Organizational Learning and Social Computation

Abstract

In Chapter XVIII we outlined the characteristics of a computational approach to support organizational analysis. Agent-based modeling, one of the several methodological tools presented in Chapter XVIII, is particularly suited for the modeling of learning processes in complex networks. In this appendix we want to provide the reader with an example of how it is possible to construct agent-based systems in order to simulate the collective behavior of social aggregates. We present a mathematical model aimed to represent and simulate adaptive organizational learning processes. With adaptive organizational learning processes we mean a learning process taking place in a social network in which individuals, by means of social interaction and subjective interpretative processes, contribute to the construction and the accumulation of shared experience. The proposed model implements a multiagent system aimed to represent a social network of interacting heterogeneous ‘virtual people’ operating in a virtual environment, here modeled as a network of resources. Learning for an agent means passing from an initial state to a target one through the identification of optimal paths within the environment by exploiting personal characteristics as well as interaction with other agents and the environment; such interaction allows agents to exchange information, to construct a collective memory on the basis of past individual experiences and to have access to resources.