Decision Market Based Learning for Multi-agent Contextual Bandit Problems

Information 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 decisionmaking. 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.