The Self-Organizing Economy — Paul Krugman on Emergence

Order from instability

Imagine you have a bag filled with a random assortment of mixed nuts. Let’s also say that you’re feeling rather mischievous today. So you give the bag a long shake. What happens? Assuming no spillage, the larger nuts tend to rise to the top as the smaller nuts shuffle towards the bottom. Somehow, your random shaking begins to organize what was originally a disorderly configuration. And granular convection of this sort have been found to occur in fields as disparate as manufacturing, geology, and astronomy. In his lecture series, The Self-Organizing Economy, Paul Krugman suspects that such “order from instability” may be a principle of the social world.

Micromotives and macrobehavior

Indeed, peanut phenomena seem to manifest in many parts of society. Game theorist Thomas Schelling showed, for example, in his checkerboard model of segregation, that if people exhibit even a mild bias for neighbors who are similar to themselves, cultural partitions between suburbs will arise over time. So even if people are tolerant of neighbors who are dissimilar to them, the demography may still reorganize as people slowly shuffle about to destinations they prefer ever so slightly.

Of course, reality is more complicated than Schelling’s models. Many factors affect our housing and neighborhood choices. But the mechanism may help to explain the sorting we tend to see in living patterns, marital choices, political leanings, corporate hirings, and so on. It also illustrates how short-range micromotives can produce large-scale macrobehavior. To Krugman, this is an example of self-organization and how social order from disorder, whether desirable or not, can emerge spontaneously.

Centralizing and decentralizing forces

To study complex social patterns like these, it can help to look for centralizing and decentralizing forces, Krugman says. Consider, for example, the tendency for states and countries to organize polycentrically—in which there are usually a few large cities and many smaller townships. Why do such distributions tend to come about in the first place?

Economists believe that this is due in part to the interdependent tradeoffs that businesses and households make. For instance, while businesses dislike head-to-head competition, they may also benefit from colocation if it pools their customers together. Similarly, while corporations may prefer mega facilities for scale economies, they may also need to spread out to reach buyers and lower transportation costs. In both cases, businesses face a tension between concentration and dispersion.

What’s important about these interactions is twofold, Krugman notes. Firstly, neither the centralizing or dispersing effects dominate. Otherwise, people would either pack themselves in like canned tuna or spread themselves outwardly and evenly. Secondly, the centralizing force seems to have a shorter range. Proximity, after all, is helpful only up to a point. This may explain why you’re likely to find bunches of retailers in a suburban mall, but are unlikely to find bunches of shopping malls together. I’m oversimplifying, of course. But I hope it hints at a process for which major hubs and minor satellites can emerge.

First and second nature

Moreover, we have to watch for ways in which micromotives and macrobehavior might rewrite themselves. In Nature’s Metropolis, the historian William Cronon makes a distinction between first nature and second nature in his studies of urban development. Indeed, for much of history, our agricultural, migratory, and settlement patterns centered around our natural landscape. Land productivity, natural resources, and geographic considerations affected our spatial choices.

But as our institutions, empires, tradebelts, and communication networks flourished, our ‘second nature’ became the prime mover of social patterns. Krugman notes, for instance, how Chicago’s position as a Great Lakes port was later “overshadowed” and “self-reinforced” by railway lines. “Railroads aimed at Chicago because it was the economic center of its region and thereby made its centrality all the greater.”

Inside these dynamics, Krugman adds, are “underlying approximate simplicities”—a mixture of chance and regularity. If modern history began itself anew, economic, financial, and technology centers would probably emerge again. But their exact locations would be hard to predict. “Silicon Valley [or Wall Street] might, given a slightly different sequence of events, have been in Los Angeles, Massachusetts, or even Oxfordshire.”

Grecian urns and power laws

Regularities in nature and society can manifest in unexpected ways. To borrow another Krugman example, “take a ceramic object, say a Grecian urn, and throw it hard against a stone wall, so that it shatters randomly into innumerable pieces.” Despite the mess you’ve just made, “a strange hidden order emerges… The process of fragmentation produces the simple order of a power law [distribution of mass].”

You probably know, of course, that power laws are recurring themes. In nature, the size-to-frequency distributions of earthquake magnitudes, meteorite impacts, animal metabolic rates, and pulsar glitches, for example, conform to their own power laws. The same is similarly true of many social phenomena. They include the size-to-rank distributions of word use, city populations, academic citations, insurance claims, income levels, artworks prices, and stock price movements.

While each of their underlying processes are complicated, aspects of their behavior seem to ‘self-organize’ into predictable hierarchies. From ceramic fragments to earthquakes to city populations, small events tend to occur more frequently than big events. And we can derive empirical relationships to describe them.

Order from random growth

That doesn’t mean, however, that regularities are the result of a conscious watchmaker or invisible hand. “Order from random growth”, Krugman suspects, may be another principle of self-organizing phenomena.

Herbert Simon showed, for example, how certain random processes can generate power-law distributions over time. As Krugman explains, “imagine an urban system with a growing population.” The population, however, does not come about smoothly, but in “discrete lumps that are neither too small nor too big.” Let’s also assume then that these lumps can either land somewhere new to birth a city, or to “clump” to preexisting residences. Now, if the lumps and clumps occur with fixed probabilities, and the clumps are distributed proportionately by size (that is the clumps are more likely to go to bigger cities than smaller cities), then it is possible, roughly speaking, for a power-law distribution of city size to emerge after some time.

In his paper, Simon suggests that such stochastic processes may help to explain the “distribution of words in prose samples by their frequency of occurrence, distributions of scientists by number of papers published, distributions of cities by population, distributions of incomes by size, and [even] distributions of biological genera by number of species.” The structural patterns in Simon’s model hold in part, however, because it assumes that “the expected rate of growth must be independent of scale”, Krugman explains. The lumps and clumps continue to go about in the same way as the system grows and unfolds. Again, real life is more complex. But it is a helpful mental model nonetheless.

Percolation economics

For another example, Krugman points to Jose Scheinkman and Michael Woodford’s models of economic fluctuations. The Scheinkman-Woodford model, you see, imagines the economy as a dense stack of layers of firms and industries. So for companies on the top layer to produce final goods like shirts or televisions, they must buy intermediate inputs like materials or electronics from the layer beneath them. And the second layer of companies must then purchase intermediate goods from the layer beneath them, and so on. So you can imagine how orders for shirts, televisions, bicycles, burgers, and whatnot can trigger a chain reaction through the stack. And how temporary and persistent frictions, delays, disruptions, innovations, and crises can cascade back-and-forth through the system. Humble snowballs at the top can turn into avalanches by the time it reaches the bottom. What’s more, the larger the economy, the longer the potential chain reaction.

Phase-locking economies

Krugman speculates further, suggesting that the global business cycle might be a product of phase-locking. The phenomena dates back to the 17th century when the physicist Christiaan Huygens observed an “odd sympathy” between the clocks in his room—for the “pendula of the clocks were oscillating in perfect consonance… [due to] the small vibrations of the wooden bar on which the clocks were hanging.” Somehow, their weak coupling through the wooden bar had led to synchronized behavior over time. Today, phase-locked-loops find applications in fields like computing and telecommunications. And research suggests that even mammalian circadian clocks and cell cycles may use a “biological equivalent of the phase locking first discovered by Huygens.”

In economics, Krugman notes that coupling in international trade is weaker than the news media may make it seem. The United States and the European Union, for example, exports only around 2 to 3 percent of their annual output to one another. Yet, there remains a strong global interdependence between the world powers. For better and worse, they tend to share their booms and recessions together. But if we return to our mental model of stacks and layers, we can imagine how a recession in one country (and a shortfall in exports) might conflagrate to neighbors who happen to be on the fringe of recession themselves. The same is similarly true of countries at the precipice of recovery. A boom overseas might be the kickstart that others need. As Krugman writes, “two economies [do] not need to be strongly linked to develop a synchronized cycle.”

A self-organizing world

So what can we take from all this loopiness of order and instability? Perhaps it goes without saying that economies are complex, self-organizing things—that somehow, under all the instability and haphazardness of individual interaction, immense cultures, languages, institutions, enterprises, and coalitions emerge. Self-correcting and self-amplifying forces are forever twisting in concert and cascades.

It is my suspicion, and that of Krugman and others, that there are hidden principles that characterize the structure and organization of many complex phenomena in nature and society. Random growth processes, Darwinian selection, Huygen synchronization, and empirical power laws are examples of this. Their concepts are applicable to many fields. There remains, however, a lot more to be discovered and understood. And field-spanning analogies are always a risky game. But the economist can rejoice in knowing at least one thing. As Paul Krugman writes:

“If economists do understand one thing much better than the lay public, it is the sheer complexity of the economic system… After all, what is general equilibrium theory but a formalization of the proposition that everything in the economy affects everything else?”
Paul Krugman. (1995). The Self Organizing Economy.

Sources and further reading

Arthur, W. B. (1994). Increasing Returns and Path Dependence in the Economy.
Arthur, W. B. (2014). Complexity and the Economy.
Bak, P. (1991). Self-Organizing Criticality.
Bak, P. (1996). How Nature Works.
Kauffman, S. (1993). The Origins of Order.
Kauffman, S. (2019). A World Beyond Physics.
Krugman, P. (1991). Increasing Returns and Economic Geography.
Krugman, P. (1993). First Nature, Second Nature, and Metropolitan Location.
Krugman, P. (1995). The Self Organizing Economy.
Lewin, R. (1992). Complexity: Life at the Edge of Chaos.
Mandelbrot, B., & Hudson, R. (2004). The Misbehaviour of Markets.
Murphy, R., Shleifer, A., and Vishny, R. (1989). Industrialization and the Big Push.
Nicolis, G., and Prigogine, I. (1989). Exploring Complexity.
Prigogine, I., and Stengers, I. (1984). Order out if Chaos.
Rigney, D. (2010). The Matthew Effect.
Scheinkman,J. A., and Woodford, M. (1994). Self-Organized Criticality and Economic Fluctations.
Schelling, T. (1978). Micromotives and Macrobehavior.
Simon, H. (1955). On a Class of Skew Distribution Functions.
Simon, H. (1962). The Architecture of Complexity.
Waldrop, W. M. (1993). Complexity.

The Self-Organizing Economy — Paul Krugman on Emergence and Instability