Decision Market Based Learning For Multi-agent Contextual Bandit Problems

Abstract

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 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.