The Invisible Handshake: Tacit Collusion between Adaptive Market Agents

Luigi Foscari, Emanuele Guidotti, Nicolò Cesa-Bianchi, Tatjana Chavdarova, Alfio Ferrara

Keywords: algorithmic collusion, market microstructure, online learning, game theory

TL;DR: We theoretically show that adaptive agents in a stochastic market with endogenous price formation are incentivized to collude without any explicit coordination.

Abstract: We study the emergence of tacit collusion between adaptive trading agents in a stochastic market with endogenous price formation. Using a two-player repeated game between a market maker and a market taker, we characterize feasible and collusive strategy profiles that raise prices beyond competitive levels. We show that, when agents follow simple learning algorithms (e.g., gradient ascent) to maximize their own wealth, the resulting dynamics converge to collusive strategy profiles, even in highly liquid markets with small trade sizes. By highlighting how simple learning strategies naturally lead to tacit collusion, our results offer new insights into the dynamics of AI-driven markets.

Submission Number: 26