Algorithmic Contract Design with Reinforcement Learning Agents
Abstract
Designing incentive mechanisms for multi-agent systems in stochastic and dynamic environments is a critical challenge, as system outcomes emerge from the complex interplay of agent learning and environmental uncertainty. Existing principal–multi-agent contract design methods often assume static settings or ignore learning dynamics, limiting their applicability in multi-agent reinforcement learning (MARL). Furthermore, the contract design space is highly constrained by feasibility requirements, such as individual rationality and incentive compatibility, making it difficult to explore.
Fields of Study & Subjects
Law
Economics
and Judicial Systems
Blockchain Technology Applications and Security
Insurance and Financial Risk Management
Publication details
- Journal / source
- Proceedings of the 25th International Conference on Autonomous Agents and Multiagent Systems
- Publisher
- International Foundation for Autonomous Agents and Multiagent Systems
- Publication date
- 24 May 2026
- Language
- EN