We investigate the ability of a platform to design its marketplace to promote competition, improve consumer surplus, and increase its own payoff. We consider demand-steering rules that reward firms that cut prices with additional exposure to consumers. We examine the impact of these rules both in theory and by using simulations with artificial intelligence pricing algorithms (specifically Q-learning algorithms, which are commonly used in computer science). Our theoretical results indicate that these policies (which require little information to implement) can have strongly beneficial effects, even when sellers are infinitely patient and seek to collude. Similarly, our simulations suggest that platform design can benefit consumers and the platform, but that achieving these gains may require policies that condition on past behavior and treat sellers in a nonneutral fashion. These more sophisticated policies disrupt the ability of algorithms to rotate demand and split industry profits, leading to low prices.

中文翻译：

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卖家使用定价算法时的平台设计

我们调查平台设计其市场以促进竞争、提高消费者剩余并增加自身回报的能力。我们考虑制定需求引导规则，奖励那些降低价格并增加与消费者接触的公司。我们从理论上并通过使用人工智能定价算法（特别是计算机科学中常用的 Q 学习算法）进行模拟来研究这些规则的影响。我们的理论结果表明，即使卖家无限耐心并寻求串通，这些政策（只需很少的信息即可实施）也可以产生强烈的有益效果。同样，我们的模拟表明平台设计可以使消费者和平台受益，但要实现这些成果，可能需要制定以过去行为为条件的政策，并以非中立的方式对待卖家。这些更复杂的政策破坏了算法旋转需求和分割行业利润的能力，导致价格低廉。