Contextual Search in Principal-Agent Games: The Curse of Degeneracy
Yiding Feng, Mengfan Ma, Bo Peng, Zongqi Wan

TL;DR
This paper studies the challenge of learning optimal contracts in principal-agent games with contextual information, revealing a surprising exponential increase in complexity due to a structural phenomenon called contextual action degeneracy.
Contribution
It introduces a generalized model of contextual search in principal-agent games, establishes tight bounds on regret, and uncovers the impact of contextual action degeneracy on learning difficulty.
Findings
Exponential regret bounds are established for the principal's learning process.
A lower bound demonstrates double-exponential hardness in certain settings.
Contextual action degeneracy significantly limits the principal's ability to learn.
Abstract
In this work, we introduce and study contextual search in general principal-agent games, where a principal repeatedly interacts with agents by offering contracts based on contextual information and historical feedback, without knowing the agents' true costs or rewards. Our model generalizes classical contextual pricing by accommodating richer agent action spaces. Over rounds with -dimensional contexts, we establish an asymptotically tight exponential bound in terms of the pessimistic Stackelberg regret, benchmarked against the best utility for the principal that is consistent with the observed feedback. We also establish a lower bound of on the classic Stackelberg regret for principal-agent games, demonstrating a surprising double-exponential hardness separation from the contextual pricing problem (a.k.a, the…
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Taxonomy
TopicsAdvanced Bandit Algorithms Research · Game Theory and Applications · Stochastic Gradient Optimization Techniques
