Learning to Mitigate Externalities: the Coase Theorem with Hindsight Rationality

TL;DR

This paper extends the Coase theorem to settings with uncertainty by designing a learning-based bargaining policy in a two-player bandit model, enabling social welfare maximization without perfect knowledge.

Contribution

It introduces a novel framework that removes the assumption of perfect knowledge in externality models, demonstrating how players can learn to bargain effectively under uncertainty.

Findings

Social welfare deteriorates without property rights.

A learning policy enables players to maximize welfare.

The approach recovers the Coase theorem under uncertainty.

Abstract

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.