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 one low rating early on, negatively 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 economic 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 producer'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, 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：在线市场利用评分系统促进高质量产品的发现。然而，这些系统也导致生产者经济结果的高方差：一个销售高质量商品的新生产者可能不幸在早期获得一条低评分，从而对其未来的受欢迎度产生负面影响。我们研究评分系统的设计，以平衡识别高质量产品（效率）和最小化类似质量生产者经济结果方差（个体生产者公平性）这两个目标。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差。我们引入先验加权评分系统作为管理这种权衡的方法。非正式地说，我们提出的系统为每一款进入市场的产品设定一个系统范围的先验质量；随后，系统根据用户随时间产生的评分，将这一先验更新为每个生产者质量的后验。我们从理论上证明，在产品以相同速率获得评论的市场中，评分系统的先验强度决定了已识别权衡中的操作点：先验越强，市场越倾向于忽略早期评分数据（增加个体公平性），但平台学习真实产品品质的速度越慢（因此效率受损）。我们进一步在客户根据历史评分做出决策的反应性市场中分析这种权衡。通过校准模拟，我们展示了在此设定中先验强度的选择同样调节着效率与一致性之间的权衡。总体而言，我们证明通过将先验作为先验加权评分系统中的设计选择进行调优，平台可以有意识地平衡效率与生产者公平性。