Institute for Software Research International
School of Computer Science, Carnegie Mellon University


OrgAhead: A Computational Model of
Organizational Learning and Decision Making
(Version 2.1.5)

Jun-Sung Lee, Kathleen M. Carley

December 2004

Center for Computational Analysis of Social and Organizational Systems
CASOS Technical Report

Keywords: Simulated annealing, organizational learning, adaptation, dynamic, decision-making, organizational model, comptuational organization theory

OrgAhead is a computational model of organizational learning and decision-making. The simulated organization consists of agents whose communication structure resembles hierarchies and whose primary goals are to learn the correct decision or answer to one or more tasks, or objective functions (e.g. typically the majority classification task); we refer to thse task functions as the task environment. The organization also seeks to adapt to an optimal structure under the specified, and possibly changing, task environment, by admitting changes in the form of turnover and reassignment of personnel and tasks. OrgAhead can be used to test various aspects of real life organizations, such as complexity in the task environment and constraintss on structure and adaptability, under the intellective paradigm of simulation models. An intellective model contains analogous entities, constructs, and complexities of the modeled organizations rather than mimicking each specific behavior.

52 pages

*Department of Social and Decision Sciences, Carnegie Mellon University

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