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成果的高方差：销售高质量商品的新生产者可能不幸在早期获得一个低评分，进而对其未来的热度产生负面影响。我们研究了评分系统的设计，旨在平衡识别高质量产品（效率）与最小化同类质量生产者经济成果方差（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的个体公平性较低。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，我们提出的系统为进入市场的产品设定一个系统级的质量先验；随后，系统根据用户生成的评分随时间将先验更新为每个生产者质量的后验。我们在理论上证明，在产品以同等速率积累评论的市场中，评分系统先验的强度决定了已识别权衡上的操作点：先验越强，市场对早期评分数据的折扣程度越大（提高个体公平性），但平台了解真实商品质量的速度越慢（因此效率受损）。我们进一步在客户基于历史评分做决策的响应市场中分析了这种权衡。通过校准模拟，我们表明在此情境中先验强度的选择调节了相同的效率-一致性权衡。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参，平台可以有意识地平衡效率与生产者公平性。