Deep reinforcement learning can promote sustainable human behaviour in a common-pool resource problem
Abstract A canonical social dilemma arises when resources are allocated to people, who can either reciprocate with interest or keep the proceeds. The right resource allocation mechanisms can encourage levels of reciprocation that sustain the commons. Here, in an iterated multiplayer trust game, we u...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-03-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-58043-7 |
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| _version_ | 1849389942966321152 |
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| author | Raphael Koster Miruna Pîslar Andrea Tacchetti Jan Balaguer Leqi Liu Romuald Elie Oliver P. Hauser Karl Tuyls Matt Botvinick Christopher Summerfield |
| author_facet | Raphael Koster Miruna Pîslar Andrea Tacchetti Jan Balaguer Leqi Liu Romuald Elie Oliver P. Hauser Karl Tuyls Matt Botvinick Christopher Summerfield |
| author_sort | Raphael Koster |
| collection | DOAJ |
| description | Abstract A canonical social dilemma arises when resources are allocated to people, who can either reciprocate with interest or keep the proceeds. The right resource allocation mechanisms can encourage levels of reciprocation that sustain the commons. Here, in an iterated multiplayer trust game, we use deep reinforcement learning (RL) to design a social planner that promotes sustainable contributions from human participants. We first trained neural networks to behave like human players, creating a stimulated economy that allows us to study the dynamics of receipt and reciprocation. We use RL to train a mechanism to maximise aggregate return to players. The RL mechanism discovers a redistributive policy that leads to a large but also more equal surplus. The mechanism outperforms baseline mechanisms by conditioning its generosity on available resources and temporarily sanctioning defectors. Examining the RL policy allows us to develop a similar but explainable mechanism that is more popular among players. |
| format | Article |
| id | doaj-art-d1b0a6dc406a496cb550eccf93247fc5 |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-d1b0a6dc406a496cb550eccf93247fc52025-08-20T03:41:49ZengNature PortfolioNature Communications2041-17232025-03-0116111310.1038/s41467-025-58043-7Deep reinforcement learning can promote sustainable human behaviour in a common-pool resource problemRaphael Koster0Miruna Pîslar1Andrea Tacchetti2Jan Balaguer3Leqi Liu4Romuald Elie5Oliver P. Hauser6Karl Tuyls7Matt Botvinick8Christopher Summerfield9Google DeepMindGoogle DeepMindGoogle DeepMindGoogle DeepMindGoogle DeepMindGoogle DeepMindUniversity of ExeterGoogle DeepMindGoogle DeepMindUniversity of OxfordAbstract A canonical social dilemma arises when resources are allocated to people, who can either reciprocate with interest or keep the proceeds. The right resource allocation mechanisms can encourage levels of reciprocation that sustain the commons. Here, in an iterated multiplayer trust game, we use deep reinforcement learning (RL) to design a social planner that promotes sustainable contributions from human participants. We first trained neural networks to behave like human players, creating a stimulated economy that allows us to study the dynamics of receipt and reciprocation. We use RL to train a mechanism to maximise aggregate return to players. The RL mechanism discovers a redistributive policy that leads to a large but also more equal surplus. The mechanism outperforms baseline mechanisms by conditioning its generosity on available resources and temporarily sanctioning defectors. Examining the RL policy allows us to develop a similar but explainable mechanism that is more popular among players.https://doi.org/10.1038/s41467-025-58043-7 |
| spellingShingle | Raphael Koster Miruna Pîslar Andrea Tacchetti Jan Balaguer Leqi Liu Romuald Elie Oliver P. Hauser Karl Tuyls Matt Botvinick Christopher Summerfield Deep reinforcement learning can promote sustainable human behaviour in a common-pool resource problem Nature Communications |
| title | Deep reinforcement learning can promote sustainable human behaviour in a common-pool resource problem |
| title_full | Deep reinforcement learning can promote sustainable human behaviour in a common-pool resource problem |
| title_fullStr | Deep reinforcement learning can promote sustainable human behaviour in a common-pool resource problem |
| title_full_unstemmed | Deep reinforcement learning can promote sustainable human behaviour in a common-pool resource problem |
| title_short | Deep reinforcement learning can promote sustainable human behaviour in a common-pool resource problem |
| title_sort | deep reinforcement learning can promote sustainable human behaviour in a common pool resource problem |
| url | https://doi.org/10.1038/s41467-025-58043-7 |
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