Wang WPfeiffer T2025-05-132025-05-132024-01-01Wang W, Pfeiffer T. (2024). Decision Market Based Learning For Multi-agent Contextual Bandit Problems. Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS. (pp. 2549-2551). International Foundation for Autonomous Agents and Multiagent Systems.1548-8403https://mro.massey.ac.nz/handle/10179/72897Information is often stored in a distributed and proprietary form, and agents who own this information are often self-interested and require incentives to reveal it. Suitable mechanisms are required to elicit and aggregate such distributed information for decision-making. In this study, we use simulations to investigate the use of decision markets as mechanisms in a multi-agent learning system to aggregate distributed information for decision-making in a contextual bandit problem.© 2024 International Foundation for Autonomous Agents and Multiagent SystemsCC BY 4.0https://creativecommons.org/licenses/by/4.0/Multi-agent systemsPrediction marketsFederated learningconference1558-2914c-conference-paper-in-proceedings2549-2551