Designing Direct Matching Mechanisms for India with Comprehensive Affirmative Action

Aygün, Orhan; Turhan, Bertan

doi:10.2139/ssrn.3582669

Cited by 4 publications

(3 citation statements)

References 51 publications

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“…We observe that students in district 31 strongly prefer Staten Island Tech Lastly, we would like to point out some concerns that are not directly observable from our data. Aygun and Turhan (2020) noted that for admissions to Indian Institutes of Technology (IIT), there is often social stigma associated with reserved seats and thus, many students prefer to not be admitted via reserved seats. We also note that NYC DOE defines disadvantaged students based on their social economic status instead of a caste system as in the case of IIT admission.…”

Section: C4 From Section 43mentioning

confidence: 99%

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Discovering Opportunities in New York City's Discovery Program: an Analysis of Affirmative Action Mechanisms

Faenza¹,

Gupta²,

Zhang³

2022

Preprint

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Discovery program (DISC) is an affirmative action policy used by the New York City Department of Education (NYC DOE) to increase the number of admissions for disadvantaged students at specialized high schools.For instance, in the 2016-2017 academic year, this program resulted in about 1, 350 more disadvantaged students being admitted, almost tripling the number of disadvantaged students prior to the introduction of the program. However, our empirical analysis of the student-school matches from the 12 recent academic years (2005-06 to 2016-17) shows that about 950 in-group blocking pairs were created each year amongst disadvantaged group of students, impacting about 650 disadvantaged students every year. Moreover, we find that this program usually benefits lower-performing disadvantaged students more than top-performing disadvantaged students (in terms of the ranking of their assigned schools), thus unintentionally creating an incentive to under-perform.In this work, we explore two affirmative action policies that can be used to minimally modify and improve the discovery program: the minority reserve (MR) and the joint-seat allocation (JSA) mechanism. Theoretically, by employing choice functions, we show that (i) both MR and JSA result in no in-group blocking pairs, and (ii) JSA is weakly group strategy-proof, ensures that at least one disadvantaged is not worse off, and when reservation quotas are carefully chosen then no disadvantaged student is worse-off. We show that each of these properties is not satisfied by DISC. In the general setting, we show that there is no clear winner in terms of the matchings provided by DISC, JSA and MR, from the perspective of disadvantaged students. We however characterize a condition for markets, that we term high competitiveness, where JSA dominates MR for disadvantaged students. This condition is verified, in particular, in certain markets when there is a higher demand for seats than supply, and the performances of disadvantaged students are significantly lower than that of advantaged students. Data from NYC DOE satisfy the high competitiveness condition, and for this dataset our empirical results corroborate our theoretical predictions, showing the superiority of JSA. We believe that the discovery program, and more generally affirmative action mechanisms, can be changed for the better by implementing the JSA mechanism, leading to incentives for the top-performing disadvantaged students while providing many benefits of the affirmative action program.

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Section: C4 From Section 43mentioning

confidence: 99%

“…We are not aware of this stigma being present in NYC SHSs, but it is definitely present in other markets employing some form of affirmative action mechanisms(Aygun and Turhan 2020).…”

mentioning

confidence: 96%

Discovering Opportunities in New York City's Discovery Program: an Analysis of Affirmative Action Mechanisms

Faenza¹,

Gupta²,

Zhang³

2022

Preprint

View full text Add to dashboard Cite

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“…In all of the above cases, the decision on which type a student should use, is made in a greedy or sequential manner and hence this greedy approach may not maximally satisfy diversity goals when students have overlapping types. A similar approach has been used by Kominers and Sönmez [31,32], Aygün and Bó [4], and Aygun and Turhan [7], Aygün and Turhan [8]. In contrast, our framework captures new diversity goals and our methodology optimally achieves these goals via a so called 'smart reserves approach.…”

Section: Related Workmentioning

confidence: 99%

Multi-Rank Smart Reserves

Aziz

Sun

2021

Proceedings of the 22nd ACM Conference on Economics and Computation

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We study the school choice problem where each school has flexible multi-ranked diversity goals, and each student may belong to multiple overlapping types, and consumes only one of the positions reserved for their types. We propose a novel choice function and show that it is the unique rule that satisfies three fundamental properties: maximal diversity, non-wastefulness, and justified envy-freeness. We provide a fast polynomialtime algorithm for our choice function that is based on the Dulmage Mendelsohn Decomposition Theorem as well as new insights into the combinatorial structure of constrained rank maximal matchings. Even for the case of minimum and maximum quotas for types (that capture two ranks), ours is the first known polynomial-time approach to compute an optimally diverse choice outcome. Finally, we prove that the choice function we design for schools, satisfies substitutability and hence can be directly embedded in the generalized deferred acceptance algorithm to achieve strategyproofness and stability. Our algorithms and results have immediate policy implications and directly apply to a variety of scenarios, such as where hiring positions or scarce medical resources need to be allocated while taking into account diversity concerns or ethical principles.CCS Concepts: • Theory of computation → Algorithmic game theory and mechanism design; Solution concepts in game theory; • Applied computing → Economics.

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Multi-rank smart reserves: A general framework for selection and matching diversity goals

Aziz,

Sun

2025

Artificial Intelligence

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Designing Direct Matching Mechanisms for India with Comprehensive Affirmative Action

Cited by 4 publications

References 51 publications

Discovering Opportunities in New York City's Discovery Program: an Analysis of Affirmative Action Mechanisms

Discovering Opportunities in New York City's Discovery Program: an Analysis of Affirmative Action Mechanisms

Multi-Rank Smart Reserves

Multi-rank smart reserves: A general framework for selection and matching diversity goals

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