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SIMULATING STRUCTURAL CHANGE IN ADAPTIVE ORGANIZATIONS

This paper presents an agent-based model of an organization. The model is made of a social network—composed of the different organization workers—and a knowledge network. Workers are assigned tasks, for which they have to use information in the knowledge network. We have modeled the quality of the information by assigning each information item a probability of being wrong. Agents can interact with other agents, who can recommend to them new information items in the knowledge network for the task to be performed. Workers are assigned different information-seeking behavior (passive, active, and learning), governing the way in which they interact with each other. Moreover, indirect interaction is also possible, as a publicly accessible knowledge base contains each agent's preferred information items. The model was implemented in SDML, and its simulation shows that agents quickly learn to discern the better information items for the given task. However, group formation (agents' collaborating by exchanging information) takes longer to stabilize. Additionally, when the quality of items is changed, agents can quickly select the better new knowledge items, and organization performance improves again to a maximum that is only randomly disturbed.