  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.
