Why Agents? On the Varied Motivations for Agent Computing in the Social Sciences

Robert Axtell. 2000. Why Agents? On the varied motivations for agent computing in the social sciences.

This paper (mainly focused in social sciences and political economy in particular) argues the existence of three distinct uses of agent-based computational models:

• When numerical realizations are relevant, agents can perform a variant of classical simulation
• When a model is incompletely solved mathematically, then agent-based model can be a useful tool of analysis, a complement to mathematics. It is generally possible to build agent-based computational models in order to gain insight into the functioning of the model.
• There are cases in which mathematical models are either apparently intracable or provably insoluble. Agent-based computing is perhaps the only technique available for systematic analysis, a substitute for formal mathematical analysis.

Strengths and Weaknesses
A very common motivation for ABM is dissatisfaction with rational agents. In most social processes either physical space or social network matter. These are difficult to account for mathematically except in hightly stylize ways. However, in ABM, it is usually quite easy to have the agent interactions mediated by space or network or both. Spatial networks are quite naturally represented in agent-based computatinal models. A physical location can be part of an agen’ts internal states. Likewise, its position in a social network can be easily represented internally. In ABM the only way to prove a sufficiency theorem is to go through multiple runs, systematically varying initial conditions or parameters in order to assess the robustness of results.