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Principal-Agent Bandit Games: Incentivized Learning Unveiled

Core Concepts
Principal-Agent Bandit Games introduce Incentivized Learning to optimize utility.
The article introduces a framework for repeated principal-agent bandit games. Misaligned objectives between principal and agent are addressed through incentives. The principal aims to maximize utility by learning optimal incentive policies. Algorithms for regret minimization in multi-armed and contextual settings are presented. Theoretical guarantees are supported by numerical experiments. The work bridges mechanism design and learning aspects in principal-agent models. Contextual bandit setting broadens applicability in various domains. Lower bounds for regret in bandit settings are discussed.
"Nearly optimal (with respect to a horizon T) learning algorithms for the principal’s regret in both multi-armed and linear contextual settings." "The overall algorithm achieves both nearly optimal distribution-free and instance-dependent regret bounds." "Contextual IPA achieves a O(d √ T log(T)) regret bound."
"The principal aims to iteratively learn an incentive policy to maximize her own total utility." "Our work focuses on the blend of mechanism design and learning." "The overall algorithm achieves nearly optimal regret bounds."

Key Insights Distilled From

by Antoine Sche... at 03-07-2024
Incentivized Learning in Principal-Agent Bandit Games

Deeper Inquiries

질문 1

프레임워크를 어떻게 확장하여 반복 상호 작용에서 전략적 행동을 통합할 수 있을까요? Answer 1 here

질문 2

제안된 알고리즘의 효과에 에이전트 측의 불확실성이 미치는 영향은 무엇인가요? Answer 2 here

질문 3

주요-에이전트 밴딧 게임의 정보 임대 개념을 어떻게 다룰 수 있을까요? Answer 3 here