AI 中文总结
该研究针对在线评分操纵问题，构建含低质量卖家、买家与平台的模型，发现评分可信度可倒置、针对性执法会转移虚假评论，提出买家导向排名需考虑评分传达信息。
AI 中文摘要
在线评分将冗长的评论历史转化为一个公开数字，这使得卖家之间的比较更加便捷，但也为卖家提供了一个明确的操纵目标。我们研究了这样一个模型：包含能够添加不同分数虚假评论的低质量卖家、根据显示的平均评分推断商品质量的买家，以及能够针对特定分数进行执法的平台。该模型将买家看到的评分与生成该评分的隐藏评论组合分离开来，两者无需同步变动。当低质量卖家尤其倾向于制造最高评分时，近乎完美的评分可能比略低的评分可信度更低；因此，若买家对某卖家而言更有价值，该卖家可能会降低评分展示程度并增加销量。在固定显示评分的情况下，针对性执法可能会将虚假评论转向其他分数而非消除操纵；由于显示的平均评分未变，买家无法观察到这种替代效应。因此，原始评分仅能提供可信度和执法情况的部分图景，面向买家的排名应考虑评分所传达的信息，而非仅关注其数值水平。
英文摘要
Online ratings turn a long review history into one public number. This makes sellers easier to compare, but it also gives them a single clear target to manipulate. We study a low-quality seller who can add fake reviews carrying different scores, buyers who infer quality from the displayed average, and a platform that can target particular scores for enforcement. The model separates the rating buyers see from the hidden mix of reviews used to produce it, and the two need not move together. An almost-perfect rating can be less credible than a slightly lower one when low-quality sellers are especially likely to manufacture the top of the scale, so a seller whose buyers become more valuable may display less and sell more. At a fixed displayed rating, targeted enforcement can redirect fake reviews toward other scores rather than eliminate manipulation; buyers do not see this substitution because the displayed average is unchanged. Raw ratings therefore provide only a partial picture of credibility and enforcement, and buyer-oriented ranking should account for what a rating conveys, not only its numerical level.
