Artificial Intelligence and Dual Contract
It addresses the AI alignment problem by highlighting risks of strategic manipulation and unintended collusion in AI-driven contract automation systems, which is an incremental contribution to algorithmic mechanism design.
This paper tackles the problem of AI algorithms autonomously designing incentive-compatible contracts in dual-principal-agent settings, finding that greater profit alignment between AI principals leads to collusive strategies, increasing principal profits by fostering cooperation at the expense of agent incentives.
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.