Strategic Ranking
This addresses fairness and efficiency issues in allocation systems for applicants and institutions, though it is incremental by extending strategic classification to ranking contexts.
The paper tackles the problem of strategic classification in competitive settings like college admissions by introducing strategic ranking, where individual rewards depend on post-effort rank, and finds that randomization in reward design can mitigate disparate impact, reducing welfare gap and access inequities.
Strategic classification studies the design of a classifier robust to the manipulation of input by strategic individuals. However, the existing literature does not consider the effect of competition among individuals as induced by the algorithm design. Motivated by constrained allocation settings such as college admissions, we introduce strategic ranking, in which the (designed) individual reward depends on an applicant's post-effort rank in a measurement of interest. Our results illustrate how competition among applicants affects the resulting equilibria and model insights. We analyze how various ranking reward designs, belonging to a family of step functions, trade off applicant, school, and societal utility, as well as how ranking design counters inequities arising from disparate access to resources. In particular, we find that randomization in the reward design can mitigate two measures of disparate impact, welfare gap and access.