We study a model of social learning from reviews where customers are computationally limited and make purchases based on reading only the first few reviews displayed by the platform. Under this limited attention, we establish that the review ordering policy can have a significant impact. In particular, the popular Newest First ordering induces a negative review to persist as the most recent review longer than a positive review. This phenomenon, which we term the Cost of Newest First, can make the long-term revenue unboundedly lower than a counterpart where reviews are exogenously drawn for each customer. We show that the impact of the Cost of Newest First can be mitigated under dynamic pricing, which allows the price to depend on the set of displayed reviews. Under the optimal dynamic pricing policy, the revenue loss is at most a factor of 2. On the way, we identify a structural property for this optimal dynamic pricing: the prices should ensure that the probability of a purchase is always the same, regardless of the state of reviews. We also consider a setting where product quality evolves over time according to a Markov chain; we find that Newest First better tracks current quality but still leads to lower revenue, highlighting a trade-off between customer belief accuracy and revenue. Finally, numerical simulations confirm the robustness of the Cost of Newest First across several modeling variants.

翻译：我们研究了基于评价的社会学习模型，其中消费者计算能力有限，仅通过阅读平台显示的前几条评价作出购买决策。在有限注意力条件下，我们证实评价排序策略会产生显著影响。特别是，流行的"最新优先"排序会导致负面评价作为最新评价的持续时间长于正面评价。我们将此现象称为"最新优先成本"，其可能使长期收入无界地低于每位消费者随机获取评价的对照情形。我们证明，动态定价可缓解"最新优先成本"的影响——该方法允许价格根据显示的评价集合进行调整。在最优动态定价策略下，收入损失至多不超过两倍。在此过程中，我们识别出该最优动态定价的结构性特征：无论评价状态如何，定价应确保购买概率始终保持恒定。我们还考虑了产品质量遵循马尔可夫链随时间演变的情形，发现"最新优先"排序虽能更好追踪当前质量，但仍会导致更低收入，凸显消费者信念准确性与收入之间的权衡。最终，数值模拟证实"最新优先成本"在多种建模变体中均具有稳健性。