Complexity and the Economy
Complexity and the Economy

Complexity and the Economy

I had gone back to Stanford, where I was teaching a course in economic development. It occurred to me, gradually at first, that John and I could design a primitive artificial economy that would execute on my computer, and use his learning system to generate increasing sophisticated action rules that would build on each other and thus emulate how an economy bootstraps its way up from raw simplicity to modern complication. In (Location 147)

A day later as the computation ran, I would look again and see that a currency had evolved for trading, and with it some primitive banking. Still later, joint stock companies would emerge. Later still, we would see central banking, and labor unions with workers occasionally striking, and insurance companies, and a few days later, options trading. The idea was ambitious and I told Holland about it over the phone. He was interested, but neither he nor I could see how to get it to work. (Location 151)

Instead of simulating the full development of an economy, we could simulate a stock market. The market would be completely stand-alone. It would exist on a computer and would have little agents—computerized investors that would each be individual computer programs—who would buy and sell stock, try to spot trends, and even speculate. We could start with simple agents and allow them to get smart by using John’s evolving condition-action rules, and we could study the results and compare these with real markets. John liked the idea. (Location 158)

But then looking more closely, we noticed the emergence of real market phenomena: small bubbles and crashes were present, as were correlations in prices and volume, and periods of high volatility followed by periods of quiescence. Our artificial market was showing real-world phenomena that standard economics with its insistence on identical agents using rational expectations could not show. (Location 180)

We were aware at the time that we were doing something different. We were simulating a market in which individual behavior competed and evolved in an “ecology” that these behaviors mutually created. (Location 183)

The interest had in fact been kindled years before, when I was exploring the idea of technologies competing for adoption. I had noticed that technologies—all the technologies I was looking at—had not come into being out of inspiration alone. They were all combinations of technologies that already existed. The laser printer had been put together from—was a combination of—a computer processor, a laser, and xerography: the processor would direct the laser to “paint” letters or images on a copier drum, and the rest was copying. (Location 221)

In 1992 I had been exploring jet engines out of curiosity and I wondered why they had started off so simple yet within two or three decades had become so complicated. (Location 226)

They had a central functioning module, and other sub-modules hung off this to set it up properly and to manage it properly. (Location 228)

I had started to read widely on technology, and decided I would study and know very well several particular technologies, somewhere between a dozen and twenty. In the end these included not just jet engines, but early radio, radar, steam engines, packet switching, the transistor, masers, computation, and even oddball “technologies” such as penicillin. (Location 233)

working. I began to see common patterns emerging in how technologies had formed and come into being. They all captured and used phenomena: ultimately technologies are phenomena used for human purposes. And phenomena came along in families—the chemical ones, the electronic ones, the genomic ones—so that technologies formed into groups: industrial chemistry, electronics, biotechnology. (Location 237)

Technology—the whole collection of individual technologies—evolved in the sense that all technologies at any time, like all species, could trace a line of ancestry back to earlier technologies. (Location 241)

Could we create a computer experiment in which a soup of primitive technologies could be combined at random and the resulting combination—a potential new technology—tossed out if not useful but retained if useful and added to the soup for further combination? Would (Location 253)

And actual technology had evolved in this way. It had bootstrapped its way from few technologies to many, and from primitive ones to highly complicated ones. (Location 267)

Of particular wonder were the mechanisms by which the collective of technology evolved, and the realization that technology is a thing with considerable logical structure. Technology, I believe, studied in itself, is every bit as complicated and structured as the economy, or the legal system. And it is an object of considerable beauty. (Location 285)

recognize that standard neoclassical economics comes out of a particular way of looking at the world. Neoclassical economics inherited the Enlightenment view that behind the seeming disorder of the world lay Order and Reason and Perfection. And it inherited much from the physics of the late 1800s, in particular the idea that large numbers of interacting identical elements could be analyzed collectively via simple mathematical equations. By the mid-1900s this led in turn to a hope that the core of economic theory could be captured in simple mathematically expressed principles and thereby axiomatized. (Location 302)

And nobody could claim either that unemployment rates of 20% and upward in some of the European economies were due to the suddenly changed preferences of the labor force; people wanted jobs just as before. (Location 315)

difficulties. One lesson Western thought has had to learn slowly in modern times is that if we try hard enough to reduce anything to pure logic—for example if we try to pin down a final meaning of such concepts as Truth, or Being, or Life, or if we try to reduce some field such as philosophy or mathematics (or economics for that matter) to a narrow set of axioms—such attempts founder. (Location 318)

The world cannot be reduced to pure logic and caged within it. Sooner or later it slips out to reveal its true messiness, and all such projects fail. (Location 321)

another. So too are theories of development that rely increasingly on understanding institutions and the workings of technology. And so too is the approach offered here which now has very many practitioners besides our initial group at Santa Fe. (Location 324)

They too saw the economy as emerging from its technologies, as changing structurally, as not necessarily being in equilibrium, and with its decision-makers facing fundamental uncertainty. (Location 328)

We are beginning to have a theoretical picture of the economy in formation and in nonequilibrium. (Location 331)

Taken together, a theme or framework for thinking emerges from the papers here. In the place of agents in well-defined problems with well-defined probabilistic outcomes using perfect deductive reasoning and thereby arriving at an equilibrium, we have agents who must make sense out of the situation they face, who need to explore choices using whatever reasoning is at hand, and who live with and must adjust to an outcome that their very adjustments may cause perpetually to change. (Location 341)

economics. Complexity economics builds on the proposition that the economy is not necessarily in equilibrium: economic agents (firms, consumers, investors) constantly change their actions and strategies in response to the outcome they mutually (Location 391)

These emerge probabilistically, last for some time and dissipate, and act at the “meso-level” of the economy (between the micro- and macro-levels). (Location 397)

Complexity economics holds that the economy is not necessarily in equilibrium, that computation as well as mathematics is useful in economics, that increasing as well as diminishing returns may be present in an economic situation, and that the economy is not something given and existing but forms from a constantly developing set of institutions, arrangements, and technological innovations. (Location 405)

economy. It gives a different view, one where actions and strategies constantly evolve, where time becomes important, where structures constantly form and re-form, where phenomena appear that are not visible to standard equilibrium analysis, and where a meso-layer between the micro and the macro becomes important. (Location 417)

a world that is organic, evolutionary, and historically contingent. (Location 420)

The economy is a vast and complicated set of arrangements and actions wherein agents—consumers, firms, banks, 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. (Location 421)

Complexity is not a theory but a movement in the sciences that studies how the interacting elements in a system create overall patterns, and how these overall patterns in turn (Location 428)

cause the interacting elements to change or adapt. (Location 429)

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 (Location 449)

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. (Location 451)

Under equilibrium by definition there is no scope for improvement 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. (Location 456)

First, fundamental uncertainty. All problems of choice in the economy involve something that takes place in the future, perhaps almost immediately, perhaps at some distance of time. (Location 472)

Therefore they involve some degree of not knowing. (Location 473)

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. (Location 476)

There is no “optimal” move. Things worsen when other agents are involved; such uncertainty then becomes self-reinforcing. (Location 478)

To the degree that outcomes are unknowable, the decision problems they pose are not well-defined. (Location 483)

None of this means that people cannot proceed in the economy, or that they do not choose to act. (Location 487)

“The future is imagined by each man for himself and this process of the imagination is a vital part of the process of decision.” (Location 492)

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 equilibrium that might be attained.” That source was “new combinations of productive 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. (Location 500)

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 (Location 509)

Both uncertainty and technology then give us an economy where agents have no determinate means to make decisions. (Location 515)

A better way forward is to observe that in the economy, current circumstances form the conditions that will determine what comes next. (Location 533)

The economy is a system whose elements are constantly updating their behavior based on the present situation. (Location 534)

There is a danger that seeing the economy this way is merely bowing to a current fashion in science, but the idea allows me to make an important point. (Location 538)

For highly interconnected systems, equilibrium and closed-form solutions are not the default outcomes; (Location 549)

Of course the algorithm behind the actual economy is not randomly chosen, it is highly structured, so it may be that the actual economy’s “computations” always have simple outcomes. Or it may equally be that the economy’s computations are always unordered and amorphous. Usually in the parts of the economy we study, neither is the case. (Location 553)

In 1991 Kristian Lindgren constructed a computerized tournament where strategies competed in randomly chosen pairs to play a repeated prisoner’s dilemma game. (The details of the prisoner’s dilemma needn’t concern us; think of this as simply a game played by a specified current set of strategies.) (Location 570)

If strategies did well they replicated and mutated, if they did badly they were removed. (Location 573)

What emerges computationally is an ecology—an ecology of strategies, each attempting to exploit and survive within an environment created by itself and other strategies attempting to exploit and survive. (Location 580)

Notice that evolution has entered, but it hasn’t been brought in from outside, it has arisen in the natural tendency of strategies to compete for survival. The point is general in this type of economics. (Location 582)

In some runs we see the quick emergence of complicated strategies, in others these appear later on. And yet there are constants: phenomena such as coexistence among strategies, exploitation, the spontaneous emergence of mutualism, sudden collapses, periods of stasis and unstable change. The picture resembles paleozoology more than anything else. (Location 588)

My answer is that theory does not consist of mathematics. Mathematics is a technique, a tool, albeit a sophisticated one. Theory is something different. (Location 596)

We can see the economy, or the parts of it that interest us, as the ever-changing outcome of agents’ strategies, forecasts, and behaviors. And we can investigate these parts, and also classic problems within economics—intergenerational transfers, asset pricing, international trade, financial transactions, banking—by constructing models where responses are specified not just at equilibrium but in all circumstances. (Location 606)

Trivially, an equilibrium speed emerges, and if we were restricting solutions to equilibrium that is all we would see. But in practice at high density, a nonequilibrium phenomenon occurs. (Location 617)

This immediately compresses the flow, which causes further slowing of the cars behind. (Location 620)

Second, the phenomenon is temporal, it emerges or happens within time, and cannot appear if we insist on equilibrium.17 And third, the phenomenon occurs neither at the micro-level (individual car level) nor at the macro-level (overall flow on the road) but at a level in between—the meso-level. (Location 623)

The first is self-reinforcing asset-price changes, or in the vernacular, bubbles and crashes. To see how these are generated consider the Santa Fe artificial stock market (Palmer et al., 1994; Arthur et al., 1997). (Location 628)

Our market becomes an ecology of forecasting methods that either succeed or are winnowed out, an ecology that perpetually changes as this happens.18 And we see several phenomena, chief among them, spontaneous bubbles and crashes. (Location 634)

A second temporal phenomenon is clustered volatility. This is the appearance of random periods of low activity followed by periods of high activity. In our artificial market these show up as periods of low and high price volatility. Low volatility reigns when agents’ forecasts are working reasonably well mutually; then there is little incentive to change them or the results they produce. (Location 645)

if the network is sparsely connected the change will sooner or later peter out for lack of onward connections. (Location 653)

the change will propagate and continue to propagate. In a network of banks, an individual bank might discover it holds distressed assets. (Location 654)

Generally in complex systems, phenomena do not appear until some underlying parameter of the model that depicts the intensity of adjustment or the degree of connection passes some point and reaches some critical level. (Location 660)

But if our investors explore at a faster, more realistic rate, the market develops a “rich psychology” of differing forecasting beliefs and starts to display temporal phenomena: complex behavior reigns. (Location 663)

We can now begin to see how such phenomena—or order, or structures, if you like—connect with complexity. Complexity, as I said, is the study of the consequences of interactions; it studies patterns, or structures, or phenomena, that emerge from interactions among elements—particles, or cells, or dipoles, or agents, or firms. (Location 670)

Complexity studies how such changes play out. Or, to put it another way, complexity studies the propagation of change through interconnected behavior. (Location 674)

We can now say why nonequilibrium connects with complexity. Nonequilibrium in the economy forces us to study the propagation of the changes it causes; and complexity is very much the study of such propagations. It follows that this type of economics properly lies within the purview of complexity. (Location 688)

They operate typically at all scales—network events can involve just a few individual nodes or they can be felt right across the economy. (Location 693)

Such positive feedbacks disturb the status quo, they cause nonequilibrium. And they cause structures to appear. A small backup in traffic causes further backup and a structure forms, in this case a traffic jam. This is where the Brownian motion I alluded to comes in; it brings perturbations around which small movements nucleate; positive feedback magnifies them and they “lock in,” in time eventually to dissipate. (Location 712)

The process that increasing returns bring into being is by now well known. What I would add is that positive feedbacks are present more widely in the economy than we previously thought: they show up not just with firms or products, but in small mechanisms and large, in decision behavior, market behavior, financial behavior, and network dynamics. (Location 727)

Their counterparts in physics are multiple metastable states, unpredictability, phase- or mode-locking, high-energy ground states, and non-ergodicity. Once again these are properties we associate with formal complexity. (Location 731)

The economy continually creates and re-creates itself, and it does this by creating novel elements—often novel technologies and institutions—which produce novel structures as it evolves. (Location 736)

Complexity should be able to help here; it is very much about the creation and re-creation of structure. (Location 739)

Technology isn’t the only agent of change in the economy but it is by far the main one (Solow, 1957). (Location 742)

And it sees technologies as formless; they just somehow arrive, singly and randomly, with no structure to how they build out or how they change the economy in character over time. (Location 746)

A complexity view would put technologies in the foreground, and prices and quantities in the back. (Location 748)

In doing this it would focus directly on the collection of technologies present at any time, and ask how this collection evolves: how its members come into being, how they create and re-create a mutually supporting set, and how this alters the economy structurally over time. (Location 750)

define individual technologies as means to human purposes. (Location 753)

We now have a system where novel elements (technologies) constantly form from existing elements, whose existence may call forth yet further elements. (Location 758)

The economy we can then say emerges from its arrangements, its technologies: it is an expression of its technologies. (Location 762)

becomes an ecology of its means of production (its technologies), one where the technologies in use need to be mutually supporting and economically consistent. (Location 763)

Most of this demand comes from the needs of technologies themselves. The automobile “demands” or calls forth the further technologies of oil exploration, oil drilling, oil refining, mass manufacture, gasoline distribution, and car maintenance. (Location 766)

The steps involved yield the following algorithm for the formation of the economy. (Location 769)

The algorithm may be simple, but once set in motion it engenders rich, patterned, endlessly novel behavior. (Location 786)

New technologies often enter in groups (Perez, 2002; Arthur, 2009): over decades, families of technologies, the steam-driven ones, electrical ones, chemical ones, digital ones, enter. (Location 788)

And they build haltingly from one or two early central technologies then fill in the needed sub-technologies. These bodies of technology are not adopted within the economy, rather they are encountered by industries, combining with business processes that already exist and causing new activities, new incentives, new available processes, and little irruptions in the shape of little firms, a few of which go on to become large firms. (Location 791)

the set of arrangements and activities that satisfy our needs—builds out as a result of all this. Indeed the economy is the result of all this. (Location 795)

A few simple properties of technology yield a system of changing elements (technologies), each new element created from previous elements, each causing replacements, and all bringing on an ever-changing set of demands for further elements, the whole channeled and structured by the properties and possibilities of the dominant families of phenomena recently captured. (Location 798)

Novel technologies form from existing technologies, so the collective of technology is self-producing or autopoietic. (Location 801)

It forms from its technologies and mediates the creation of further technologies and thereby its own further formation. Here again we are very much in complexity territory. (Location 802)

On a longer time scale, the large bodies of technology define a thematic way by which operations in the economy are carried out. (Location 806)

the economy changes structurally. (Location 807)

The reason is that the evolutionary process is based on mechanisms that work in steps and trigger each other, and it continually defines new categories—new species. (Location 817)

mathematical; it is process-based, not quantity-based. In a word it is procedural. (Location 821)

One is allocation within the economy: how quantities of goods and services and their prices are determined within and across markets. This is represented by the great theories of general equilibrium, international trade, and game-theoretic analysis. (Location 833)

The other is formation within the economy: how an economy emerges in the first place, and grows and changes structurally over time. This is represented by ideas about innovation, economic development, structural change, and the role of history, institutions, and governance in the economy. (Location 834)

structures. By contrast neoclassical economics handles time poorly (Smolin, 2009, 2013). At equilibrium an outcome simply persists and so time largely disappears; or in dynamic models it becomes a parameter that can be slid back and forth reversibly to denote the current outcome (Harris, 2003). This has made many economic thinkers uncomfortable (Robinson, 1980). In 1973 Joan Robinson said famously, “Once we admit that an economy exists in time, that history goes one way, from the irrevocable past into the unknown future, the conception of equilibrium...becomes untenable. The whole of traditional economics needs to be thought out afresh.” (Location 856)

The failures of economics in the practical world are largely due to seeing the economy in equilibrium. (Location 872)

2008—all these were caused in no small part by the exploitation of the system by a few well-positioned players, or by markets that careened out of control (Location 875)

by definition, equilibrium is a condition where no agent has any incentive to diverge from its present behavior, therefore exploitive behavior cannot happen. And it cannot see extreme market behavior easily either: divergences are quickly corrected by countervailing forces. By its base assumptions, equilibrium economics is not primed to look for exploitation of parts of the economy or for system breakdowns. (Location 877)

Theory in turn becomes not the discovery of theorems of undying generality, but the deep understanding of mechanisms that create these patterns and propagations of change. (Location 896)

The type of rationality assumed in economics—perfect, logical, deductive rationality—is extremely useful in generating solutions to theoretical problems. But it demands much of human behavior, much more in fact than it can usually deliver. (Location 1144)

For example, the game tic-tac-toe is simple, and one can readily find a perfectly rational, minimax solution to it; but rational “solutions” are not found at the depth of checkers; and certainly not at the still modest depths of chess and Go. (Location 1148)

Objective, well-defined, shared assumptions then cease to apply. In turn, rational, deductive reasoning (deriving a conclusion by perfect logical processes from well-defined premises) itself cannot apply. The problem becomes ill-defined.     Economists, of course, are well aware of this. The question is not whether perfect rationality works, but rather what to put in its place. How does one model bounded rationality in economics? Many ideas have been suggested in the small but growing literature on bounded rationality; but there is not yet much convergence among them. (Location 1154)

This is a system in which learning takes place. Agents “learn” which of their hypotheses work, and from time to time they may discard poorly performing hypotheses and generate new “ideas” to put in their place. Agents linger with their currently most believable hypothesis or belief model but drop it when it no longer functions well, in favor of a better one. This causes a built-in hysteresis. A belief model is clung to not because it is “correct”—there is no way to know this—but rather because it has worked in the past and must cumulate a record of failure before it is worth discarding. In general, there may be a constant slow turnover of hypotheses acted upon. One could speak of this as a system of temporarily fulfilled expectations—beliefs or models or hypotheses that are temporarily fulfilled (though not perfectly), which give way to different beliefs or hypotheses when they cease to be fulfilled. (Location 1189)

This emergent ecology is almost organic in nature. For, while the population of active predictors splits into this 60/40 average ratio, it keeps changing in membership forever. This is something like a forest whose contours do not change, but whose individual trees do. These results appear throughout the experiments and are robust to changes in types of predictors created and in numbers assigned. (Location 1263)

One explanation might be that 60 is a natural “attractor” in this bar problem; in fact, if one views it as a pure game of predicting, a mixed strategy of forecasting above 60 with probability 0.4 and below it with probability 0.6 is a Nash equilibrium. Still, this does not explain how the agents approximate any such outcome, given their realistic, subjective reasoning. To (Location 1267)