Quantifying the Self-Interest Level of Markov Social Dilemmas
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913843492421632 |
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| author | Willis, Richard Du, Yali Leibo, Joel Z Luck, Michael |
| author_facet | Willis, Richard Du, Yali Leibo, Joel Z Luck, Michael |
| contents | This paper introduces a novel method for estimating the self-interest level of Markov social dilemmas. We extend the concept of self-interest level from normal-form games to Markov games, providing a quantitative measure of the minimum reward exchange required to align individual and collective interests. We demonstrate our method on three environments from the Melting Pot suite, representing either common-pool resources or public goods. Our results illustrate how reward exchange can enable agents to transition from selfish to collective equilibria in a Markov social dilemma. This work contributes to multi-agent reinforcement learning by providing a practical tool for analysing complex, multistep social dilemmas. Our findings offer insights into how reward structures can promote or hinder cooperation, with potential applications in areas such as mechanism design. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_16138 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quantifying the Self-Interest Level of Markov Social Dilemmas Willis, Richard Du, Yali Leibo, Joel Z Luck, Michael Computer Science and Game Theory Multiagent Systems This paper introduces a novel method for estimating the self-interest level of Markov social dilemmas. We extend the concept of self-interest level from normal-form games to Markov games, providing a quantitative measure of the minimum reward exchange required to align individual and collective interests. We demonstrate our method on three environments from the Melting Pot suite, representing either common-pool resources or public goods. Our results illustrate how reward exchange can enable agents to transition from selfish to collective equilibria in a Markov social dilemma. This work contributes to multi-agent reinforcement learning by providing a practical tool for analysing complex, multistep social dilemmas. Our findings offer insights into how reward structures can promote or hinder cooperation, with potential applications in areas such as mechanism design. |
| title | Quantifying the Self-Interest Level of Markov Social Dilemmas |
| topic | Computer Science and Game Theory Multiagent Systems |
| url | https://arxiv.org/abs/2501.16138 |