Correlation Neglect in Student-to-School Matching

Alex Rees-Jones, Ran Shorrer, Chloe J. Tergiman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations


A growing body of evidence suggests that many people struggle with decision-making in the presence of correlation. In typical examples of this problem, decision-makers are presented with multiple signals that are each influenced both by independent components and information from a common source. The process by which signals are generated induces correlation, and optimal decision-making requires taking it into account. In practice, however, experiments like those of Enke and Zimmermann [1] demonstrate that many decision-makers neglect to do so, effectively acting as if these correlated signals are independent. We study the prevalence and consequences of these failures of reasoning in a decision of considerable importance: the application strategies of students applying to schools. Many application processes inherently require students to make forecasts of events determined by common underlying inputs, resulting in correlation structures like those described above. For example, students commonly must whittle a large number of schools down to a smaller set that are applied to or ranked, introducing an incentive to avoid listing two programs with highly correlated admissions decisions. In such environments, a student harboring correlation neglect faces a challenging decision.

Original languageEnglish (US)
Title of host publicationEC 2020 - Proceedings of the 21st ACM Conference on Economics and Computation
PublisherAssociation for Computing Machinery
Number of pages2
ISBN (Electronic)9781450379755
StatePublished - Jul 13 2020
Event21st ACM Conference on Economics and Computation, EC 2020 - Virtual, Online, Hungary
Duration: Jul 13 2020Jul 17 2020

Publication series

NameEC 2020 - Proceedings of the 21st ACM Conference on Economics and Computation


Conference21st ACM Conference on Economics and Computation, EC 2020
CityVirtual, Online

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)
  • Economics and Econometrics
  • Statistics and Probability
  • Computational Mathematics


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