As a result,
there is no need for strong assumptions about the capacity, knowledge or
rationality of the participants, although none of these properties are
prohibited. (So much for rational expectations!)
- Once formed,
complex systems exhibit surprising properties, called “emergent
properties” which cannot be deduced in advance from the properties of
the participants, from the rules for their interaction or from any
combination thereof. Adam Smith’s “invisible hand” was one of the first
to be recognized.7
- A complex system is
likely to spend more time in disequilibrium than equilibrium. And there
is no guarantee that departures from equilibrium will be short, in
either distance or time. (Keynes lives!)
Moreover, being in equilibrium may even be suboptimal, if it
means that you are “asleep at the switch” and are going to be “zapped”
by a competitor, as happened to the US auto manufacturers on the eve of
the first Japanese assault on their market share.8 Finally multiple equilibriums are also
possible.
- The dynamics of a
complex system are best described by non-linear as opposed to linear
relationships9, but as yet it is not possible to accurately
model the former. The closest one can come are simulations based on
cellular automata.10 Despite their limitations, these models
produce behavior similar to the observed behavior of real systems.11
An important subfamily of complex systems are both adaptive and evolutionary
(CAE systems).12 Some important characteristics of CAE systems are
the following.
- CAE systems
co-evolve with their environment(s) and/or other system(s). Examples are
the interactions of deer, soil, vegetation, weather and wolves;
biologically healthy lakes being managed for multiple use; and of
course, a national economy in a world of other economies, national and
international institutions et cetera. This co-evolution is often more
“bouncy”, complicated and faster than Darwin imagined for biological
species, and may involve symbiosis as well as competition.13
- In the medium and
long runs, the evolution of a CAE system is liable to be unpredictable,
in both space and time. To be sure, the inherent characteristics of the
participants, their initial endowments and institutions, the
environments within which the system operates and phenomena such as lockins and path dependence14, may set a
certain “tone” to the system’s evolution and for a while at least, keep
it within a fairly compact region of its possibility tree. However,
other factors, some like the bumpers in a game of pinball, will
eventually set the system off in unexpected directions. These include
“visits” to chaotic and random modes, the importance of initial
conditions in the case of the former, the unpredictability of outcomes
in both cases, increasing returns to scale, political crises,
technological innovations, epidemics, wars and branch jumps on the
possibility tree.
- Given the
foregoing, the “best estimate” forecasting long favored
by American automobile manufacturers and Marxist dictatorships (among
others) is “out”, and “scenario planning” is “in”.
- Long-run optimums
cannot be defined and may be multiple. So every investment plan must be
“re-optimized” from time to time. And in comparing investment options,
strategic merits and robustness against surprise may be more important
than an incremental advantage in terms of the internal rate of return.15
- In a world of CAE
systems, a new kind of manager and a new kind of planner are required.
Also and for the first time in history, “antenna people” become
important. These are people who can detect whether the current scenario
is unfolding as planned, shifting under ones feet or turning into
something unforeseen, and do so in time for the organization to avoid being
ambushed!16
Alan
Greenspan
In the last few years, a
number of agencies of the US federal government have hired consultants who
specialize in applying the fruits of complexity research to strategic
planning and/or to management. In August
2003, there occurred what may turn out to be one of the biggest breakthroughs
of all for complexity theory and in a most unlikely place, Jackson Hole,
Wyoming. Moreover, it happened at a
symposium sponsored by the Federal Reserve Bank of Kansas City, located in
what some consider “the heartland of America”. Speaking on “Monetary Policy under
Uncertainty” and clothing his message in the traditional language of risk
management, Alan Greenspan, Chairman of the FRB,
expressed numerous ideas which could have come straight out of the mouth of a
complexity economist. If my suspicions are correct, complexity economics has partially penetrated one of the
greatest bastions of the US economy.
Chairman Greenspan’s talk is only five pages long. Following are a few
quotes from this extraordinary document.17 [Italics are mine.]
“Uncertainty
is … the defining feature of [the monetary] landscape …As a consequence, the conduct of monetary policy …
requires an understanding of the many sources of risk and uncertainty that
policy makers face …
“…a critical result [of the attempt to
achieve this understanding] has been the identification of a relatively small
set of key relationships that, taken together, provide a useful approximation
of our economy’s dynamics … [However] our knowledge about many … important
linkages is far from complete and in all likelihood will always remain so. Every model … is a vastly simplified
representation of the world that we experience …
“… a prominent shortcoming of our structural models is that … not only are
economic responses presumed fixed
through time, but they are generally assumed to be linear …
“… also the relationships underlying the
economy’s structure change over time in ways that are difficult to anticipate
… what constitutes money has been obscured by the introduction of
technologies that have facilitated the proliferation of financial products …
“A well-known proposition is that, under
a very restrictive set of assumptions, uncertainty has no bearing on the
actions that policy makers might choose …These assumptions are never met in the real world.
“… policy makers need to consider not
only the most likely future path …but also the distribution of possible outcomes about that path …
“A policy action that is calculated to
be optimal … may not in fact be
optimal, once the full extent of uncertainty …is taken into account …
“… only a limited number of risks can be
quantified with any confidence. And even these risks are generally
quantifiable only if we accept the
assumption that the future will replicate the past … 18
“… Our problem is not the complexity of
our models but the far greater
complexity of a world economy whose underlying linkages appear to be in a
constant state of flux.
“Rules by their nature are simple and, when
[both] significant and shifting uncertainties exist in the [economy, these
rules] cannnot substitute for risk-management
paradigms …
“… monetary policy based on risk management
appears to be the most useful regime by which to conduct policy …”
I wrote the Chairman about this speech and received a Delphic reply
from one of his assistants, assuring me that the Chairman will continue to
consider such factors in the future!
So if he has embraced much of the complexity message, he is not yet
“out of the closet”. His language is veiled, and his “conversion” is
incomplete.
As regards the language, one conjectures that he has clothed his
ideas in the mantel of risk management, so as not to scare his ex-coworkers on Wall Street.
As regards his philosophy, other positions adopted in recent years
show that in some ways, he is still far to the right, in terms of the
traditional US political spectrum.19 For example, in 2000, he
denounced “irrational exuberance” in the stock markets, then refrained from
action, when he could have sent a strong psychological message by raising
“margin requirements”, the minimum down payment required for purchases of
stocks on credit. Subsequently he opposed tax cuts. But once they were enacted
(with 60% going to 10% of the
taxpayers) he called for expenditure cuts in order to balance the federal
budget. This of course leaves some 43 million Americans who lack health
insurance “out in the cold”.
Nevertheless, “a cat” did “get out of the bag” at Jackson Hole.
Let’s see how we can turn this felicitous event to our advantage, in the
struggle to replace neoclassical economics with something humane and
realistic.
Notes
1. Smith, A., An Enquiry into the Nature and Causes of
the Wealth of Nations (Glasgow edition, two volumes, Oxford, 1976).
2. Durlauf, S., “Complexity and Empirical Economics”, Feb
2003, < ideas.repec.org >.
3. In addition
to the examples mentioned previously, see also: Allison, M. A. and Kelly, S.,
The Complexity Advantage : How the
Science of Complexity Can Help Your Business Achieve Peak Performance
(McGraw-Hill, 1999), Axelrod, R. and Cohen, M. D., Harnessing Complexity (The Free Press,
1999), and Kupers, R., “What Organizational Leaders
Should Know about the New Science of Complexity”, Complexity Sep/Oct 2000,
among others.
4. Klüver, J., “The Evolution of Social Geometry”, Complexity 09/01. For a detailed and
erudite discussion of complex systems in different disciplines, see Bar-Yam,
Y., Dynamics of Complex Systems (Addison-Wesley,
1997).
5. A
“possibility tree” is a diagram which charts the possible evolutions of a
dynamic system from its present condition in the form of a branching tree, on
the assumption that each possibility can be discretely described and is related
only to one antecedent and a few successors. An example of a “branch jump”
would be if Oman, currently dependent on crude oil, suddenly found it
economic to apply the Shell Middle Distillates process to producing diesel,
kerosene and naphtha from stranded gas fields at isolated locations in that
country.
6. Participants
are called “agents” in the literature, which begs the question, Agents of
whom? In fact, participants are often independent, as is the case with small
business owners and stock-market players.
7. The formation
of water from hydrogen and oxygen is sometimes cited as an example of an
emergent property in chemistry. In fact, a Martian who knew about the valence
electrons of these two gases and how earthly chemical reactions take place, could
predict the possibility of water without ever having seen it.
8. The phrase
“asleep at the switch” refers to the early days of railroading, when switches
in the tracks were manually operated by a “switchman” who spent most of his
shift in a small shack along side a telegraph instrument, waiting for word
that a train was coming. Sometimes switchmen fell asleep, from alcohol or
boredom, occasionally with disastrous consequences.
9. Mateos, R., Olemdo E., Sancho, M., and Valderas, J.
M., “From Linearity to Complexity : towards a New Economics”, 2004, <
server.srcpc.unsw.edu >.
10. For example:
Conway’s “Game of Life” in Gardner, M., Life
and Other Mathematical Amusements (Freeman, 1983) ; Epstein, J. M.,
“Agent-based Computational Models and Generative Social Science, Complexity May/June 1999 ; Gross, D.
and Strand, R., “Can Agent-based Models Assist Decisions on Large-scale
Practical Problems ?”, Complexity Jul/Aug 2000, and Page, S., “Computational
Models from A to Z”, Complexity Sept/Oct 1999. See also
< econ.iastate.edu/tesfatsi >.
11. For two
additional candidate characteristics, see Chu, D.,
Strand, R. and Fjelland, R., “Theories of
Complexity”, Complexity 08/03 (2003).
12. The literature frequently refers to complex adaptive systems
(CAS), a somewhat looser term which appears to encompass CAE systems. See Markose,
S., “Markets as Complex Adaptive Systems” 09/03 < ideas.repec.org >.
13. Darwin, C., On the Origin of the Species (Harvard
U. Press, 1964).
14. The reality
of lockins is controversial and has generated a
large literature for which I have not found a good summary. However, path
dependence is common in developing countries. For an excellent example, see
Reinhart, C., Rogoff, K. and Savastano,
M. A., “Debt Intolerance”, NBER working paper
#9208, August 2003.
15. Some of the
best examples of this kind of tradeoff are
unfortunately the least accessible. For example, the debates between “bean
counters” and “innovators” within pharmaceutical companies. And the internal
debates between bean counters and engineers over energy conservation and
renewable energy measures for existing factories.
16. See Smith,
L. “Who Matters in a Complex Society ?”, June 2004, Economics Web
Institute. Go to Google Advanced
Search, enter < economicswebinstitute.org > for English language only.
Once “inside” this URL, look for above title and click on underlined word essay
below the abstract.
17. http://www.federalreserve.gov/boarddocs/speeches/2003/200308
29/default.htm
18. A risk which is not
quantifiable is no longer a risk. It is an uncertainty.
19. By our emphasis on disequilibrium,
complexity investigators are not only “off the spectrum” but in rebellion
against 250 years of economics. After all, in the final analysis, even
Austrians, Marxists and Schumpaterians come down in
favor of equilibrium as normal, if not also a good
thing.
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SUGGESTED CITATION:
Lewis L. Smith, “Complexity Economics and Alan
Greenspan ”, post-autistic economics review,
issue no. 26, 2 August 2004, article 2, http://www.paecon.net/PAEReview/issue26/Smith26.htm
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