The Invisible Handshake: Tacit Collusion between Adaptive Market Agents
This reveals a potential issue for AI-driven markets, where automated agents might inadvertently collude, but it is incremental as it builds on existing game theory and learning models.
The study tackled the problem of tacit collusion emerging between adaptive trading agents in a stochastic market, showing that simple learning algorithms like gradient ascent lead to collusive strategies that raise prices beyond competitive levels, even in highly liquid markets.
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.