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The Evolution of Trust and Cooperation between Strangers: A Computational Model

Michael W. Macy and John Skvoretz
American Sociological Review
Vol. 63, No. 5 (Oct., 1998), pp. 638-660
Stable URL: http://www.jstor.org/stable/2657332
Page Count: 23
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Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
The Evolution of Trust and Cooperation between Strangers: A Computational Model
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Abstract

Social and economic exchanges often occur between strangers who cannot rely on past behavior or the prospect of future interactions to establish mutual trust. Game theorists formalize this problem as a "one-shot prisoner's dilemma" and predict mutual noncooperation. Recent studies, however, challenge this conclusion. If the game provides an option to exit (or to refuse to play), strategies based on "projection" (of a player's intentions) and "detection" (of the intentions of a stranger) can confer a "cooperator's advantage." Yet previous research has not found a way for these strategies to evolve from a random start or to recover from invasion by aggressive strategies that feign trustworthiness. We use computer simulation to show how trust and cooperation between strangers can evolve without formal or informal social controls. The outcome decisively depends, however, on two structural conditions: the payoff for refusing to play, and the embeddedness of interaction. Effective norms for trusting strangers emerge locally, in exchanges between neighbors, and then diffuse through "weak ties" to outsiders.

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