Proceedings

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

Nanda Kishore Sreenivas, Kate Larson

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Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence. Main Track. Pages 357-364.

https://doi.org/10.24963/ijcai.2026/41

Keywords

  • Agent-based and Multi-agent Systems: Multi-agent learning
  • Agent-based and Multi-agent Systems: Coordination and cooperation
  • Agent-based and Multi-agent Systems: Agreement technologies: Negotiation and contract-based systems

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.

Nanda Kishore Sreenivas, and Kate Larson. 2026. Information and Contract Design for Repeated Interactions Between Agents with Misaligned Incentives. In Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-26. IJCAI, 357–364. https://doi.org/10.24963/ijcai.2026/41
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