Researchers have developed new methods for principal-agent games in a multi-armed bandit framework, addressing scenarios where the principal cannot observe the agent's reward. The first paper focuses on a perfect-knowledge agent, constructing an estimator for the agent's rewards and a policy with a low regret. The second paper extends this to a setting where the agent also learns its rewards, introducing a parallel learning algorithm for the principal and proving the consistency of the estimator and a regret bound for the principal. Both papers demonstrate the applicability of their frameworks through simulations in areas like personalized healthcare and green energy contracts. AI
IMPACT These theoretical advancements could lead to more effective incentive designs in complex real-world applications where rewards are not directly observable.
RANK_REASON The cluster contains two academic papers published on arXiv detailing novel theoretical frameworks and algorithms for principal-agent problems in machine learning.
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