论文标题
排名目标之间的权衡：减少形式的证据和结构估计
Trade-Offs Between Ranking Objectives: Reduced-Form Evidence and Structural Estimation
论文作者
论文摘要
在线零售商和平台通常使用排名的产品列表提供替代方案。通过调整排名，这些平台会影响消费者的选择，进而影响转化，平台收入和消费者福利。在本文中，我研究了针对这些不同目标的排名算法之间的权衡。首先，我强调并提供了减少形式的证据，证明了塑造这些权衡的关键因素：位置效应中的跨产品异质性。为了量化不同排名的效果，我基于Greminger的搜索和发现模型（2022）开发了一个经验框架。对于此框架，我表明，最大化转化的排名也最大化了消费者的福利，这意味着这两个目标之间没有权衡。此外，我开发和测试了一种启发式排名算法，以最大程度地提高收入。最后，我估计模型并比较针对不同目标开发的排名的效果。结果突出了不同排名的有效性，并揭示了提出的启发式启发式提高收入也增加了消费者的福利，这表明收入最大化与消费者福利之间的权衡也有限。
Online retailers and platforms typically present alternatives using ranked product lists. By adjusting the ranking, these platforms influence consumers' choices and, in turn, conversions, platform revenues, and consumer welfare. In this paper, I study the trade-offs between ranking algorithms that target these different objectives. First, I highlight and provide reduced-form evidence for a key factor shaping these trade-offs: cross-product heterogeneity in position effects. To quantify the effects of different rankings, I then develop an empirical framework based on the search and discovery model of Greminger (2022). For this framework, I show that the ranking that maximizes conversions also maximizes consumer welfare, implying no trade-off between these two objectives. Moreover, I develop and test a heuristic ranking algorithm to maximize revenues. Finally, I estimate the model and compare the effects of the rankings developed for the different objectives. The results highlight the effectiveness of the different rankings and reveal that the proposed heuristic to maximize revenues also increases consumer welfare, suggesting that the trade-off between revenue maximization and consumer welfare also is limited.
