Introspection dynamics: a simple model of counterfactual learning in asymmetric games
Social behavior in human and animal populations can be studied as an evolutionary process. Individuals often make decisions between different strategies, and those strategies that yield a fitness advantage tend to spread. Traditionally, much work in evolutionary game theory considers symmetric games...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2022-01-01
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| Series: | New Journal of Physics |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/1367-2630/ac6f76 |
| _version_ | 1797748501125267456 |
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| author | M C Couto S Giaimo C Hilbe |
| author_facet | M C Couto S Giaimo C Hilbe |
| author_sort | M C Couto |
| collection | DOAJ |
| description | Social behavior in human and animal populations can be studied as an evolutionary process. Individuals often make decisions between different strategies, and those strategies that yield a fitness advantage tend to spread. Traditionally, much work in evolutionary game theory considers symmetric games: individuals are assumed to have access to the same set of strategies, and they experience the same payoff consequences. As a result, they can learn more profitable strategies by imitation. However, interactions are oftentimes asymmetric. In that case, imitation may be infeasible (because individuals differ in the strategies they are able to use), or it may be undesirable (because individuals differ in their incentives to use a strategy). Here, we consider an alternative learning process which applies to arbitrary asymmetric games, introspection dynamics . According to this dynamics, individuals regularly compare their present strategy to a randomly chosen alternative strategy. If the alternative strategy yields a payoff advantage, it is more likely adopted. In this work, we formalize introspection dynamics for pairwise games. We derive simple and explicit formulas for the abundance of each strategy over time and apply these results to several well-known social dilemmas. In particular, for the volunteer’s timing dilemma, we show that the player with the lowest cooperation cost learns to cooperate without delay. |
| first_indexed | 2024-03-12T16:05:42Z |
| format | Article |
| id | doaj.art-8c54270afe48460f92772ddb8c0c44e9 |
| institution | Directory Open Access Journal |
| issn | 1367-2630 |
| language | English |
| last_indexed | 2024-03-12T16:05:42Z |
| publishDate | 2022-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | New Journal of Physics |
| spelling | doaj.art-8c54270afe48460f92772ddb8c0c44e92023-08-09T14:22:46ZengIOP PublishingNew Journal of Physics1367-26302022-01-0124606301010.1088/1367-2630/ac6f76Introspection dynamics: a simple model of counterfactual learning in asymmetric gamesM C Couto0https://orcid.org/0000-0002-2868-9685S Giaimo1https://orcid.org/0000-0003-0421-3065C Hilbe2https://orcid.org/0000-0001-5116-955XMax Planck Research Group: Dynamics of Social Behavior, Max Planck Institute for Evolutionary Biology , 24306 Plön, GermanyDepartment of Evolutionary Theory, Max Planck Institute for Evolutionary Biology , 24306 Plön, GermanyMax Planck Research Group: Dynamics of Social Behavior, Max Planck Institute for Evolutionary Biology , 24306 Plön, GermanySocial behavior in human and animal populations can be studied as an evolutionary process. Individuals often make decisions between different strategies, and those strategies that yield a fitness advantage tend to spread. Traditionally, much work in evolutionary game theory considers symmetric games: individuals are assumed to have access to the same set of strategies, and they experience the same payoff consequences. As a result, they can learn more profitable strategies by imitation. However, interactions are oftentimes asymmetric. In that case, imitation may be infeasible (because individuals differ in the strategies they are able to use), or it may be undesirable (because individuals differ in their incentives to use a strategy). Here, we consider an alternative learning process which applies to arbitrary asymmetric games, introspection dynamics . According to this dynamics, individuals regularly compare their present strategy to a randomly chosen alternative strategy. If the alternative strategy yields a payoff advantage, it is more likely adopted. In this work, we formalize introspection dynamics for pairwise games. We derive simple and explicit formulas for the abundance of each strategy over time and apply these results to several well-known social dilemmas. In particular, for the volunteer’s timing dilemma, we show that the player with the lowest cooperation cost learns to cooperate without delay.https://doi.org/10.1088/1367-2630/ac6f76evolutionary game theorycounterfactual learningmyopic updatingasymmetric gamessocial dilemmasvolunteer’s dilemma |
| spellingShingle | M C Couto S Giaimo C Hilbe Introspection dynamics: a simple model of counterfactual learning in asymmetric games New Journal of Physics evolutionary game theory counterfactual learning myopic updating asymmetric games social dilemmas volunteer’s dilemma |
| title | Introspection dynamics: a simple model of counterfactual learning in asymmetric games |
| title_full | Introspection dynamics: a simple model of counterfactual learning in asymmetric games |
| title_fullStr | Introspection dynamics: a simple model of counterfactual learning in asymmetric games |
| title_full_unstemmed | Introspection dynamics: a simple model of counterfactual learning in asymmetric games |
| title_short | Introspection dynamics: a simple model of counterfactual learning in asymmetric games |
| title_sort | introspection dynamics a simple model of counterfactual learning in asymmetric games |
| topic | evolutionary game theory counterfactual learning myopic updating asymmetric games social dilemmas volunteer’s dilemma |
| url | https://doi.org/10.1088/1367-2630/ac6f76 |
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