Incentivized Learning in Principal-Agent Bandit Games

Real-world decision-making problems, however, often present challenges that are not addressed in this simple This work considers a repeated principal-agent optimization framework. These include the challenge of bandit game, where the principal can only scarcity when there are multiple decision-makers, issues interact with her environment through the agent. of misaligned objectives, and problems arising from The principal and the agent have misaligned information asymmetries and signaling. The economics objectives and the choice of action is only left to literature addresses these issues through the design of the agent. However, the principal can influence game-theoretic mechanisms, including auctions and the agent's decisions by offering incentives which contracts (see, e.g., Myerson, 1989; Laffont & Martimort, add up to his rewards. The principal aims to 2009), aiming to achieve favorable outcomes despite agents' iteratively learn an incentive policy to maximize self-interest and limited information set.