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Learning to Mitigate Externalities: the Coase Theorem with Hindsight Rationality

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arxiv 2406.19824 v3 pith:LR4B5EAS submitted 2024-06-28 cs.GT stat.ML

classification cs.GTstat.ML
keywords coaseplayerswelfareexternalitysocialtheorembargainingknowledge
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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.

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Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Learning to Incentivize in Repeated Principal-Agent Problems with Adversarial Agent Arrivals

    cs.GT 2025-05 reject novelty 7.0 of 10

    New adversarial-arrival principal-agent model with regret upper bounds for greedy and smooth agents, but the claimed matching lower bound for the smooth setting is invalid because the constructed instance is not L-Lipschitz.

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