We study competitive dynamic pricing among multiple sellers, motivated by the rise of large-scale experimentation and algorithmic pricing in retail and online marketplaces. Sellers repeatedly set prices using simple learning rules and observe only their own prices and realized demand, even though demand depends on all sellers' prices and is subject to random shocks. Each seller runs two-point A/B price experiments, in the spirit of switchback-style designs, and updates a baseline price using a linear demand estimate fitted to its own data. Under certain conditions on demand, the resulting dynamics converge to a Conjectural Variations (CV) equilibrium, a classic static equilibrium notion in which each seller best responds under a conjecture that rivals' prices respond systematically to changes in its own price. Unlike standard CV models that treat conjectures as behavioral primitives, we show that these conjectures arise endogenously from the bias in demand learning induced by correlated experimentation (e.g., due to synchronized repricing schedules). This learning bias selects the long-run equilibrium, often leading to supra-competitive prices. Notably, we show that under independent experimentation, this bias vanishes and the learning dynamics converge to the standard Nash equilibrium. We provide simple sufficient conditions on demand for convergence in standard models and establish a finite-sample guarantee: up to logarithmic factors, the squared price error decays on the order of $T^{-1/2}$. Our results imply that in competitive markets, experimentation design can serve as a market design lever, selecting the equilibrium reached by practical learning algorithms.

翻译：本研究探讨多个卖家之间的竞争性动态定价问题，其现实背景是零售与在线市场中大规模实验与算法定价的兴起。卖家使用简单的学习规则重复设定价格，且仅能观察到自身价格与实现的需求量，尽管实际需求取决于所有卖家的价格并受到随机冲击的影响。每个卖家采用类似切换式设计的两点A/B价格实验，并基于自身数据拟合的线性需求估计来更新基准价格。在满足特定需求条件时，该动态过程会收敛至推测变差（CV）均衡——一种经典的静态均衡概念，其中每个卖家在推测竞争对手价格会对其自身价格变动做出系统性反应的条件下实现最优响应。与将推测视为行为原型的标准CV模型不同，我们证明这些推测内生于相关实验（如同步调价计划）引发的需求学习偏差。这种学习偏差决定了长期均衡的选择，通常导致超竞争价格的形成。值得注意的是，我们证明在独立实验条件下，这种偏差会消失且学习动态将收敛至标准纳什均衡。我们为标准模型中的收敛性提供了简洁的充分需求条件，并建立了有限样本保证：在忽略对数因子的情况下，价格平方误差以$T^{-1/2}$量级衰减。研究结果表明，在竞争性市场中，实验设计可作为市场设计的调控杠杆，影响实际学习算法所达成的均衡选择。