Information and Contract Design for Repeated Interactions between Agents with Misaligned Incentives

TL;DR

This paper analyzes how information asymmetries and incentives affect communication and contract design between agents with conflicting interests, proposing mechanisms for optimal information exchange and payment structures.

Contribution

It introduces a model of linear contracts for information exchange in multi-agent systems with misaligned incentives, highlighting strategic communication and fairness issues.

Findings

Sender learns optimal communication strategies based on incentive conflicts.

Linear contracts can improve Sender's rewards but may reduce fairness.

Communication strategies are sensitive to reward conflicts and available environmental information.

Abstract

We study the consequences of information asymmetries and misaligned incentives in settings with multiple independent agents. We model an interaction between a Sender, who holds vital private information but cannot act, and a Receiver, who must make decisions but is dependent on the Sender's information. We find that the Sender learns an optimal communication strategy that the Receiver reliably acts on. Importantly, this strategy is highly sensitive to the degree of conflict in the agents' rewards and the amount of environmental information the Receiver can already observe. We introduce a mechanism allowing the agents to form linear contracts, where a price is established for the information. We demonstrate that the Sender learns to use these payment structures to improve its rewards, though this comes at a cost of "fairness" between agents as the Sender is able to extract much of the Receiver's surplus. This raises questions about fairness, contract design, and learning in the context of multi-agent systems.