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.