WouldBe Worlds

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In the past few years, a number of electronic worlds have been created by researchers associated with the Santa Fe Institute and elsewhere to study the properties of complex, adaptive systems. The authors cite just three such worlds here as prototypical examples of how to use the computer as a kind of information laboratory to investigate such systems.

Tierra. This world, created by naturalist Tom Ray, is populated by binary strings that serve as electronic surrogates for genetic material. As time unfolds, these strings compete with each other for resources, with which they create copies of themselves. New strings are also created by computational counterparts of the real-world processes of mutation and crossover. Over the course of time, the world of Tierra displays many of the features associated with evolutionary processes seen in the natural world, and hence can be used as a way of experimenting with such processes - without having to wait millions of years to bring the experiment to a conclusion. But it is important to keep in mind that Tierra is not designed to mimic any particular real-world biological process; rather, it is a laboratory within which to study neo-Darwinian evolution, in general.

TRANSIMS. For the past 3 years, a team of researchers at the Los Alamos National Laboratory headed by Chris Barrett has built an electronic counterpart of the city of Albuquerque, New Mexico, inside their computers. The purpose of this world, which is called TRANSIMS, is to provide a testbed for studying the flow of road traffic in an urban area of nearly half a million people. In contrast to Tierra, TRANSIMS is explicitly designed to mirror the real world of Albuquerque as faithfully as possible, or at least to mirror those aspects of the city that are relevant for road-traffic flow. Thus, the simulation contains the entire road traffic network from freeways to back alleys, together with information about where people live and work, as well as demographic information about incomes, children, type of cars, and so forth. So here we have a would-be world whose goal is to indeed duplicate as closely as possible a specific real-world situation.

Sugarscape. Somewhere between Tierra and TRANSIMS is the would-be world called Sugarscape, which was created by Joshua Epstein and Rob Axtell of The Brookings Institution in Washington, DC. This world is designed as a tool to study processes of cultural and economic evolution. On the one hand, the assumptions about how individuals behave and the spectrum of possible actions at their disposal is a vast simplification of the possibilities open to real people as they go through everyday life. On the other hand, Sugarscape makes fairly realistic assumptions about the things that motivate people to act in the way they do, as well as about how they go about trying to attain their goals. What is of considerable interest is the rich variety of behaviors that emerge from simple rules for individual action, and the uncanny resemblance these emergent behaviors have to what is actually seen in real life.

In order to conduct the kinds of repeatable, controlled experiments that natural scientists take for granted when trying to understand and create theories of physical and engineering systems, Epstein and Axtell decided to 'grow' a social order from scratch by creating an ever-changing environment and a set of agents who interact with each other and the environment in accordance with simple rules of survival. An entire social structure - trade, economy, culture - then evolves from the interactions ofthe agents. As Epstein remarks about social problems, ''You don't solve it, you evolve it.'' Epstein and Axtell call their laboratory in which societies evolve the 'CompuTerrarium'. Here is how it works.

The interacting agents are each graphically represented by a single colored dot on the landscape they inhabit, which is called the Sugarscape. Every location in the landscape contains time-varying concentrations of a food resource, called sugar. Each individual has a unique set of characteristics; some are fixed like sex, visual range for food detection, and metabolic rate, whereas others are variable like health, marital status, and wealth. The behavior of these agents is determined by a set of extremely simple rules that constitute nothing more than common-sense rules for survival and reproduction. A typical set of rules might be:

1. Find the nearest location containing sugar. Go there, eat as much as necessary to maintain your metabolism, and save the rest.

2. Breed if you have accumulated enough energy and other resources.

3. Maintain your current cultural identity (set of characteristics) unless you see that you are surrounded by many agents of different types ('tribes'). If you are, change your characteristics and/or preferences to fit in with your neighbors.

With even such primitive rules as this, strange and wondrous things begin to happen. A typical scenario is shown in Figure 3, where we see the sugar marked by yellow dots on the Sugarscape. The agents are initially distributed randomly on the landscape, red dots being agents that have a good ability to see food at a distance, blue dots representing more myopic agents. It is reasonable to expect that if no other considerations enter, natural selection would tend to favor good vision over time. Indeed this is the case, as seen by the center panel in Figure 3, showing a preponderance of red agents in the population. However, if the experiment is run again, giving agents the possibility of passing wealth on to their offspring in the form of sugar, we find that inheritance has a pronounced effect on survival. This is shown in the third panel of the figure, in which many more agents having poor vision are able to survive by making use of sugar willed to them by their parents.

Although this simple example is useful in illustrating the workings of the CompuTerrarium, it hardly suggests a revolution in our way of thinking about and studying social structures. For that we need to add a lot more whistles and bells to the system. Epstein and Axtell have done exactly this. When they add seasons so that sugar concentrations change periodically over time, the agents begin to migrate. When a second resource, spice, is introduced, a primitive economy emerges as a result of a new elementary rule: ''Look around for a neighbor having a commodity you need. Bargain with that neighbor until you reach a mutually satisfactory price. Trade at that price.'' Figure 4 shows the effect of this type of trading

Initial condition No inheritance With inheritance

Figure 3 Evolution on the Sugarscape.

Initial condition No inheritance With inheritance

Figure 3 Evolution on the Sugarscape.

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^ Agents forage for 'sugar and spice' Figure 4 The effects of trade in the Sugarscape.

If trade is allowed, they flourish

) Without trade, many starve

economy. In the first part of the figure, agents are simply foraging independently for both sugar and spice. In the middle panel we see the effect of beginning trade; now lots of agents flourish. Finally, the third panel shows the effect of turning off the trade. Without trade being allowed, many of the agents cannot survive.

There is certainly much more that can be said about the social laboratory constructed by Epstein and Axtell. Issues involving the emergence of cultural groups, combat, institutional structures, and the like can all be introduced to study myriad questions of interest to social scientists. The interested reader will certainly want to consult the monograph by Epstein and Axtell that details these and many other matters. It is cited in the references for this article. Here we must content ourselves with simply noting that the CompuTerrarium offers a platform to study society from the bottom up. With this view, we can explore social behavior that is dynamic, evolutionary, and locally simple. What could be better than to have a laboratory like this in which to do such experiments?

The main point in presenting these discussions of Tierra, TRANSIMS, and Sugarscape is to emphasize two points: (1) we need different types of would-be worlds to study different sorts of questions, and (2) each of these worlds has the capability of serving as a laboratory within which to test hypotheses about the phenomena they can represent. And, of course, it is this latter property that encourages the view that such computational universes will play the same role for the creation of theories of complex systems that chemistry labs and particle accelerators have played in the creation of scientific theories of simple systems. Gleick has given a fuller account of the technical, philosophical, and theoretical problems surrounding the construction and use of these silicon worlds.


The key components in each and every complex, adaptive system and a decent mathematical formalism to describe and analyze them would go a long way toward the creation of a viable theory of such processes. These key components are given as follows.

A medium-sized number of agents. In contrast to simple systems - like superpower conflicts, which tend to involve a small number of interacting agents - or large systems -like galaxies or containers of gas, which have a large enough collection of agents that we can use statistical means to study them - complex systems involve what we might call a medium-sized number of agents. Just like Goldilocks's porridge, which was not too hot and not too cold, complex systems have a number of agents that are not too small and not too big, but just right to create interesting patterns of behavior.

Intelligent and adaptive agents. Not only are there a medium-sized number of agents, these agents are intelligent and adaptive. This means that they make decisions on the basis of rules, and that they are ready to modify the rules they use on the basis of new information that becomes available. Moreover, the agents are able to generate new rules that have never before been used, rather than being hemmed in by having to choose from a set of preselected rules for action. This means that an ecology of rules emerges, one that continues to evolve during the course of the process.

Local information. In the real world of complex systems, no agent knows what 'all' the other agents are doing. At most, each person gets information from a relatively small subset of the set of all agents, and processes this 'local' information to come to a decision as to how he or she will act. In the Sugarscape, for instance, what the traders adjacent to a given individual in the market are doing constitutes the local information that the individual has available to help decide what to do next.

So these are the components of all complex, adaptive systems like the Sugarscape, TRANSIMS, or Tierra situations - a medium-sized number of intelligent, adaptive agents interacting on the basis of local information. At present, there appears to be no known mathematical structures within which we can comfortably accommodate a description of'any' of these worlds. This suggests a situation completely analogous to that faced by gamblers in the seventeenth century, who sought a rational way to divide the stakes in a game of dice when the game had to be terminated prematurely (probably by the appearance of the police or, perhaps, the gamblers' wives). The description and analysis of that very definite real-world problem led Fermat and Pascal to the creation of a mathematical formalism we now call probability theory. At present, complex-system theory still awaits its Pascal and Fermat. The mathematical concepts and methods currently available were developed, by and large, to describe systems composed of material objects like planets and atoms. It is the development of a proper theory of complex systems that will be the capstone ofthe transition from the material to the informational.

See also: Biodiversity; Cybernetics; Emergent Properties; Hierarchy Theory in Ecology; Self-Organization; Stability versus Complexity; Systems Ecology.

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