DESIMMAL modeling, simulation, animal-farm system

The complexity of cooperation; agent based models of competition and collaboration (Axelrod, 1997)

Complexity theory involves the study of many actors and their interactions. The actors may be atoms, fish, people, organizations, or nations. Their interactions may consist of attraction, combat, mating, communication, trade, partnership, or rivalry. Because the study of large numbers of actors with changing patterns of interactions often gets too difficult for a mathematical solution, a primary research tool of complexity theory is computer simulation. The trick is to specify how the agents interact, and then observe properties that occur at the level of the whole society. For example, with given rules about actors and their interactions, do the actors tend to align into two competing groups? Do particular strategies dominate the population? Do clear patterns of behavior develop? The simulation of agents and their interactions is known by several names, including agent-based modeling, bottom-up modeling, and artificial social systems. Whatever name is used, the purpose of agent based modeling is to understand properties of complex social systems through the analysis of simulations. This method of doing science can be contrasted with the two standard methods of induction and deduction. Induction is the discovery of patterns in empirical data. For example, in the social sciences induction is widely used in the analysis of opinion surveys and macroeconomic data. Deduction, on the other hand, involves specifying a set of axioms and proving consequences that can be derived from those assumptions. The discovery of equilibrium results in game theory using rational-choice axioms is a good example of deduction. Agent-based modeling is a third way of doing science. Like deduction, it starts with a set of explicit assumptions. But unlike deduction, it does not prove theorems. Instead, an agent-based model generates simulated data that can be analyzed inductively. Unlike typical induction, however, the simulated data come from a rigorously specified set of rules rather than direct measurement of the real world. Whereas the purpose of induction is to find patterns in data and that of deduction is to find consequences of assumptions, the purpose of agent-based modeling is to aid intuition. Agent-based modeling is a way of doing thought experiments. Although the assumptions may be simple, the consequences may not be at all obvious. Numerous examples appear throughout this volume of locally interacting agents producing large-scale effects. The large-scale effects of locally interacting agents are called “emergent properties” of the system. Emergent properties are often surprising because it can be hard to anticipate the full consequences of even simple forms of interaction.axelrod