Incentive design for rational agents

Conference item

We introduce Incentive Design: a new class of problems for equilibrium verification in multi-agent systems. In our model, agents attempt to maximize their utility functions, which are expressed as formulae in LTL[F], a quantitative extension of Linear Temporal Logic with functions computable in polynomial time. We assume agents are rational, in the sense that they adopt strategies consistent with game theoretic solution concepts such as Nash equilibrium. For each solution concept we consider, we analyze the problems of verifying whether an incentive scheme achieves a societal objective and finding one that does so, whether it be social welfare or any other aggregate measure of collective well-being. We study both static and dynamic incentive schemes, showing that the latter are more powerful than the former. Finally, we solve the incentive verification and synthesis problems for all the solution concepts we consider, and analyze their complexity.

Authors: Hyland, D; Mittelmann, M; Murano, A; Perelli, G; Wooldridge, M

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: International Joint Conferences on Artificial Intelligence
• Host title: Proceedings of the 21st International Conference on Principles of Knowledge Representation and Reasoning
• Pages: 464-474
• Publication date: 2024-10-25
• Acceptance date: 2024-08-21
• Event title: 21st International Conference on Principles of Knowledge Representation and Reasoning
• Event location: Hanoi, Vietnam
• Event start date: 2024-11-02
• Event end date: 2024-11-08
• DOI: 10.24963/kr.2024/44
• EISSN: 2334-1033
• ISSN: 2334-1025
• ISBN: 978-1-956792-05-8
• Language: English