Over time organizations change and coordinate personnel in new ways. Such changes may be precipitated by actual or anticipated changes in personnel, the environment, technologies, legislation, or the top management team. This adaptation is constrained and not all forms of coordination are feasible. Since organizations are inherently computational entities insight is gained by examining the adaptation of organizations using intelligent artificial agents. Using ORGAHEAD, a multi-agent model of organizational behavior, a series of virtual experiments were run to examine issues of organizational adaptation. Results suggest the concurrent occurrence of experiential learning and structural learning generates within the organization the ability to learn meta-change strategies which can be either adaptive or maladaptive. Such meta-change strategies effectively lock organizations into divergent paths of behavior which produce heterogeneity of form across the population of organizations. Organizational performance and form depend on a complex of array of factors including environmental change, experiential and structural learning, and the emergence of institutionalized strategies.
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