Artificial Intelligence and Dual Contract

TL;DR

This paper investigates how AI algorithms can autonomously design incentive-compatible contracts in dual-principal-agent settings, revealing emergent collusive behaviors and raising concerns about strategic manipulation and AI alignment.

Contribution

It introduces a dynamic model where AI principals interact with an agent, demonstrating how profit alignment influences strategic behaviors like collusion.

Findings

AI principals can develop collusive strategies when profits are aligned.

Emergent behaviors persist across different environments and principal heterogeneity.

AI-driven contract design raises concerns about manipulation and unintended collusion.

Abstract

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.