Tacit Coordination in AI-Driven Liquidity Provision: A Practitioner’s Diagnostic Toolkit

Modern electronic markets are increasingly populated by adaptive artificial intelligence (AI)–driven trading systems that continuously learn from market outcomes and from one another. This article studies a specific form of algorithmic interaction risk. We simulate multiple market-making agents using tabular Q-learning to optimize quote aggressiveness in the presence of noise traders. Although the agents do not communicate or optimize a joint objective, repeated interaction leads to persistent spread widening, increased dealer profitability, and deterioration in transaction-cost proxies faced by liquidity demanders. Using memory-1 Q-learning agents that compete only on spread, we find that two-dealer markets converge to outcomes that close 78% of the gap between the static Bertrand–Nash equilibrium and joint monopoly, with 97.5% of the joint-action mass on the diagonal (both liquidity providers quoting the same spread). This article develops several observable diagnostics for detecting such behavior, including spread persistence, quote clustering, and forced-deviation response tests. Unlike prior work focused on informed trading and price discovery in Kyle-style environments, our framework emphasizes quote competition, liquidity provision, and execution quality in electronic markets. The results suggest that adaptive interaction among AI trading systems may create new forms of algorithmic vulnerability relevant for exchanges, trading venues, execution desks, and market-surveillance teams.