Deferred Acceptance Research Articles

Resource-aware fog service placement using deferred acceptance in edge computing

Fog computing aims at reducing network latency, bandwidth and processing delay by bringing computation resources closer to the data sources. Modern IoT applications, such as smart surveillance systems, remote health monitoring applications, ambient-aware applications etc., are time-critical and computationally intensive. For such applications, it is ideal to consider an intermediate layer of processing i.e a fog layer in addition to cloud. However, processing the workload of such IoT applications with an intermediate layer of fog nodes has several challenges. The significant challenges include the mobility of end-user nodes, dynamic patterns of workload generation leading to highly-variant resource requirements and sensitive quality of service (QOS) requirements. Hence a dynamic approach is required to precisely map the service requests from end-user devices to a suitable fog node. The optimality of a fog node for service placement depends on several factors, including location, resource utilization rates such as CPU, Memory and Disk utilization % and energy consumption. Several meta-heuristic techniques and evolutionary algorithms have been proposed in the past for optimal service placement among fog nodes. This paper investigates the applicability of a game theory-based approach called Deferred acceptance (DA) for the same. The DA algorithm, originally designed as a stable matching mechanism for the marriage market is adapted for service placement problems in fog-based IoT systems. The effectiveness of optimized service placement through the DA approach is validated experimentally. The experiment is carried out by creating a test bed that mimics a fog computing-based intelligent traffic management application. The proposed approach's performance is evaluated through parameters such as the startup time for the DA approach to produce outcomes, total execution time and handshake latency. The numerical results of the DA approach are compared with those of a simple scheduling algorithm ( FIFO) and a basic genetic algorithm-based approach. The DA approach appears to perform on par with the genetic algorithm in terms of start-up time, but exceeds the genetic algorithm with respect to stability of the outcomes produced.

Analysis on the Mechanism of Chinese College Admission System

The college entrance examination has a history of more than fourty years in China. With the development of the society, the previous admission system has revealed more and more drawbacks, such as high scores and low admission which means the students who got the high score was not into the high reference college. Also, some schools are not full. In order to continue to exploring the entrance exams admission mechanisms. First, it describes the operation mechanism of the existing admission methods and analyzes the advantages and disadvantages, and then puts forward an application of foreign deferred acceptance (DA) algorithms to the college entrance examination and analyzing the advantages and disadvantages. The paper conducts a questionnaire to investigate the satisfaction of students and parents with the previous admission system and some privileges, and analyses the results of these data. In addition to this, the paper uses an example about Inner Mongolias university admission mechanisms and some drawback of this. Finally, this paper clarifies that the DA algorithm cannot be applied to the college entrance examination admission mechanism because of the large number of candidates and provinces in China.

Expanding Access to Higher Education: The Case for Using Minority Quotas or Reserves to Increase Diversity

This project report aims to explore the use of minority quotas or minority reserves as a means of increasing diversity in university admissions and widening access. The report provides a critical review of the literature on this topic, outlines the benefits and drawbacks of using quotas and reserves, and presents a mathematical model which introduces a fairness constraint to the Men-proposing Deferred Acceptance (DA) Algorithm to address the issues of underrepresented minority groups and promote diversity in the college admissions process. The model is used to match students and schools in a way that maximizes diversity while maintaining fairness and efficiency. This report provides proof and computational results to demonstrate the effectiveness of our approach. The report draws on Alvin E. Roth's The Art of Designing Markets to underscore the importance of designing effective market mechanisms that address the challenges of diversity and equity in university admissions. The report concludes that minority quotas and reserves can be effective tools for promoting diversity and social justice in higher education, but that they must be implemented carefully and in conjunction with other policies aimed at addressing structural inequalities.

CATCHMENT AREAS, STRATIFICATION, AND ACCESS TO BETTER SCHOOLS

AbstractSchool Choice provides students with the opportunity to attend better schools than those in their neighborhood. This is crucial for students from disadvantaged areas where schools may be of lower quality. Our theoretical model and numerical simulations show that the widely used Deferred Acceptance (DA) algorithm has limitations in providing access to better schools (ABS). When schools have varying levels of quality and when there are priorities linked to neighborhood schools, the DA algorithm experiences significant limitations in providing ABS. Top Trading Cycles, when compared to DA, offers greater ABS, particularly for disadvantaged students.

The Performance of School Assignment Mechanisms in Practice

Theory points to a potential trade-off between two main school assignment mechanisms; Boston and Deferred Acceptance (DA). While DA is strategy-proof and gives a stable matching, Boston might outperform DA in terms of ex-ante efficiency. We quantify the (dis)advantages of the mechanisms by using information about actual choices under Boston complemented with survey data eliciting students' school preferences. We find that under Boston around 8% of the students apply to another school than their most-preferred school. We compare allocations resulting from Boston with DA with single tie-breaking (one central lottery; DA-STB) and multiple tie-breaking (separate lottery per school; DA-MTB). DA-STB places more students in their top-n schools, for any n, than Boston and results in higher average welfare. We find a trade-off between DA-STB and DA-MTB. DA-STB places more students in their single most-preferred school than DA-MTB, but fewer in their top-n, for n >= 2. Finally, students from disadvantaged backgrounds benefit most from a switch from Boston to any of the DA mechanisms.

Prospect Theory-Based Two-Sided Satisfied and Stable Matching Mechanism for the Shared Parking Slots Problem

The contradiction between the dramatic increase in the aggregate number of automobiles and the short supply of parking spaces leads to parking difficulties. Sharing mode helps improve the efficiency of existing parking spaces, increase resource utilization, and alleviate the difficulty of parking. This paper focuses on the matching mechanism in the shared parking slots problem, which involves three agents: shared parking suppliers, shared parking demanders, and shared parking platform. We propose a prospect theory-based two-sided satisfied and stable matching model (PT-TSSM) with two objectives to maximize the satisfaction degree of both shared parking demanders and shared parking suppliers. Numerical experiments are illustrated to demonstrate the efficiency of the proposed model. Moreover, the PT-TSSM model is compared with the other two shared parking mechanisms. The proposed model considers the satisfaction degrees of both shared parking demanders and suppliers, while first book first serve (FBFS) cares only one side of the participants. And compared with deferred acceptance (DA), our model not only takes two-sided stable matching into account but also considers the satisfaction degree of all the demand and supply participants, which obtain a two-sided satisfied and stable matching scheme.

Expectations-Based Loss Aversion May Help Explain Seemingly Dominated Choices in Strategy-Proof Mechanisms

Deferred acceptance (DA), a widely implemented algorithm, is meant to improve allocations: under classical preferences, it induces preference-concordant rankings. However, recent evidence shows that—in both real, large-stakes applications and experiments—participants frequently play seemingly dominated, significantly costly strategies that avoid small chances of good outcomes. We show theoretically why, with expectations-based loss aversion, this behavior may be partly intentional. Reanalyzing existing experimental data on random serial dictatorship (a restriction of DA), we show that such reference-dependent preferences, with a degree and distribution of loss aversion that explain common levels of risk aversion elsewhere, fit the data better than no-loss-aversion preferences. (JEL D11, D82, D91)

Reallocation with priorities and minimal envy mechanisms

We investigate the problem of reallocation with priorities where one has to assign objects or positions to individuals. Agents can have an initial ownership over an object. Each object has a priority ordering over the agents. In this framework, there is no mechanism that is both individually rational (IR) and stable, i.e. has no blocking pairs. Given this impossibility, an alternative approach is to compare mechanisms based on the blocking pairs they generate. A mechanism has minimal envy within a set of mechanisms if there is no other mechanism in the set that always leads to a set of blocking pairs included in the one of the former mechanism. Our main result shows that the modified Deferred Acceptance mechanism (Guillen and Kesten in Int Econ Rev 53(3):1027–1046, 2012), is a minimal envy mechanism in the set of IR and strategy-proof mechanisms. We also show that an extension of the Top Trading Cycle (Karakaya et al. in J Econ Theory 184:104948, 2019) mechanism is a minimal envy mechanism in the set of IR, strategy-proof and Pareto-efficient mechanisms. These two results extend the existing ones in school choice.

Tie-breaking and efficiency in the laboratory school choice

In school choice problems with weak priorities, the deferred acceptance (DA) mechanism may produce inefficient stable matchings due to tie-breaking. The stable-improvement-cycles (SIC) and choice-augmented deferred acceptance (CADA) mechanisms were proposed to remedy inefficiencies but they are manipulable. In a simple environment, we theoretically and experimentally analyze students' strategic behavior when DA, SIC, and CADA are implemented. We show that obtaining the efficiency gain relative to DA crucially depends on whether students report their preferences truthfully in SIC and whether they play a particular equilibrium strategy in CADA. Our laboratory experiment reveals that (i) non-negligible degrees of untruthful reporting are observed but they are not a major drawback for practical efficiency improvements of the mechanisms we consider; (ii) SIC achieves gains from trade whenever they exist, both on and off the equilibrium paths; and (iii) the additional layer of equilibrium coordination required by CADA makes it harder for CADA to fully produce the promised welfare advantage relative to DA. These findings are robust to various environments.

A comparison study on responsiveness of three mechanisms to affirmative action in school choice

This note provides a comparison study on responsiveness of three popular mechanisms to affirmative action in school choice. When the exogenous school choice environment gives full priority to the minority, the student-proposing deferred acceptance (DA) mechanism is responsive to both the quota-based and reserve-based affirmative action policies. In comparison with DA mechanism, the top trading cycles (TTC) mechanism is responsive to neither the quota-based nor the reserve-based affirmative action even when the exogenous school choice environment gives full priority to the minority. When each minority student has higher priority than each majority student at each school, the TTC mechanism is responsive to the quota-based affirmative action. However, under the environment as above, the Boston mechanism is not responsive to the quota-based affirmative action.

Development of public cloud manufacturing markets: a mechanism design approach

Online manufacturing markets are new emerging types of markets that are spreading in the shade of public Cloud Manufacturing. This paper introduces a public Cloud Manufacturing market and denotes its differences from a private one. Public Cloud Manufacturing enables service-oriented manufacturing models for everyone in society. The prosperity of such market is tied to appropriate matching between providers to consumers. This paper elaborates on a mechanism to prescribe an appropriate allocation between resources and demands in this public market. The mechanism exploits the VIKOR method to develop preference lists for providers and consumers, the method also is augmented by the Intuitionistic Fuzzy Sets to remove complexities and uncertainties that exist in the process of ranking; and Deferred Acceptance (DA) algorithm is utilised as the matching algorithm of the mechanism. The performance of the mechanism is analysed under influence of the proposer, resource availability and matching algorithm. The influence of proposer study indicates that the situation of the consumer as the proposer is preferred to the providers’ situation as proposer; the influence of resource availability study indicates a balanced situation is preferred to the scarcity and abundancy situations and the influence of matching algorithm indicates that DA outperforms Top Trading Cycle (TTC) algorithm as the matching algorithm of the mechanism.

Multi-phase matching mechanism for stable and optimal resource allocation in cloud manufacturing platforms Using IF-VIKOR method and deferred acceptance algorithm

ABSTRACT Public platforms are one of the most important Cloud Manufacturing modes. Public platforms enable an environment for manufacturers and demanders to freely and directly connect with each other. Exploiting the high potentials of public platforms depends on final matching. This paper has developed a multi-phase matching mechanism for stable and optimal resource allocation in public platform. The proposed mechanism grades the demanders using an intuitionistic fuzzy VIKOR method using three measures of quality, time, and sustainability. Then, the mechanism clusters the demanders based on these three measures; and finally, allocates the clusters using the Deferred Acceptance (DA) algorithm to the manufacturers. The mechanism is examined using a case study from the Iranian automotive industry. The paper has extended and examined the model under three directions of: the grading method impact, clustering analysis impact, and the platform mode impact. Based on the experiments, the intuitionistic fuzzy TOPSIS-DA results on average 4.34% better outcome rather than the intuitionistic fuzzy VIKOR-DA. The proposed heuristic and optimized clustering method results on average 1.09% better solution rather than the KM clustering method. Also, the analysis of the PoS reveals that a private platform yields on average 10% better utility rather than a public platform.

Parallel markets in school choice

When applying to schools, students often submit applications to distinct school systems that operate independently, which leads to waste and distortions of stability due to miscoordination. To alleviate this issue, Manjunath and Turhan (2016) introduce the Iterative Deferred Acceptance mechanism (IDA). We design an experiment to compare the performance of this mechanism under parallel markets (DecDA2) to the classic Deferred Acceptance mechanism with both divided (DecDA) and unified markets (DA). Consistent with the theory, we find that both stability and efficiency are highest under DA, intermediate under DecDA2, and lowest under DecDA. While IDA is not strategy-proof, we show theoretically that strategic reporting can only lead to improved efficiency for all market participants. The experimental results are consistent with this prediction. Our findings cast doubt on whether strategy-proofness should be perceived as a universal constraint to market mechanisms.

Sequential school choice: Theory and evidence from the field and lab

We analyze sequential preference submission in centralized matching problems. Our motivation comes from school districts and colleges that use an application website where students submit their preferences over schools sequentially, after learning information about previous submissions. Comparing the widely used Boston Mechanism (BM) to the celebrated Deferred Acceptance (DA) mechanism, we show that a sequential implementation of BM can achieve superior equilibrium outcomes relative to the student-optimal stable matching. Our empirical tests use data from the field and from a laboratory experiment. We find that sequential preference submission may allow students to overcome the coordination problem in school choice. Our findings may have important policy implications in numerous places across the world where BM is currently in use.

The Effect of Attending a Charter School in Newark, New Jersey, on Student Test Scores

We exploit information about parental preference and a randomized component in the assignment of students to schools within a deferred acceptance (DA) mechanism to estimate the causal effect of enrolling in a charter school in Newark, New Jersey, on student test scores. The estimates incorporate variation from students attending about 70% of the city’s charter schools, accounting for about 85% of charter school enrollment. Enrolling in a Newark charter school that participated in the DA assignment process leads to a large and statistically significant increase in math and English Language Arts (ELA) scores. Enrolling in a charter school that is operated by either the Knowledge Is Power Program (KIPP) or Uncommon charter school networks has an especially large effect.

Manipulability in school choice

We compare the manipulability of school choice mechanisms based on the occurrence of dominant strategies. We characterize dominant strategies in the constrained versions of the deferred acceptance (DA) and Boston (BOS) mechanisms. We leverage our characterizations to show that dominant strategies occur more often in constrained DA than in constrained BOS and that these differences can be quantitatively sizable. Dominant strategies also become more frequent in constrained DA as students are allowed to report more schools. Although our focus is on constrained mechanisms, we provide dominant strategies comparisons for other mechanisms as well, including application-rejection mechanisms and mechanisms that Pareto dominate DA.

Mechanism performance under strategy advice and sub-optimal play: A school choice experiment

We implement a laboratory experiment to study how strategy advice affects participant decisions in a school choice game. In the Deferred Acceptance (DA) mechanism, advice to choose the dominant strategy of truth-telling induces participants to do so. In the Immediate Acceptance (IA) mechanism, advice to implement one of two heuristic strategies induces participants to choose one of those strategies. We develop a new partially-ordered typology of DA strategies to study the sub-optimal strategies chosen by participants under advice versus no advice. Then, using the varying proportions of participants choosing sub-optimal strategies in our data, we perform exploratory analyses on mechanism performance. We find that DA outperforms IA in efficiency, stability, and proportion of participants assigned their most preferred school. These performance differences are larger under strategy advice. (school choice, experiment, strategy advice, mechanism design, sub-optimal play)

A utility-based matching mechanism for stable and optimal resource allocation in cloud manufacturing platforms using deferred acceptance algorithm

Cloud Manufacturing (CM) as a successful manufacturing business model and a major driver of Industry 4.0 has attracted a lot of attention in recent years. CM idea aims to streamline the on-demand provisioning of manufacturing resources and capabilities as services, providing end-users with flexible and scalable services accessible through global networks. This idea created many opportunities and challenges. One of the critical challenges is resource allocation, which determines who interacts with whom and how in the CM platform. The type of the platform is a determining factor for the selection of the appropriate resource allocation. To analyze the impact of the allocation on the utilities, the paper models the behavior of the manufacturing providers and consumers based on their preference attributes. Then, the paper discusses the influence of the platform, matching algorithm, and resource availability on the utility of the manufacturing providers and consumers. As a result, the paper presents a framework to obtain managerial insights to decide about the appropriate matching algorithm under different situations. The framework suggests Consumer as Proposer Deferred Acceptance algorithm for public platforms when the resources are greater than or equal to the demand, and Provider as Proposer Deferred Acceptance algorithm when the resources are less than the demand in the same platform. In private platform, Consumer-oriented Kuhn-Munkres is suggested when the resources are greater than or equal to the demand, and Provider-oriented Kuhn-Munkres when the resources are less than the demand.

Fair Stable Matchings Under Correlated Preferences (Student Abstract)

Stable matching models are widely used in market design, school admission, and donor organ exchange. The classic Deferred Acceptance (DA) algorithm guarantees a stable matching that is optimal for one side (say men) and pessimal for the other (say women). A sex-equal stable matching aims at providing a fair solution to this problem. We demonstrate that under a class of correlated preferences, the DA algorithm either returns a sex-equal solution or has a very low sex-equality cost.

Reallocation with Priorities

We consider a reallocation problem with priorities where each agent is initially endowed with a house and is willing to exchange it but each house has a priority ordering over the agents of the market. In this setting, it is well known that there is no individually rational and stable mechanism. As a result, the literature has introduced a modified stability notion called µ0-stability. In contrast to college admission problems, in which priorities are present but there is no initial endowment, we show that the modified Deferred Acceptance mechanism identified in the literature is not the only individually rational, strategy-proof and µ0-stable mechanism. By introducing a new axiom called the independence of irrelevant agents and using the standard axiom of unanimity, we show that the modified Deferred Acceptance mechanism is the unique mechanism that is individually rational, strategy-proof, µ0-stable, unanimous and independent of irrelevant agents.