From T-Mazes to Labyrinths: Learning from Model-Based Feedback

Jerker. Denrell (), Christina. Fang () and Daniel A. Levinthal ()
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Jerker. Denrell: Graduate School of Business, Stanford University, Stanford, California 94305
Christina. Fang: Stern School of Business, New York University, New York, New York 10012, and National University of Singapore, Singapore
Daniel A. Levinthal: Department of Management and Economics, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104

Management Science, 2004, vol. 50, issue 10, 1366-1378

Abstract: Many organizational actions need not have any immediate or direct payoff consequence but set the stage for subsequent actions that bring the organization toward some actual payoff. Learning in such settings poses the challenge of credit assignment (Minsky 1961), that is, how to assign credit for the overall outcome of a sequence of actions to each of the antecedent actions. To explore the process of learning in such contexts, we create a formal model in which the actors develop a mental model of the value of stage-setting actions as a complex problem-solving task is repeated. Partial knowledge, either of particular states in the problem space or inefficient and circuitous routines through the space, is shown to be quite valuable. Because of the interdependence of intelligent action when a sequence of actions must be identified, however, organizational knowledge is relatively fragile. As a consequence, while turnover may stimulate search and have largely benign implications in less interdependent task settings, it is very destructive of the organization's near-term performance when the learning problem requires a complementarity among the actors' knowledge.

Keywords: organizational learning; credit assignment; organizational routines; task interdependency; reinforcement learning (search for similar items in EconPapers)
Date: 2004
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Citations: View citations in EconPapers (26)

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