  In a semi-realistic market simulator, independent reinforcement learning
algorithms may facilitate market makers to maintain wide spreads even without
communication. This unexpected outcome challenges the current antitrust law
framework. We study the effectiveness of maker-taker fee models in preventing
cooperation via algorithms. After modeling market making as a repeated
general-sum game, we experimentally show that the relation between net
transaction costs and maker rebates is not necessarily monotone. Besides an
upper bound on taker fees, we may also need a lower bound on maker rebates to
destabilize the cooperation. We also consider the taker-maker model and the
effects of mid-price volatility, inventory risk, and the number of agents.
