2023/03/22 by Qi, Qian
Decision Sciences · Economics, Econometrics and Finance · #Artificial Intelligence (cs.AI) #Auction Theory and Applications #Computers and Society (cs.CY) #FOS: Computer and information sciences #FOS: Economics and business #General Economics (econ.GN) #Law, Economics, and Judicial Systems
paper · pdf · doi:10.48550/arxiv.2303.12350
openalex publication_date 2023/03/22 · openalex created_date 2023/03/25 · openalex updated_date 2026/07/28
This paper explores the capacity of artificial intelligence (AI) algorithms to autonomously design incentive-compatible contracts in dual-principal-agent settings, a relatively unexplored aspect of algorithmic mechanism design. We develop a dynamic model where two principals, each equipped with independent Q-learning algorithms, interact with a single agent. Our findings reveal that the strategic behavior of AI principals (cooperation vs. competition) hinges crucially on the alignment of their profits. Notably, greater profit alignment fosters collusive strategies, yielding higher principal profits at the expense of agent incentives. This emergent behavior persists across varying degrees of principal heterogeneity, multiple principals, and environments with uncertainty. Our study underscores the potential of AI for contract automation while raising critical concerns regarding strategic manipulation and the emergence of unintended collusion in AI-driven systems, particularly in the context of the broader AI alignment problem.