2018/11/16 by Alon Cohen, Cohen, Alon, Moran Koren +3 · 5 citations
Computer Science · Decision Sciences · Economics, Econometrics and Finance · Mathematics · #Action (physics) #Auction Theory and Applications #Computer Science and Game Theory (cs.GT) #Computer science #Computer security #Contract theory #Economic theories and models #Economics #FOS: Computer and information sciences #FOS: Economics and business #Finance #Law, Economics, and Judicial Systems #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Mathematical economics #Mathematical optimization #Mathematics #Microeconomics #Outcome (game theory) #Principal (computer security) #Principal–agent problem #Profit (economics) #Risk neutral #Theoretical Economics (econ.TH) #Wage #cs.GT #cs.LG #econ.TH #stat.ML
paper · pdf · doi:10.48550/arxiv.1811.06736
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2018/11/16 · arxiv created 2022/07/13 · arxiv updated 2022/07/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/08
In principal-agent models, a principal offers a contract to an agent to perform a certain task. The agent exerts a level of effort that maximizes her utility. The principal is oblivious to the agent's chosen level of effort, and conditions her wage only on possible outcomes. In this work, we consider a model in which the principal is unaware of the agent's utility and action space: she sequentially offers contracts to identical agents, and observes the resulting outcomes. We present an algorithm for learning the optimal contract under mild assumptions. We bound the number of samples needed for the principal to obtain a contract that is within \eps of her optimal net profit for every \eps>0. Our results are robust even when considering risk-averse agents. Furthermore, we show that when there are only two possible outcomes or the agent is risk-neutral, the algorithm's outcome approximates the optimal contract described in the classical theory.