[2303.12350] Artificial Intelligence and Dual Contract

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.