Collusive AI? Rethinking Platform Governance in the Age of Emergent Agent Behavior

Building on Lillo & Macri (2024), who found that RL agents in financial markets could tacitly collude and deviate from Nash equilibrium, this idea explores what happens when platforms—be they marketplaces, gig economy, or digital ad exchanges—are increasingly populated by autonomous agents. Existing governance and anti-collusion mechanisms (designed for human actors) may be obsolete when agents “learn” to collude or game the system in unforeseen ways. This research would combine simulation, empirical case studies, and regulatory analysis to propose new governance frameworks that anticipate and counteract emergent agent behavior. It pushes the literature on platform governance (see Lin et al., 2024) into the algorithmic age and has major implications for competition policy, trust, and platform health.

References:

1. Deviations from the Nash equilibrium and emergence of tacit collusion in a two-player optimal execution game with reinforcement learning. Fabrizio Lillo, Andrea Macri (2024).
2. Governance of corporate social responsibility: a platform ecosystem perspective. Wenfang Lin, Yifeng Wang, Georges Samara, Jintao Lu (2024). Management Decision.