Information and Contract Design for Repeated Interactions between Agents with Misaligned Incentives
For researchers in multi-agent systems and mechanism design, this work provides insights into information asymmetry and contract design, though it is an incremental extension of known concepts.
This paper studies how a Sender with private information can optimally communicate with a Receiver whose incentives are misaligned, finding that the Sender's communication strategy is sensitive to reward conflict and environmental information. Introducing linear contracts, the Sender improves its rewards at the cost of fairness by extracting the Receiver's surplus.
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.