Complex Adaptive Systems: An Introduction to Computational Models of Social Life (Princeton Studies in Complexity Book 14)

by John H. Miller

It is the interest in between stasis and utter chaos. The world tends not to be completely frozen or random, but rather it exists in between these two states. We want to know when and why productive systems emerge and how they can persist.

In a complicated world, the various elements that make up the system maintain a degree of independence from one another.

Complexity arises when the dependencies among the elements become important. In such a system, removing one such element destroys system behavior to an extent that goes well beyond what is embodied by the particular element that is removed. Complexity is a deep property of a system, whereas complication is not. A complex system dies when an element is removed, but complicated ones continue to live on, albeit slightly compromised. Removing a seat from a car makes it less complicated; removing the timing belt makes it less complex (and useless). Complicated worlds are reducible, whereas complex ones are not.

Social agents must predict and react to the actions and predictions of other agents. The various connections inherent in social systems exacerbate these actions as agents become closely coupled to one another. The result of such a system is that agent interactions become highly nonlinear, the system becomes difficult to decompose, and complexity ensues.

Regardless of the approach, the quest of any model is to ease thinking while still retaining some ability to illuminate reality.

If heterogeneity is a key feature of complex systems, then traditional social science tools—with their emphases on average behavior being representative of the whole—may be incomplete or even misleading.

Hives with genetic diversity produce much more stable internal temperatures. As the temperature drops, only a few bees react and huddle together, slowly bringing up the temperature. If the temperature continues to fall, a few more bees join into the mass to help out. A similar effect happens when the hive begins to overheat. This moderate and escalating response prevents wild swings in temperature. Thus, the genetic diversity of the bees leads to relatively stable temperatures that ultimately improve the health of the hive.

The key feature of disorganized complexity is that the interactions of the local entities tend to smooth each other out.

Thus, in cases of disorganized complexity, it should be easy to derive fairly precise emergence theorems based on fundamental concepts that are centuries old. Unfortunately, disorganized complexity accounts for only one part of our world.

Under organized complexity, the relationships among the agents are such that through various feedbacks and structural contingencies, agent variations no longer cancel one another out but, rather, become reinforcing.

Rather, we are suggesting that it is time to take our old components and use them in new ways. Such an approach is not without risks, for surely some of the new structures that we build will fall; but others will stand and inspire.

While computation as theory can have many meanings, and surely over time it will acquire new ones, the focus of the models in this book is on computations that are designed to improve our theoretical understanding of the world by relying on agent-based objects.

Second, we focus here on models that are composed of a set of simple algorithmic components, each associated with an individual agent.

AGENT-BASED OBJECT models offer a new theoretical portal from which to explore complex adaptive social systems.

Although some of the most interesting driving forces in social systems are related to process, being able to ignore things may be a real advantage in modeling (though, of course, explicitly knowing about and appreciating the impact of what we are ignoring is still needed).

Much of the creativity required for developing such models is in finding a good way to represent the key issues (in computational learning, this is known as the “representation problem”).

The ability to analyze systems of “adaptive” agents systematically is an area of great promise for social scientists, but it does face a potentially serious scientific challenge: can we create a coherent science of adaptive agents?

A more interesting and, in our opinion, more promising approach is to “let a thousand flowers bloom” in hopes that large equivalence classes of adaptive behavior will be discovered.

At the moment, the evidence from computational models hints at the potential existence of a large equivalence class of adaptive behavior.

Outfielders do not run in a straight line from whatever their location happens to be when the ball is hit to the place on the field where the ball will land. Instead, studies (for example, McBeath et al., 1995) indicate that outfielders run in an arc-shaped path that is consistent with a simple, vision-based behavioral heuristic that keeps the ball on a linear trajectory relative to the background.

Computational models using agent-based objects are a very natural way to explore the dynamic behavior of a system. Regardless of the presence of equilibria, such behavior is often the most interesting part of the system. As Ursula Le Guin (1969, 220) said, “It is good to have an end to journey toward; but it is the journey that matters, in the end.”

Being able to manipulate easily the scaling of our models may promote the discovery of key scaling laws for complex adaptive systems. In biology, the branching features of a variety of respiratory systems scale as a fixed power of body size across at least twenty-five orders of magnitude.

Agent-based object models inherently provide constructive “proofs” to propositions. In particular, once we specify an agent-based object model and find that it leads to a coherent macrophenomenon, we have thereby found at least one set of microconditions that is sufficient to generate the macro-observations.

Nonetheless, Epstein (1999) argues that this “generative” approach—that is, we must grow it to show it—is a distinct and powerful way to do social science.

The problem is thus in no way solved if we can show that all the facts, if they were known to a single mind (as we hypothetically assume them to be given to the observing economist), would uniquely determine the solution; instead we must show how a solution is produced by the interactions of people each of whom possesses only partial knowledge.

Such heuristics may go wrong at times, and indeed a lot of work in the area of behavioral decision theory is focused on finding situations where normally useful rules go bad. After all, to err is economic.

Models of complex systems phenomena should be simple, not complicated.

More than two-thirds of the chaotic rules are attractor rules, and all of the complex rules fall into this set. This partly explains why complex rules have chaotic edges, as the complex and chaotic rules belong to the same set.