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Screening with Disadvantaged Agents

Authors Hedyeh Beyhaghi , Modibo K. Camara , Jason Hartline , Aleck Johnsen , Sheng Long

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Author Details

Hedyeh Beyhaghi
  • Carnegie Mellon University, Pittsburgh, PA, USA
Modibo K. Camara
  • University of Chicago, IL, USA
Jason Hartline
  • Northwestern University, Evanston, IL, USA
Aleck Johnsen
  • Geminus Research, Cambridge, MA, USA
Sheng Long
  • Northwestern University, Evanston, IL, USA

Funding

  • Hartline, Jason: Supported in part by NSF CCF 1934931.
  • Long, Sheng: Supported in part by NSF CCF 1934931.

Cite As Get BibTex

Hedyeh Beyhaghi, Modibo K. Camara, Jason Hartline, Aleck Johnsen, and Sheng Long. Screening with Disadvantaged Agents. In 4th Symposium on Foundations of Responsible Computing (FORC 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 256, pp. 6:1-6:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023) https://doi.org/10.4230/LIPIcs.FORC.2023.6

Abstract

Motivated by school admissions, this paper studies screening in a population with both advantaged and disadvantaged agents. A school is interested in admitting the most skilled students, but relies on imperfect test scores that reflect both skill and effort. Students are limited by a budget on effort, with disadvantaged students having tighter budgets. This raises a challenge for the principal: among agents with similar test scores, it is difficult to distinguish between students with high skills and students with large budgets.
Our main result is an optimal stochastic mechanism that maximizes the gains achieved from admitting "high-skill" students minus the costs incurred from admitting "low-skill" students when considering two skill types and n budget types. Our mechanism makes it possible to give higher probability of admission to a high-skill student than to a low-skill, even when the low-skill student can potentially get higher test-score due to a higher budget. Further, we extend our admission problem to a setting in which students uniformly receive an exogenous subsidy to increase their budget for effort. This extension can only help the school’s admission objective and we show that the optimal mechanism with exogenous subsidies has the same characterization as optimal mechanisms for the original problem.

Subject Classification

ACM Subject Classification
  • Applied computing → Economics
  • Theory of computation → Algorithmic mechanism design
Keywords
  • screening
  • strategic classification
  • budgeted mechanism design
  • fairness
  • effort-incentives
  • subsidies
  • school admission

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References

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