  With the digitalization of the financial market, dealers are increasingly
handling market-making activities by algorithms. Recent antitrust literature
raises concerns on collusion caused by artificial intelligence. This paper
studies the possibility of cooperation between market makers via independent
Q-learning. Market making with inventory risk is formulated as a repeated
general-sum game. Under a stag-hunt type payoff, we find that market makers can
learn cooperative strategies without communication. In general, high spreads
can have the largest probability even when the lowest spread is the unique Nash
equilibrium. Moreover, introducing more agents into the game does not
necessarily eliminate the presence of supra-competitive spreads.
