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

2026-05-11

AI Summary

This work addresses the efficiency–fairness trade-off in multi-agent systems arising from information asymmetry and misaligned incentives. It proposes a learnable linear contract mechanism that models the repeated interaction between a sender with private information and a receiver who relies on that information for decision-making. The mechanism enables the sender to optimize its utility through strategic information pricing, while revealing how communication strategies are sensitive to incentive misalignment and environmental observability. Experimental results demonstrate that the sender can effectively learn optimal communication and pricing policies, substantially increasing its own payoff at the expense of significantly reduced receiver surplus. This quantifies the impact of information pricing on system fairness and highlights the inherent tension between efficiency and equity in strategic information exchange.

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.

Problem

• information asymmetry
• misaligned incentives
• contract design
• multi-agent systems
• fairness

Innovation

• information asymmetry
• misaligned incentives
• linear contracts
• multi-agent learning
• contract design