Learning to Mitigate Externalities: the Coase Theorem with Hindsight Rationality

Antoine Scheid, Aymeric Capitaine, Etienne Boursier, Eric Moulines, Michael Jordan, Alain Oliviero Durmus

Keywords: Online Learning, Bandits, Algorithmic Game Theory, Externality, Policy Design, Two-Players Game

Abstract: In Economics, the concept of externality refers to any indirect effect resulting from an interaction between players and affecting a third party without compensation. 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 adress this issue, we consider a two-players bandit game setting where the actions of one of the player affect the other one. Building upon this setup, we extend the Coase theorem [Coase, 2013], which suggests that the optimal approach for maximizing the social welfare in the presence of externality is to establish property rights, i.e., enabling transfers and bargaining between the players. Nonetheless, this fundamental result relies on the assumption that bargainers possess perfect knowledge of the underlying game. We first demonstrate that in the absence of property rights in the considered online scenario, the social welfare breaks down. We then provide a policy for the players, which allows them to learn a bargaining strategy which maximizes the total welfare, recovering the Coase theorem under uncertainty.

Primary Area: Algorithmic game theory

Submission Number: 16261