Social Learning with Bounded Rationality: Negative Reviews Persist under Newest First

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 bounded rationality, 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 study an extension of the model where customers put more weight on more recent reviews (and discount older reviews based on their time of posting), and we show that Newest First is still not the optimal ordering policy if customers discount slowly. Lastly, we corroborate our theoretical findings using a real-world review dataset. We find that the average rating of the first page of reviews is statistically significantly smaller than the overall average rating, which is in line with our theoretical results.