Computational organization theory: Autonomous agents and emergent behavior

Abstract

A computational organization theory is the articulation of an organization theory in the form of a computer program. We describe an example of this approach to studying organizational phenomena through the use of simulated autonomous intelligent agents, present a detailed description of such a model, and demonstrate the application through a series of experiments conducted with the model. The model, called Plural‐Soar, represents a partial instantiation of a cognitively motivated theory that views organizational behavior as emergent behavior from the collective interaction of intelligent agents over time, and that causal interpretations of certain organizational phenomena must be based on theoretically sufficient models of individual deliberation. We examine the individual and collective behavior of the agents under varying conditions of agent capabilities defined by their communication and memory properties. Thirty separate simulations with homogeneous agent groups were run varying agent type, group size, and number of items in the order list an agent acquires. The goal of the simulation experiment was to examine how fundamental properties of individual coordination (communication and memory) affected individual and group productivity and coordination efforts under different task properties (group size and order size). The specific results indicate that the length of the item list enhances performance for one to three agent groups, but with larger groups memory effects dominate. Communication capabilities led to an increase in idle time and undesirable collective behavior. The general conclusion is that there are subtle and complex interactions between agent capabilities and task properties that can restrict the generality of the results, and that computational modeling can provide insight into those interactions.