The instability of matching with overconfident agents

Pan, Siqi

doi:10.1016/j.geb.2018.10.001

Cited by 27 publications

(9 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Overconfidence: Overconfidence is a prevalent trait among physicians (25), and is commonly thought to broadly generate decision errors (26). Furthermore, recent research demonstrates that this bias affects suboptimal reporting in the related, but gameable, Boston mechanism (27). We generate a measure of overconfidence in the course of conducting our test of logical reasoning ability.…”

Section: Student Qualitymentioning

confidence: 99%

An experimental investigation of preference misrepresentation in the residency match

Rees-Jones

Skowronek

2018

Proc. Natl. Acad. Sci. U.S.A.

View full text Add to dashboard Cite

SignificancePolicymakers increasingly rely on matching algorithms to assign students to schools. Common algorithms can be “gamed” by students misrepresenting their preferences for schools, resulting in assignments that are unduly influenced by application strategies. In strategy-proof algorithms that incentivize students to tell the truth, this undesirable influence of strategic sophistication is argued to be eliminated. We conduct an online experiment among participants in a leading exemplar of strategy-proof market design: the assignment of new doctors to medical residencies. Our results suggest that many market participants do not understand that telling the truth is optimal. This illustrates that strategy-proof environments are not immune to the influence of strategic sophistication, and that practical tensions arise when using complex means to implement simple incentives.

show abstract

Section: Student Qualitymentioning

confidence: 99%

An experimental investigation of preference misrepresentation in the residency match

Rees-Jones

Skowronek

2018

Proc. Natl. Acad. Sci. U.S.A.

View full text Add to dashboard Cite

show abstract

“…Using laboratory experiments, Lien et al ( 48 ) and Jiang ( 49 ) argue that requiring preference submissions before students take the examination can help correct the observed examination measurement error under IA. However, Pan ( 50 ) finds that preexamination IA rewards overconfidence and creates more mismatches between students and schools. Comparing all three mechanisms in the Chinese school choice context in the laboratory, Chen and Kesten ( 51 ) find that PA is less manipulable and more stable than IA.…”

Section: Related Literaturementioning

confidence: 99%

An empirical evaluation of Chinese college admissions reforms through a natural experiment

Cao

Jiang

Kesten

2020

Proc. Natl. Acad. Sci. U.S.A.

View full text Add to dashboard Cite

College admissions policies affect the educational experiences and labor market outcomes for millions of students each year. In China alone, 10 million high school seniors participate in the National College Entrance Examination to compete for 7 million seats at various universities each year, making this system the largest centralized matching market in the world. The last 20 y have witnessed radical reforms in the Chinese college admissions system, with many provinces moving from a sequential (immediate acceptance) mechanism to some version of the parallel college admissions mechanism, a hybrid between the immediate and deferred acceptance mechanisms. In this study, we use a natural experiment to evaluate the effectiveness of the sequential and parallel mechanisms in motivating student college ranking strategies and providing stable matching outcomes. Using a unique dataset from a province that implemented a partial reform between 2008 and 2009, we find that students list more colleges in their rank-ordered lists, and more prestigious colleges as their top choices, after the province adopts the parallel mechanism in its tier 1 college admissions process. These listing strategies in turn lead to greater stability in matching outcomes, consistent with our theoretical prediction that the parallel mechanism is less manipulable and more stable than the sequential mechanism.

show abstract

“…7 See Hassidim et al (2021); Rees-Jones (2018); Rees-Jones and Skowronek (2018); and Shorrer and Sóvágó (2018). 8 For other examples of experiments testing the role of specific behavioral models as accounts of mistakes in matching markets, see Li (2017) examining failures of contingent reasoning, Pan (2019) or Dargnies et al (2019) examining self-confidence, or Dreyfuss et al (2019) examining expectations-based reference dependence. Note that the models considered in these papers do not predict differences in behavior across our correlated and uncorrelated environment (conditional on choosing one of the focal strategies constructed to have equivalent payoffs across environments).…”

Section: Motivating Matching Environmentsmentioning

confidence: 99%