期刊
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
卷 114, 期 18, 页码 4715-4720出版社
NATL ACAD SCIENCES
DOI: 10.1073/pnas.1621239114
关键词
evolutionary game theory; reciprocity; repeated games
资金
- European Research Council [279307]
- Austrian Science Fund (FWF) [P23499-N23]
- FWF Nationale Forschungsnetzwerke [S11407-N23]
- Office of Naval Research [N00014-16-1-2914]
- John Templeton Foundation
- ISTFELLOW program
- European Research Consortium for Informatics and Mathematics Alain Bensoussan Fellowship Program
Humans routinely use conditionally cooperative strategies when interacting in repeated social dilemmas. They are more likely to cooperate if others cooperated before, and are ready to retaliate if others defected. To capture the emergence of reciprocity, most previous models consider subjects who can only choose from a restricted set of representative strategies, or who react to the outcome of the very last round only. As players memorize more rounds, the dimension of the strategy space increases exponentially. This increasing computational complexity renders simulations for individuals with higher cognitive abilities infeasible, especially if multiplayer interactions are taken into account. Here, we take an axiomatic approach instead. We propose several properties that a robust cooperative strategy for a repeated multiplayer dilemma should have. These properties naturally lead to a unique class of cooperative strategies, which contains the classical Win-Stay Lose-Shift rule as a special case. A comprehensive numerical analysis for the prisoner's dilemma and for the public goods game suggests that strategies of this class readily evolve across various memory-n spaces. Our results reveal that successful strategies depend not only on how cooperative others were in the past but also on the respective context of cooperation.
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