核心概念
This work explores online contract design from various perspectives, including heterogeneous and homogeneous agents, non-myopic behavior, and team production models.
要約
This content delves into the complexities of online contract design, analyzing different agent types and behaviors. It discusses learning algorithms for optimal contracts and connections to game theory and auction mechanisms.
The study covers scenarios with single agents, team production models, and strategic non-myopic agents. It highlights the challenges of designing contracts without full knowledge of agent types or behaviors.
The content also addresses the use of linear contracts, Lipschitz bandits reduction techniques, and regret minimization strategies in contracting scenarios.
Overall, it provides insights into the evolving landscape of contract theory in online settings.
統計
The regret can be bounded by Opř∆ią0 log T ∆i δT D log Kq.
The deviation cost at each round t is lower bounded by λδ2t.
The immediate deviation cost can be lower bounded by λδ2t.
The regret using Algorithm 2 can be bounded by Opř∆ią0 log T ∆i ` logpT Tγ{λq¨log K logp1{γq q.
There exists an algorithm that achieves Op?T log T ` logpT Tγ{λq logpT { log Tq logp1{γq q regret.