Inferring to cooperate: Evolutionary games with Bayesian inferential strategies
Strategies for sustaining cooperation and preventing exploitation by selfish agents in repeated games have mostly been restricted to Markovian strategies where the response of an agent depends on the actions in the previous round. Such strategies are characterized by lack of learning. However, learn...
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IOP Publishing
2024-01-01
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| Series: | New Journal of Physics |
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| Online Access: | https://doi.org/10.1088/1367-2630/ad4e5e |
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| author | Arunava Patra Supratim Sengupta Ayan Paul Sagar Chakraborty |
| author_facet | Arunava Patra Supratim Sengupta Ayan Paul Sagar Chakraborty |
| author_sort | Arunava Patra |
| collection | DOAJ |
| description | Strategies for sustaining cooperation and preventing exploitation by selfish agents in repeated games have mostly been restricted to Markovian strategies where the response of an agent depends on the actions in the previous round. Such strategies are characterized by lack of learning. However, learning from accumulated evidence over time and using the evidence to dynamically update our response is a key feature of living organisms. Bayesian inference provides a framework for such evidence-based learning mechanisms. It is therefore imperative to understand how strategies based on Bayesian learning fare in repeated games with Markovian strategies. Here, we consider a scenario where the Bayesian player uses the accumulated evidence of the opponent’s actions over several rounds to continuously update her belief about the reactive opponent’s strategy. The Bayesian player can then act on her inferred belief in different ways. By studying repeated Prisoner’s dilemma games with such Bayesian inferential strategies, both in infinite and finite populations, we identify the conditions under which such strategies can be evolutionarily stable. We find that a Bayesian strategy that is less altruistic than the inferred belief about the opponent’s strategy can outperform a larger set of reactive strategies, whereas one that is more generous than the inferred belief is more successful when the benefit-to-cost ratio of mutual cooperation is high. Our analysis reveals how learning the opponent’s strategy through Bayesian inference, as opposed to utility maximization, can be beneficial in the long run, in preventing exploitation and eventual invasion by reactive strategies. |
| format | Article |
| id | doaj-art-c243f7b7e19b488393e00c9d05a5466a |
| institution | DOAJ |
| issn | 1367-2630 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | New Journal of Physics |
| spelling | doaj-art-c243f7b7e19b488393e00c9d05a5466a2025-08-20T03:17:58ZengIOP PublishingNew Journal of Physics1367-26302024-01-0126606300310.1088/1367-2630/ad4e5eInferring to cooperate: Evolutionary games with Bayesian inferential strategiesArunava Patra0Supratim Sengupta1https://orcid.org/0000-0001-5294-0561Ayan Paul2Sagar Chakraborty3https://orcid.org/0000-0001-7568-0598Department of Physics, Indian Institute of Technology Kanpur , Kanpur, Uttar Pradesh 208016, IndiaDepartment of Physical Sciences, Indian Institute of Science Education and Research Kolkata , Mohanpur Campus, Mohanpur, West Bengal 741246, IndiaDepartment of Electrical and Computer Engineering Northeastern University , Boston, MA 0211, United States of AmericaDepartment of Physics, Indian Institute of Technology Kanpur , Kanpur, Uttar Pradesh 208016, IndiaStrategies for sustaining cooperation and preventing exploitation by selfish agents in repeated games have mostly been restricted to Markovian strategies where the response of an agent depends on the actions in the previous round. Such strategies are characterized by lack of learning. However, learning from accumulated evidence over time and using the evidence to dynamically update our response is a key feature of living organisms. Bayesian inference provides a framework for such evidence-based learning mechanisms. It is therefore imperative to understand how strategies based on Bayesian learning fare in repeated games with Markovian strategies. Here, we consider a scenario where the Bayesian player uses the accumulated evidence of the opponent’s actions over several rounds to continuously update her belief about the reactive opponent’s strategy. The Bayesian player can then act on her inferred belief in different ways. By studying repeated Prisoner’s dilemma games with such Bayesian inferential strategies, both in infinite and finite populations, we identify the conditions under which such strategies can be evolutionarily stable. We find that a Bayesian strategy that is less altruistic than the inferred belief about the opponent’s strategy can outperform a larger set of reactive strategies, whereas one that is more generous than the inferred belief is more successful when the benefit-to-cost ratio of mutual cooperation is high. Our analysis reveals how learning the opponent’s strategy through Bayesian inference, as opposed to utility maximization, can be beneficial in the long run, in preventing exploitation and eventual invasion by reactive strategies.https://doi.org/10.1088/1367-2630/ad4e5eReactive StrategyBayes’ TheoremRepeated GamesEvolutionarily Stable StrategyEvolutionary Game Theory |
| spellingShingle | Arunava Patra Supratim Sengupta Ayan Paul Sagar Chakraborty Inferring to cooperate: Evolutionary games with Bayesian inferential strategies New Journal of Physics Reactive Strategy Bayes’ Theorem Repeated Games Evolutionarily Stable Strategy Evolutionary Game Theory |
| title | Inferring to cooperate: Evolutionary games with Bayesian inferential strategies |
| title_full | Inferring to cooperate: Evolutionary games with Bayesian inferential strategies |
| title_fullStr | Inferring to cooperate: Evolutionary games with Bayesian inferential strategies |
| title_full_unstemmed | Inferring to cooperate: Evolutionary games with Bayesian inferential strategies |
| title_short | Inferring to cooperate: Evolutionary games with Bayesian inferential strategies |
| title_sort | inferring to cooperate evolutionary games with bayesian inferential strategies |
| topic | Reactive Strategy Bayes’ Theorem Repeated Games Evolutionarily Stable Strategy Evolutionary Game Theory |
| url | https://doi.org/10.1088/1367-2630/ad4e5e |
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