Algorithmic Coercion with Faster Pricing

Zach Brown and Alexander MacKay

No 34070, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: We examine a model in which one firm uses a pricing algorithm that enables faster pricing and multi-period commitment. We characterize a coercive equilibrium in which the algorithmic firm maximizes its profits subject to the incentive compatibility constraint of its rival. By adopting an algorithm that enables faster pricing and (imperfect) commitment, a firm can unilaterally induce substantially higher equilibrium prices even when its rival maximizes short-run profits and cannot collude. The algorithmic firm can earn profits that exceed its share of collusive profits, and coercive equilibrium outcomes can be worse for consumers than collusive outcomes. In extensions, we incorporate simple learning by the rival, and we explore the implications for platform design.

JEL-codes: D43 L13 L40 L81 L86 (search for similar items in EconPapers)
Date: 2025-07
Note: IO
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