Online marketplaces use rating systems to promote the discovery of high-quality products. However, these systems also lead to high variance in producers' economic outcomes: a new producer who sells high-quality items, may unluckily receive a low rating early, severely impacting their future popularity. We investigate the design of rating systems that balance the goals of identifying high-quality products (``efficiency'') and minimizing the variance in outcomes of producers of similar quality (individual ``producer fairness''). We show that there is a trade-off between these two goals: rating systems that promote efficiency are necessarily less individually fair to producers. We introduce prior-weighted rating systems as an approach to managing this trade-off. Informally, the system we propose sets a system-wide prior for the quality of an incoming product; subsequently, the system updates that prior to a posterior for each product's quality based on user-generated ratings over time. We show theoretically that in markets where products accrue reviews at an equal rate, the strength of the rating system's prior determines the operating point on the identified trade-off: the stronger the prior, the more the marketplace discounts early ratings data (increasing individual fairness), but the slower the platform is in learning about true item quality (so efficiency suffers). We further analyze this trade-off in a responsive market where customers make decisions based on historical ratings. Through calibrated simulations in 19 different real-world datasets sourced from large online platforms, we show that the choice of prior strength mediates the same efficiency-consistency trade-off in this setting. Overall, we demonstrate that by tuning the prior as a design choice in a prior-weighted rating system, platforms can be intentional about the balance between efficiency and producer fairness.

翻译：在线市场平台采用评分系统以促进高质量商品的发现。然而，这些系统也导致生产者经济结果的高方差：一位销售高质量商品的新生产者可能不幸在早期获得低评分，从而严重影响其未来受欢迎程度。本研究探讨如何设计评分系统以平衡两大目标：识别高质量商品（“效率”）与最小化相似质量生产者之间的结果方差（个体“生产者公平”）。我们证明这两个目标之间存在权衡：促进效率的评分系统必然对生产者个体公平性较低。我们引入先验加权评分系统作为管理这一权衡的方法。简言之，所提出的系统为新增商品的质量设定一个系统级先验；随后，系统根据用户随时间生成的评分，将该先验更新为每个商品质量的后验估计。我们从理论上证明，在商品以相同速率累积评论的市场中，评分系统先验的强度决定了所识别权衡的操作点：先验越强，市场对早期评分数据的折扣越大（个体公平性提高），但平台学习真实商品质量的速度越慢（效率受损）。我们进一步在客户基于历史评分做出决策的响应式市场中分析这一权衡。通过对来自大型在线平台的19个不同真实数据集进行校准模拟，我们证明先验强度的选择在此情境下同样调节着效率与一致性之间的权衡。总体而言，我们表明通过在先验加权评分系统中将先验强度作为设计参数进行调节，平台可以有意识地平衡效率与生产者公平性。