Payoff equivalence and complete strategy spaces of direct reciprocity

Repeated games and stochastic games are important frameworks to study direct reciprocity. Individuals react strategically to their coplayers' previous behavior. While strategies in such games can be arbitrarily complex, explorations of evolutionary dynamics are often done in specific strategy spaces. Individuals may consider a fixed number of past rounds, or only some of the partner's previous actions. Such restrictions can make the interpretation of the results difficult. Strategies found to be superior within a restricted set may lose stability when more complex strategies are permitted. We discuss two notions of completeness that rule out this possibility. If a strategy space, S, is best-reply-complete, then any strategy in S is guaranteed to have a best reply in S. If a space, S, is payoff-complete, then any strategy playing against an opponent in S can be replaced by an equivalent strategy within S without affecting either player's payoff. Sufficient conditions for best-reply-completeness have been given in a seminal paper by Levínský et al. Here, we show that for strategies of bounded memory, the same conditions are sufficient for payoff-completeness. Furthermore, using those conditions, we illustrate how to construct many complete spaces for simple games. Taken together, our findings highlight the importance of complete strategy spaces, which are particularly useful when interpreting evolutionary simulations and determining best responses.

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Publication Details

Journal
Proceedings of the National Academy of Sciences
Published
2026-01-29
DOI
https://doi.org/10.1073/pnas.2518486123
Citations
3
Primary Topic
Evolutionary Game Theory and Cooperation
Type
article
Field-Weighted Citation Impact
166.72
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article

Payoff equivalence and complete strategy spaces of direct reciprocity

M NOWAK, Nikoleta E. Glynatsi, Philip LaPorte, Christian Hilbe
3 citations
Proceedings of the National Academy of Sciences
Evolutionary Game Theory and Cooperation
166.72
article

Payoff equivalence and complete strategy spaces of direct reciprocity

M NOWAK, Nikoleta E. Glynatsi, Philip LaPorte, Christian Hilbe
article en
3 citations

Abstract

Repeated games and stochastic games are important frameworks to study direct reciprocity. Individuals react strategically to their coplayers' previous behavior. While strategies in such games can be arbitrarily complex, explorations of evolutionary dynamics are often done in specific strategy spaces. Individuals may consider a fixed number of past rounds, or only some of the partner's previous actions. Such restrictions can make the interpretation of the results difficult. Strategies found to be superior within a restricted set may lose stability when more complex strategies are permitted. We discuss two notions of completeness that rule out this possibility. If a strategy space, S, is best-reply-complete, then any strategy in S is guaranteed to have a best reply in S. If a space, S, is payoff-complete, then any strategy playing against an opponent in S can be replaced by an equivalent strategy within S without affecting either player's payoff. Sufficient conditions for best-reply-completeness have been given in a seminal paper by Levínský et al. Here, we show that for strategies of bounded memory, the same conditions are sufficient for payoff-completeness. Furthermore, using those conditions, we illustrate how to construct many complete spaces for simple games. Taken together, our findings highlight the importance of complete strategy spaces, which are particularly useful when interpreting evolutionary simulations and determining best responses.

Proceedings of the National Academy of SciencesVol. 123(5)
Center for Discrete Mathematics and Theoretical Computer Science (US), Harvard University Press (US), University of California System (US), Max Planck Institute for Dynamics and Self-Organization (DE), Technology Dynamics (United States) (US), RIKEN Center for Computational Science (JP), Max Planck Institute for Evolutionary Biology (DE), Toronto Metropolitan University (CA)
Openalex Percentile: Top 0%
Evolutionary Game Theory and Cooperation
166.72
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