Artificial Intelligence and Dual Contract

TL;DR

The paper investigates whether AI agents can autonomously design incentive-compatible contracts in a dynamic dual-principal–agent setting using multi-agent reinforcement learning (MARL). It develops a dynamic model where two principals, each with independent Q-learning, interact with a single agent, and first analyzes a single-principal baseline before extending to a dual-contract framework with profit alignment and heterogeneity parameters. Key findings show that greater profit alignment (\gamma) fosters emergent cooperation or collusion among AI principals, yielding higher principal profits at the expense of agent incentives, with robustness to heterogeneity (\kappa) and scenarios with more principals. The results illustrate both the potential for AI-driven contract automation and the risk of unintended algorithmic collusion in AI-aligned systems, emphasizing careful design to mitigate welfare losses and ensure fair, efficient outcomes.

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.

Figures (17)

• Figure 1: Impact of Learning Rate $\alpha$ and Exploration Rate $\beta$ on Q-learning Dynamics in a Dynamic Contract Setting. The heatmaps depict the average values of six key metrics over 1000 simulation sessions for each combination of $\alpha$ and $\beta$. Panel A illustrates the average profit accrued by the principal. Panel B shows the average profit gained by the agent. Panel C presents the average tax rate chosen by the principal. Panel D depicts the average effort exerted by the agent. Panel E highlights the converged tax rate, if achieved. Panel F displays the number of iterations required for convergence.
• Figure 2: Average values for Principal 1 profit, Principal 2 profit, effort for Project 1, effort for Project 2, tax rate for Principal 1, and tax rate for Principal 2 for $\gamma=0, \kappa=0$. The heatmaps illustrate the impact of learning rate $\alpha$ and exploration rate $\beta$ on these six variables.
• Figure 3: Convergence Iteration for Principal 1 and Principal 2 for $\gamma=0, \kappa=0$. The heatmap illustrates the impact of learning rate $\alpha$ and exploration rate $\beta$ on the convergence iteration.
• Figure 4: Average values for Principal 1 profit, Principal 2 profit, effort for Project 1, effort for Project 2, tax rate for Principal 1, and tax rate for Principal 2 for $\gamma=0.25, \kappa=0$. The heatmaps illustrate the impact of learning rate $\alpha$ and exploration rate $\beta$ on these six variables.
• Figure 5: Average values for Principal 1 profit, Principal 2 profit, effort for Project 1, effort for Project 2, tax rate for Principal 1, and tax rate for Principal 2 for $\gamma=0.5, \kappa=0$. The heatmaps illustrate the impact of learning rate $\alpha$ and exploration rate $\beta$ on these six variables.