Learning from Failure

Posted: 13 Aug 2008

See all articles by Andreas Blume

Andreas Blume

University of Pittsburgh - Department of Economics

April Franco

University of Toronto - Rotman School of Management; University of Toronto at Scarborough - Division of Management

Date Written: February 2002

Abstract

We study decentralized learning in organizations. Decentralization is captured through a symmetry constraint on agents' strategies. Among such attainable strategies, we solve for optimal and equilibrium strategies. We model the organization as a repeated game with imperfectly observable actions. A fixed but unknown subset of action profiles are successes and all other action profiles are failures. The game is played until either there is a success or the time horizon is reached. For any time horizon, including infinity, we demonstrate existence of optimal attainable strategies and show that they are Nash equilibria. For some time horizons, we can solve explicitly for the optimal attainable strategies and show uniqueness. The solution connects the learning behavior of agents to the fundamentals that characterize the organization: Agents in the organization respond more slowly to failure as the future becomes more important, the size of the organization increases and the probability of success decreases.

Keywords: Game Theory, Decentralized Learning, Organizations

JEL Classification: C7, D21

Suggested Citation

Blume, Andreas and Franco, April, Learning from Failure (February 2002). Available at SSRN: https://ssrn.com/abstract=1221666

Andreas Blume (Contact Author)

University of Pittsburgh - Department of Economics ( email )

4901 Wesley Posvar Hall
230 South Bouquet Street
Pittsburgh, PA 15260
United States

April Franco

University of Toronto - Rotman School of Management ( email )

105 St. George Street
Toronto, Ontario M5S 3E6 M5S1S4
Canada

University of Toronto at Scarborough - Division of Management ( email )

1265 Military Trial
Scarborough, Ontario M1C 1A4
Canada

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