With the dramatic progress of artificial intelligence algorithms in recent times, it is hoped that algorithms will soon supplant human decision-makers in various fields, such as contract design. We analyze the possible consequences by experimentally studying the behavior of algorithms powered by Artificial Intelligence (Multi-agent Q-learning) in a workhorse \emph{dual contract} model for dual-principal-agent problems. We find that the AI algorithms autonomously learn to design incentive-compatible contracts without external guidance or communication among themselves. We emphasize that the principal, powered by distinct AI algorithms, can play mixed-sum behavior such as collusion and competition. We find that the more intelligent principals tend to become cooperative, and the less intelligent principals are endogenizing myopia and tend to become competitive. Under the optimal contract, the lower contract incentive to the agent is sustained by collusive strategies between the principals. This finding is robust to principal heterogeneity, changes in the number of players involved in the contract, and various forms of uncertainty.
翻译:随着人工智能算法的快速进步,人们希望算法将很快在各个领域中取代人类决策者,比如合同设计。本文通过实验研究了由人工智能(Multi-agent Q-learning)算法驱动的算法在双一级代理问题中工作的基本“双重契约”模型中的行为,分析了可能的后果。我们发现,算法可以自主地学习设计激励相容契约,而不需要外部指导或他们之间的通信。我们强调,由不同的人工智能算法驱动的负责人可以玩混合和博弈行为,如勾结和竞争。我们发现,更聪明的负责人倾向于变得合作,而更不聪明的负责人则出现内化短视,倾向于变得竞争。在最优契约下,代理的较低契约激励被负责人之间的勾结策略所维持。这一发现对负责人异质性、参与合同的玩家数量的变化以及各种不确定性形式都很稳健。