TY - JOUR

T1 - Learning approximately optimal contracts

AU - Cohen, Alon

AU - Deligkas, Argyrios

AU - Koren, Moran

N1 - Publisher Copyright:
© 2023 Elsevier B.V.

PY - 2023/11/20

Y1 - 2023/11/20

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.

KW - Bandits

KW - Contracts

KW - Game theory

KW - Learning

UR - http://www.scopus.com/inward/record.url?scp=85172922446&partnerID=8YFLogxK

U2 - 10.1016/j.tcs.2023.114219

DO - 10.1016/j.tcs.2023.114219

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AN - SCOPUS:85172922446

SN - 0304-3975

VL - 980

JO - Theoretical Computer Science

JF - Theoretical Computer Science

M1 - 114219

ER -