Two-sided matching markets have long existed to pair agents in the absence of regulated exchanges. A common example is school choice, where a matching mechanism uses student and school preferences to assign students to schools. In such settings, forming preferences is both difficult and critical. Prior work has suggested various prediction mechanisms that help agents make decisions about their preferences. Although often deployed together, these matching and prediction mechanisms are almost always analyzed separately. The present work shows that at the intersection of the two lies a previously unexplored type of strategic behavior: agents returning to the market (e.g., schools) can attack future predictions by interacting short-term non-optimally with their matches. Here, we first introduce this type of strategic behavior, which we call an `adversarial interaction attack'. Next, we construct a formal economic model that captures the feedback loop between prediction mechanisms designed to assist agents and the matching mechanism used to pair them. This economic model allows us to analyze adversarial interaction attacks. Finally, using school choice as an example, we build a simulation to show that, as the trust in and accuracy of predictions increases, schools gain progressively more by initiating an adversarial interaction attack. We also show that this attack increases inequality in the student population.

中文翻译：

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具有预测增强偏好形成的双边匹配市场的激励

在没有受监管的交易所的情况下，双边匹配市场早已存在以配对代理。一个常见的例子是学校选择，其中匹配机制使用学生和学校的偏好来将学生分配到学校。在这种情况下，形成偏好既困难又关键。先前的工作提出了各种预测机制，可帮助代理做出有关其偏好的决定。尽管经常一起部署，但这些匹配和预测机制几乎总是分开分析。目前的工作表明，在两者的交集处存在一种以前未探索过的战略行为：返回市场的代理（例如，学校）可以通过与他们的比赛进行短期非最佳互动来攻击未来的预测。在这里，我们首先介绍这种类型的战略行为，我们称之为“对抗性交互攻击”。接下来，我们构建了一个正式的经济模型，该模型捕获旨在帮助代理的预测机制与用于配对的匹配机制之间的反馈循环。这种经济模型使我们能够分析对抗性交互攻击。最后，以学校选择为例，我们构建了一个模拟来表明，随着预测的信任度和准确性的提高，学校通过发起对抗性交互攻击逐渐获得更多收益。我们还表明，这种攻击加剧了学生群体的不平等。这种经济模型使我们能够分析对抗性交互攻击。最后，以学校选择为例，我们构建了一个模拟来表明，随着预测的信任度和准确性的提高，学校通过发起对抗性交互攻击逐渐获得更多收益。我们还表明，这种攻击加剧了学生群体的不平等。这种经济模型使我们能够分析对抗性交互攻击。最后，以学校选择为例，我们构建了一个模拟来表明，随着预测的信任度和准确性的提高，学校通过发起对抗性交互攻击逐渐获得更多收益。我们还表明，这种攻击加剧了学生群体的不平等。