Algorithmic Coercion with Faster Pricing

We study price competition when one firm uses a pricing algorithm that can react quickly to a rival’s price. We characterize a coercive equilibrium in which the algorithm encodes a persistent rule that maximizes discounted profits subject to the rival’s incentive compatibility constraint. The combination of faster pricing and multi-period commitment allows the algorithmic firm to unilaterally induce supracompetitive prices even when the rival is myopic and cannot sustain collusion. The algorithmic firm can earn more than its full collusion profits, and consumer surplus can fall below the full collusion benchmark. When the rival uses a learning rule to set prices, we show via simulations that outcomes rapidly converge to the coercive equilibrium. Finally, we demonstrate the implications of our framework for platform design.