Learning to Mitigate Externalities: the Coase Theorem with Hindsight Rationality
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866917903813574656 |
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| author | Scheid, Antoine Capitaine, Aymeric Boursier, Etienne Moulines, Eric Jordan, Michael I Durmus, Alain |
| author_facet | Scheid, Antoine Capitaine, Aymeric Boursier, Etienne Moulines, Eric Jordan, Michael I Durmus, Alain |
| contents | In economic theory, the concept of externality refers to any indirect effect resulting from an interaction between players that affects the social welfare. Most of the models within which externality has been studied assume that agents have perfect knowledge of their environment and preferences. This is a major hindrance to the practical implementation of many proposed solutions. To address this issue, we consider a two-player bandit setting where the actions of one of the players affect the other player and we extend the Coase theorem [Coase, 1960]. This result shows that the optimal approach for maximizing the social welfare in the presence of externality is to establish property rights, i.e., enable transfers and bargaining between the players. Our work removes the classical assumption that bargainers possess perfect knowledge of the underlying game. We first demonstrate that in the absence of property rights, the social welfare breaks down. We then design a policy for the players which allows them to learn a bargaining strategy which maximizes the total welfare, recovering the Coase theorem under uncertainty. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2406_19824 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learning to Mitigate Externalities: the Coase Theorem with Hindsight Rationality Scheid, Antoine Capitaine, Aymeric Boursier, Etienne Moulines, Eric Jordan, Michael I Durmus, Alain Computer Science and Game Theory Machine Learning In economic theory, the concept of externality refers to any indirect effect resulting from an interaction between players that affects the social welfare. Most of the models within which externality has been studied assume that agents have perfect knowledge of their environment and preferences. This is a major hindrance to the practical implementation of many proposed solutions. To address this issue, we consider a two-player bandit setting where the actions of one of the players affect the other player and we extend the Coase theorem [Coase, 1960]. This result shows that the optimal approach for maximizing the social welfare in the presence of externality is to establish property rights, i.e., enable transfers and bargaining between the players. Our work removes the classical assumption that bargainers possess perfect knowledge of the underlying game. We first demonstrate that in the absence of property rights, the social welfare breaks down. We then design a policy for the players which allows them to learn a bargaining strategy which maximizes the total welfare, recovering the Coase theorem under uncertainty. |
| title | Learning to Mitigate Externalities: the Coase Theorem with Hindsight Rationality |
| topic | Computer Science and Game Theory Machine Learning |
| url | https://arxiv.org/abs/2406.19824 |