Complexity and the Economy Read online

  that are not visible to standard equilibrium analysis, and where a meso-layer

  between the micro and the macro becomes important. This view, in other

  words, gives us a world closer to that of political economy than to neoclassical theory, a world that is organic, evolutionary, and historically contingent.

  THE ECONOMY AND COMPLEXITY

  Let me begin with the economy itself. The economy is a vast and complicated

  set of arrangements and actions wherein agents—consumers, firms, banks,

  2. See The Economy as an Evolving Complex System volumes edited by: Arrow, Anderson and Pines (1988); Arthur, Durlauf and Lane (1997); and Blume and Durlauf (2006).

  For history of the ideas see Fontana (2010), Arthur (2010b), and the popular accounts of Waldrop (1992) and Beinhocker (2006). Variants of complexity economics include generative economics, interactive-agent economics, agent-based computational economics, (see Epstein, 2006a; Miller and Page, 2007; Tesfatsion and Judd, 2006).

  3. For other essays on this general approach see Axtell (2007), Colander (2000, 2012), Epstein (2006), Farmer (2012), Judd (2006), Kirman (2011), Rosser (1999), and

  Tesfatsion (2006). The term “complexity economics” was first used in Arthur (1999).

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  investors, government agencies—buy and sell, speculate, trade, oversee, bring products into being, offer services, invest in companies, strategize, explore, forecast, compete, learn, innovate, and adapt. In modern parlance we would

  say it is a massively parallel system of concurrent behavior. And from all this concurrent behavior markets form, prices form, trading arrangements form,

  institutions and industries form. Aggregate patterns form.

  One of the earliest insights of economics—it certainly goes back to Smith—

  is that these aggregate patterns form from individual behavior, and individual behavior in turn responds to these aggregate patterns: there is a recursive

  loop. It is this recursive loop that connects with complexity. Complexity is

  not a theory but a movement in the sciences that studies how the interact-

  ing elements in a system create overall patterns, and how these overall pat-

  terns in turn cause the interacting elements to change or adapt. It might study how individual cars together act to form patterns in traffic, and how these

  patterns in turn cause the cars to alter their position. Complexity is about

  formation—the formation of structures—and how this formation affects the

  objects causing it.

  To look at the economy, or areas within the economy, from a complexity

  viewpoint then would mean asking how it evolves, and this means examin-

  ing in detail how individual agents’ behaviors together form some outcome

  and how this might in turn alter their behavior as a result. Complexity, in

  other words, asks how individual behaviors might react to the pattern they together create, and how that pattern would alter itself as a result. This is

  often a difficult question; we are asking how a process is created from the purposed actions of multiple agents. And so economics early in its history took a simpler approach, one more amenable to mathematical analysis. It asked not

  how agents’ behaviors would react to the aggregate patterns these created, but what behaviors (actions, strategies, expectations) would be upheld by—would

  be consistent with—the aggregate patterns these caused. It asked, in other words, what patterns would call for no changes in micro-behavior, and would

  therefore be in stasis, or equilibrium. (General equilibrium theory thus asked what prices and quantities of goods produced and consumed would be consistent with—would pose no incentives for change to—the overall pattern of

  prices and quantities in the economy’s markets. Classical game theory asked

  what strategies, moves, or allocations would be consistent with—would be

  the best course of action for an agent (under some criterion)—given the strat-

  egies, moves, allocations his rivals might choose. And rational expectations

  economics asked what expectations would be consistent with—would on

  average be validated by—the outcomes these expectations together created.)

  This equilibrium shortcut was a natural way to examine patterns in the

  economy and render them open to mathematical analysis. It was an under-

  standable—even proper—way to push economics forward. And it achieved

  a great deal. Its central construct, general equilibrium theory, is not just

  comPlexi t y economics [ 3 ]

  mathematically elegant; in modeling the economy it re-composes it in our minds, gives us a way to picture it, a way to comprehend the economy in its

  wholeness. This is extremely valuable, and the same can be said for other equilibrium modelings: of the theory of the firm, of international trade, of financial markets.

  But there has been a price for this equilibrium finesse. Economists have

  objected to it—to the neoclassical construction it has brought about—on the

  grounds that it posits an idealized, rationalized world that distorts reality, one whose underlying assumptions are often chosen for analytical convenience.4

  I share these objections. Like many economists I admire the beauty of the

  neoclassical economy; but for me the construct is too pure, too brittle—too

  bled of reality. It lives in a Platonic world of order, stasis, knowableness, and perfection. Absent from it is the ambiguous, the messy, the real.

  Good economists of course have always harbored a richer view of the

  economy than this (Colander and Kupers, 2012; Louça, 2010), so perhaps we

  could stick with equilibrium as the basis of our thinking, allowing that experience and intuition can fill out the realities. But this still is not satisfactory.

  If we assume equilibrium we place a very strong filter on what we can see in

  the economy. Under equilibrium by definition there is no scope for improve-

  ment or further adjustment, no scope for exploration, no scope for creation,

  no scope for transitory phenomena, so anything in the economy that takes

  adjustment—adaptation, innovation, structural change, history itself—must

  be bypassed or dropped from theory. The result may be a beautiful structure,

  but it is one that lacks authenticity, aliveness, and creation.

  What if economics allowed the wider possibility and asked how agents in

  the economy might react to the patterns they together create? Would this

  make a difference? What would we see then?

  ENDOGENOUSLY GENERATED NONEQUILIBRIUM

  The first thing to observe is that in asking “how agents might react to,” we

  are implicitly assuming nonequilibrium, for if novel reactions are possible

  they will alter the outcome, so by definition it cannot be an equilibrium.

  A well-trained economist might object to this assumption of nonequilibrium;

  standard doctrine holds that nonequilibrium cannot be important in the

  economy. “[P] ositions of unstable equilibrium,” said Samuelson (1983), “even

  4. Blaug (2003), Bronk (2009, 2011), Cassidy (2009), Colander et al. (2009), Davis (2007), Farmer and Geanakoplos, (2008), Kirman (2010), Koppl and Luther (2010), Krugman (2009), Mirowski (2002), Simpson (2002).

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  if they exist, are transient, non-persistent states. . . . How many times has the reader seen an egg standing on its end?”5

  Equilibrium, we are assured, is the natural state of the economy.

  I want to argue that this is not the case, emphatically not the case, that

  nonequilibrium is the natural state of the economy, and therefore the econ-


  omy is always open to reaction. This isn’t merely because of outside shocks

  or external influences, but because nonequilibrium arises endogenously in

  the economy. There are two main reasons for this. One is fundamental (or

  Knightian) uncertainty, the other is technological innovation. Let me take

  each in turn.

  First, fundamental uncertainty. All problems of choice in the economy

  involve something that takes place in the future, perhaps almost immedi-

  ately, perhaps at some distance of time. Therefore they involve some degree

  of not knowing. In some cases agents are well informed, or can put realistic

  probability distributions over events that might happen; but in many other

  cases—in fact in most cases—they have no basis to do this, they simply do

  not know.6 I may be choosing to put venture capital into a new technology,

  but my startup may simply not know how well the technology will work, how

  the public will receive it, how the government will choose to regulate it, or who will enter the space with a competing product. I must make a move but I have

  genuine not-knowingness—fundamental uncertainty. There is no “optimal”

  move. Things worsen when other agents are involved; such uncertainty then

  becomes self-reinforcing. If I cannot know exactly what the situation is, I can take it that other agents cannot know either. Not only will I have to form

  subjective beliefs, but I will have to form subjective beliefs about subjective beliefs. And other agents must do the same. Uncertainty engenders further

  uncertainty.7

  This observation of course is not new. Other economists, Shackle in par-

  ticular (1955, 1992), have written much about this. But it has an important

  consequence for theorizing. To the degree that outcomes are unknowable, the

  decision problems they pose are not well-defined. It follows that rationality—

  pure deductive rationality—is not well-defined either, for the simple rea-

  son that there cannot be a logical solution to a problem that is not logically defined. It follows that in such situations deductive rationality is not just a 5. Walras expressed a similar thought in a 1909 conversation with Schumpeter, “life is essentially passive and merely adapts itself to the natural and social influences which may be acting on it, so that the theory of a stationary process constitutes really the whole of theoretical economics. . . . ” (Tabb, 1999; Reisman, 2004).

  6. As Keynes (1937) puts it: “the prospect of a European war . . . the price of cop-per . . . the rate of interest twenty years hence. . . . About these matters there is no scientific basis on which to form any calculable probability whatever. We simply do not know.”

  7. Soros (1987) calls this the principle of reflexivity.

  comPlexi t y economics [ 5 ]

  bad assumption; it cannot exist. There might be intelligent behavior, there might be sensible behavior, there might be farsighted behavior, but rigorously speaking there cannot be deductively rational behavior. Therefore we cannot

  assume it.

  None of this means that people cannot proceed in the economy, or that

  they do not choose to act. Behavioral economics tells us that often the context determines how people decide, and certainly we can use its findings. And cognitive science tells us that if a decision is important, people may stand back from the situation and attempt to make sense out of it by surmising, making

  guesses, using past knowledge and experience. They use their imaginations to

  try to come up with some picture of the future and proceed on this (Bronk,

  2009, 2014). Indeed, as Shackle (1992) puts it, “The future is imagined by each man for himself and this process of the imagination is a vital part of the process of decision.” One way to model this is to suppose economic agents form

  individual beliefs (possibly several) or hypotheses—internal models—about

  the situation they are in and continually update these, which means they con-

  stantly adapt or discard and replace the actions or strategies based on these as they explore.8 They proceed in other words by induction (Holland et al., 1986; Sargent, 1993; Arthur, 1994a).9

  This ongoing materialization of exploratory actions causes an always-present

  Brownian motion within the economy. The economy is permanently in disrup-

  tive motion as agents explore, learn, and adapt. These disruptions, as we will see, can get magnified into larger phenomena.

  The other driver of disruption is technological change. About a hundred

  years ago, Schumpeter (1912) famously pointed out that there is “a source of

  energy within the economic system which would of itself disrupt any equi-

  librium that might be attained.” That source was “new combinations of pro-

  ductive means.” (Nowadays we would say new combinations of technology.)

  Economics does not deny this, but incorporates it by allowing that from time

  to time its equilibria must adjust to such outside changes.

  But this technology force is more disruptive than Schumpeter allowed. Novel

  technologies call forth further novel technologies: when computers arrive,

  they call forth or “demand” the further technologies of data storage, com-

  puter languages, computational algorithms, and solid-state switching devices.

  And novel technologies make possible other novel technologies: when the

  vacuum tube arrives, it makes possible or “supplies” the further technologies

  8. A standard objection is that allowing agents to reason non-deductively admits arbitrariness. What prevents such beliefs or behaviors from being chosen ad-hoc to yield some favored outcome? Certainly this is possible, but that doesn’t justify retreating to unrealistic “rational” models of behavior. The idea is not to assume behavior that makes analysis simple, but behavior that makes models realistic.

  9. Calling this “bounded rationality” is a misnomer. It implies that agents do not use all reasoning powers at their disposal, which under uncertainly may often be false.

  [ 6 ] Complexity and the Economy

  of radio transmission and receiving, broadcasting, relay circuits, early computation, and radar. And these novel technologies in turn demand and sup-

  ply yet further technologies. It follows that a novel technology is not just a one-time disruption to equilibrium, it is a permanent ongoing generator and

  demander of further technologies that themselves generate and demand still

  further technologies (Arthur, 2009). Notice again the self-reinforcing nature

  of this process. The result is not occasional disruption but ongoing waves of

  disruption causing disruptions, acting in parallel across the economy and at all scales within the economy. Technology change breeds further change endogenously and continually, and this throws the economy into a permanent state

  of disruption.

  Technological disruption acts on a somewhat slower timescale than the

  Brownian motion of uncertainty. But if anything it causes larger upheavals.

  And by itself it induces further uncertainty: businesses and industries simply do not know what technologies will enter their space next. Both uncertainty

  and technology then give us an economy where agents have no determinate

  means to make decisions.

  A picture is now emerging of the economy different from the standard

  equilibrium one. To the degree that uncertainty and technological changes

  are present in the economy—and certainly both are pervasive at all lev-

  els—agents must explore their way forward, must “learn” about the deci-

  sion problem they are in, must respond to the opportunities confronting

  them. We are i
n a world where beliefs, strategies, and actions of agents are

  being “tested” for survival within a situation or outcome or “ecology” that

  these beliefs, strategies, and actions together create. Further, and more

  subtly, these very explorations alter the economy itself and the situation

  agents encounter. So agents are not just reacting to a problem they are

  trying to make sense of; their very actions in doing so collectively re-form

  the current outcome, which requires them to adjust afresh. We are, in

  other words, in a world of complexity, a complexity closely associated with

  nonequilibrium.

  THEORIZING UNDER NONEQUILIBRIUM

  Where does this leave us? If the economy is large and constantly aboil with

  activity, then we would seem to be dealing here (to borrow a phrase from

  Schumpeter, 1954) with “a chaos that is not in analytical control.” Faced with this prospect in the past, economics has metaphorically thrown up its hands

  and backed away. But what if we don’t do this, what if we stand our ground

  and take nonequilibrium seriously, how then can we proceed? Can we say any-

  thing useful? What would we see? And above all, what would it mean to do

  theory under nonequilibrium?

  comPlexi t y economics [ 7 ]

  Certainly, many parts of the economy could still be treated as approximately at equilibrium, and standard theory would still be valid here. And other parts could be treated as temporarily diverging from strong attracting states, and

  we could study convergence here. But this would still be seeing the economy

  as a well-balanced machine temporarily prone to getting out of adjustment;

  and that neither gets us to the heart of seeing how the economy behaves out

  of equilibrium nor captures the creative side of nonequilibrium.

  A better way forward is to observe that in the economy, current circum-

  stances form the conditions that will determine what comes next. The econ-

  omy is a system whose elements are constantly updating their behavior based

  on the present situation.10 To state this in another way, formally, we can say that the economy is an ongoing computation—a vast, distributed, massively parallel, stochastic one.11 Viewed this way, the economy becomes a system