Incentives in Two-sided Matching Markets with Prediction-enhanced Preference-formation

TL;DR

This paper explores how prediction mechanisms in two-sided matching markets, like school choice, can be exploited by strategic agents through adversarial interactions, affecting fairness and increasing inequality.

Contribution

It introduces the concept of adversarial interaction attacks in matching markets and models their impact on prediction accuracy and market fairness.

Findings

Adversarial attacks become more effective as prediction trust increases.

Such attacks can lead to increased inequality among agents.

Simulation results demonstrate the potential for strategic manipulation in prediction-enhanced matching.

Abstract

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.