ArticleScientific reports2016
Comparing reactive and memory-one strategies of direct reciprocity.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- The dynamics of cooperation in asymmetric public goods games.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- The collaborative evolution of trust, information flow, and social cooperation: a study on network stability based on dynamic game models.Scientific reports · 2025Article
- Conditional cooperation with longer memory.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Evolution of cooperation in multichannel games on multiplex networks.PLoS computational biology · 2024Article
- Evolution of reciprocity with limited payoff memory.Proceedings. Biological sciences · 2024Article
- Efficiency and resilience of cooperation in asymmetric social dilemmas.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Social dilemma in foraging behavior and evolution of cooperation by learning.Scientific reports · 2023Article
- A geometric process of evolutionary game dynamics.Journal of the Royal Society, Interface · 2023Article
- Grouping promotes both partnership and rivalry with long memory in direct reciprocity.PLoS computational biology · 2023Article
- Mutation enhances cooperation in direct reciprocity.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Evolution of direct reciprocity in group-structured populations.Scientific reports · 2022Article
- Direct reciprocity between individuals that use different strategy spaces.PLoS computational biology · 2022Article
- Payoff landscapes and the robustness of selfish optimization in iterated games.Journal of mathematical biology · 2022Article
- Inferring strategies from observations in long iterated Prisoner's dilemma experiments.Scientific reports · 2022Article
- Misperception influence on zero-determinant strategies in iterated Prisoner's Dilemma.Scientific reports · 2022Article
- Win-Stay-Lose-Shift as a self-confirming equilibrium in the iterated Prisoner's Dilemma.Proceedings. Biological sciences · 2021Article
- Friendly-rivalry solution to the iterated n-person public-goods game.PLoS computational biology · 2021Article
- Signalling boosts the evolution of cooperation in repeated group interactions.Journal of the Royal Society, Interface · 2020Article
- Evolving cooperation in multichannel games.Nature communications · 2020Article
- Social dilemmas among unequals.Nature · 2019Article
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Authors and funding
4 authors.
Funding
Abstract
Direct reciprocity is a mechanism for the evolution of cooperation based on repeated interactions. When individuals meet repeatedly, they can use conditional strategies to enforce cooperative outcomes that would not be feasible in one-shot social dilemmas. Direct reciprocity requires that individuals keep track of their past interactions and find the right response. However, there are natural bounds on strategic complexity: Humans find it difficult to remember past interactions accurately, especially over long timespans. Given these limitations, it is natural to ask how complex strategies need to be for cooperation to evolve. Here, we study stochastic evolutionary game dynamics in finite populations to systematically compare the evolutionary performance of reactive strategies, which only respond to the co-player's previous move, and memory-one strategies, which take into account the own and the co-player's previous move. In both cases, we compare deterministic strategy and stochastic strategy spaces. For reactive strategies and small costs, we find that stochasticity benefits cooperation, because it allows for generous-tit-for-tat. For memory one strategies and small costs, we find that stochasticity does not increase the propensity for cooperation, because the deterministic rule of win-stay, lose-shift works best. For memory one strategies and large costs, however, stochasticity can augment cooperation.
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