TY - GEN

T1 - Learning Approximately Optimal Contracts

AU - Cohen, Alon

AU - Deligkas, Argyrios

AU - Koren, Moran

N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

PY - 2022

Y1 - 2022

N2 - In principal-agent models, a principal offers a contract to an agent to preform 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 obtain a contract that is within ϵ of her optimal net profit for every ϵ> 0. Our results are robust even when considering risk averse agents. Furthermore, we show that when there only two possible outcomes, or the agent is risk neutral, the algorithm’s outcome approximates the optimal contract described in the classical theory.

AB - In principal-agent models, a principal offers a contract to an agent to preform 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 obtain a contract that is within ϵ of her optimal net profit for every ϵ> 0. Our results are robust even when considering risk averse agents. Furthermore, we show that when there only two possible outcomes, or the agent is risk neutral, the algorithm’s outcome approximates the optimal contract described in the classical theory.

UR - http://www.scopus.com/inward/record.url?scp=85138815128&partnerID=8YFLogxK

U2 - 10.1007/978-3-031-15714-1_19

DO - 10.1007/978-3-031-15714-1_19

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AN - SCOPUS:85138815128

SN - 9783031157134

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 331

EP - 346

BT - Algorithmic Game Theory - 15th International Symposium, SAGT 2022, Proceedings

A2 - Kanellopoulos, Panagiotis

A2 - Kyropoulou, Maria

A2 - Voudouris, Alexandros

PB - Springer Science and Business Media Deutschland GmbH

Y2 - 12 September 2022 through 15 September 2022

ER -