Full Ranking Research Articles

An integrated multi-polar fuzzy N-soft preference ranking organization method for enrichment of evaluations of the digitization of global economy

Digitization represents the ultimate expression of globalization that has revolutionized every facet of global existence, enhancing connectivity, financial terms, trade opportunities, and public services. To further broaden or fortify this digital realm and advance global progress, various strategies for multi-criteria group decision-making (MCGDM) have been developed. This research article extends the literature study on the sensational MCGDM method known as Preference Ranking Organization Method for Enrichment of Evaluations (PROMETHEE), which shares the burden of selecting the best alternative under many terms and conditions. This article aims to make a positive contribution to the development of the digital world. The main principle of the PROMETHEE technique is to calculate positive and negative flows of the selected options according to the divergence of the alternatives’ scores. It provides us with partial and full rankings of options by determining score degrees, suitable preference functions, and a multi-criteria preference index. To deal with the multi-polarity of the modern world and assign N-ordered levels, this paper proposes the multi-polar fuzzy N-soft (mFNS) PROMETHEE technique. Prior to this, an Analytic Hierarchy Process (AHP) technique is applied to guarantee the credibility of the criterion weights for each criterion. After that, the whole strategy of m-polar fuzzy N-soft PROMETHEE approach to order the selected options is explained, and all the course of action of this remarkable (MCGDM) technique are synchronized in an extensive flowchart, helping us to learn more about the technology keenly. Moreover, the utility of our discussed technology is illustrated by considering two applications of generating electricity through human movement and choosing the best digital currency. The most suitable choice is extracted with the help of the outranked directed graph. The results obtained by mFNS PROMETHEE technique benefits us with the selection of most reliable source to digitized the global economy, in addition, It enables us to rank the alternatives from most preferable to least one, that not only saves time but also produces the better outcomes. Subsequently, the eminence of mFNS PROMETHEE technology is checked by comparability with the prior art. Lastly, the advantages and disadvantages of our proposed technique are supplemented to demonstrate its productivity and shortcomings.

A multi-attribute group decision-making method based on data envelopment analysis under parallel expert evaluation system

In traditional multi-attribute group decision-making (MAGDM) methods, the aggregation of expert evaluation information, the normalization of attribute scales, and the determination of attribute prior weights can lead to information loss and inconsistent results. In addition, these methods cannot identify and improve the shortcomings of non-optimal alternatives. Considering these, this paper proposes a MAGDM method based on data envelopment analysis (DEA) by setting up a parallel expert evaluation system, thus integrating the initial opinions of all experts to rank the alternatives from a global perspective. First, we construct the multi-expert evaluation process as a parallel system from the global perspective so that the decision-making process is based on the initial expert evaluation information. Then, a new directional distance function model based on endogenous projection directions is proposed to measure the alternatives under each expert subsystem, eliminating data processing and artificial weight assignment. The non-optimal alternatives can be improved to an efficient state by adjusting the attribute levels to target values. Next, we propose a DDF super-efficiency model to perform a full ranking of the alternatives. Finally, a case study is provided to demonstrate the rationality and effectiveness of the proposed method.

Building a composite index using the multi-objective approach: An application to the case of human development

The composite indicator (CI) is a powerful tool to observe the performance of various alternatives when several dimensions are in question. Normalisation, weighting and aggregation are known as the three obligatory steps to build a CI. The current paper proposes a new and unconventional method, that does not require any of these steps to construct a CI. In this method, we suggest the use of Pareto frontiers, a concept in multi-objective optimisation, to set the alternatives (e.g., countries, firms) in an order. Pareto frontiers help to collect similar alternatives together regarding their performance on several dimensions, sequentially. Each Pareto frontier represents a different achievement level. However, it is not possible to evaluate the alternatives located in the same Pareto frontier. To identify the performance of each alternative within the Pareto frontiers, we implement the ordered logit regression model by considering Pareto frontiers as the thresholds. The estimations of this model are then used to predict the latent variable that represents a specific problem. We use this methodology to re-measure human development. A comparison was carried out between the Human Development Index (HDI) and our proposed method. According to the results, the country ranking using the proposed method shows a significant difference in comparison to HDI, although there is a high Spearman’s correlation between them. The proposed method is strong and reliable because it does not require any vague selection of the so-called obligatory steps to build a CI. Moreover, it provides full non-compensatory results and effective ranking of the alternatives.

A novel integrated approach based on best–worst and VIKOR methods for green supplier selection under multi-granularity extended probabilistic linguistic environment

With consideration for the extensive resources consumption and environmental degradation being on the rise today, implementing green development strategy to pursue both socioeconomic growth and the coordinated of environment sustainability, has become an increasingly important issue in modern enterprise supply chain operations management. Hence, the appropriate green supplier selection (GSS), viewed as a core issue in green supply chain management (GSCM), requires continuous research in this field to obtain a complete perception on GSS practices. It can be regarded as a multi-attribute group decision-making (MAGDM) problem that involves many conflict and unmeasurable evaluation criteria. In view of the superiority of multi-granularity extended probabilistic linguistic term sets (MGEPLTSs) in modeling such issues on potential ambiguity, complexity and uncertainty in actual GSS practices, we propose a novel integrated MAGDM methodology for GSS problems, by integrating the BWM (best–worst method) with the VIKOR (VIšekriterijumsko KOmpromisno Rangiranje) technique under the MGEPLTSs environment. First, by introducing the multi-granularity and probabilistic linguistic term sets, the MGEPLTSs are proposed to represent and quantify the decision information of GSCM practitioners. Then, the BWM is introduced to the MGEPLTSs environment to compute the weights of decision-making panels and evaluation attributes in GSS problems, by building the fuzzy mathematical programming model, respectively. Finally, we extend a multi-granularity extended probabilistic linguistic VIKOR method to calculate the compromise measure of alternatives considering the group utility maximization and the individual regret minimization, thereby achieving the full ranking of alternatives. A GSS case is conducted to illustrate the feasibility of the proposed approach, and the sensitivity analysis and comparative analysis with other similar approaches are presented to demonstrate its effectiveness and advantages.

Differences in Researcher Rankings in Multiple Authorship–Oriented Research Fields Determined by Full Counting and Harmonic Counting

ABSTRACTThis study explored whether harmonic counting, which emphasizes the position and role of authors in the author byline, produces researcher rankings (based on the number of articles published) that differ from those produced through full counting in multiple authorship–oriented research fields. An analysis of articles published during a 10‐year period (2012–2021) by 377 chemical engineering professors or associate professors revealed a significant and positive correlation between full counting rankings and harmonic counting rankings. This finding indicates that when multiple authorship is the predominant trend for researchers within a given field, full counting can be performed to quickly determine researcher productivity rankings because it is more straightforward than harmonic counting. Future research should explore other research fields with diverse publishing trends.

A novel Group Decision Making method with the prediction selection rate

Methods on the basis of the consensus reaching process are prevalent in Group Decision Making (GDM), which typically forces some evaluators to revise initial opinions in order to reach group consensus without being able to precisely reflect original viewpoints. Furthermore, in case the correct opinion is embedded in the hand of the minority, existing methods may not reach the correct consensus. With the aim to tackle these observations, a novel approach of the Positive and Negative Prediction Selection Rate (PNPSR) is proposed on the basis of the Pythagorean Fuzzy Preference Relation (PFPR) which enables to present individuals’ opinions in a pairwise manner using the linguistic preference relation. The PFPR expressed opinions then serve as input for the computation of the proposed PNPSR, the minimum of which is subsequently selected as the correct one. Finally, the full ranking of the alternatives can be calculated through the proposed iterative algorithm. In the process, the evaluators’ original opinions are not required to modify, and the correct result can be achieved when the minority evaluators provide the correct opinions. Experimental results demonstrate the efficacy of the proposed approach in comparison with two state-of-the-art methods.

Contributions to Constructing Forced-Choice Questionnaires Using the Thurstonian IRT Model

Forced-choice questionnaires involve presenting items in blocks and asking respondents to provide a full or partial ranking of the items within each block. To prevent involuntary or voluntary response distortions, blocks are usually formed of items that possess similar levels of desirability. Assembling forced-choice blocks is not a trivial process, because in addition to desirability, both the direction and magnitude of relationships between items and the traits being measured (i.e., factor loadings) need to be carefully considered. Based on simulations and empirical studies using item pairs, we provide recommendations on how to construct item pairs matched by desirability. When all pairs contain items keyed in the same direction, score reliability is improved by maximizing within-block loading differences. Higher reliability is obtained when even a small number of pairs consist of unequally keyed items.

Assessment of national circular economy performance using super-efficiency dual data envelopment analysis and Malmquist productivity index: Case study of 27 European countries

The global commitment toward carbon neutrality and net zero emissions has substantially pressed the needs for all countries to accelerate the adoption of the circular economy paradigm. Tracking the national progress in terms of circular economy performance would provide valuable insights that could aid the effective design of improvement strategies toward sustainability. The current research proposes the combination of super-efficiency dual Data Envelopment Analysis and Malmquist productivity index to provide a full ranking and measure productivity changes in terms of circular economy of 27 European countries. The assessment involved six circular economy indicators encompassing waste generation per capita, waste intensity of the economy, recycling rate of waste in both overall and specific types of waste, i.e., packaging waste and biowaste, and circular material use rate. Our study indicates about one-half of the European countries were efficient in terms of circularity in 2018, where Netherlands, Germany, Austria, and Belgium were the front runner. The proposed approach suggests the European countries enhance their overall circular economy performance by prioritizing improvements strategies through promoting the recycling of biowaste and the circular material use rate. The MPI results over 2012–2018 indicate that Luxembourg exhibited the highest circularity advancement by 6%. Overall, the European countries have slightly enhanced their progression towards circular economy around 0.2% improvement. This suggests the European countries strengthen their policy and regulatory frameworks in support of the transition towards circular economy and encourage progressive movements in such a collaborative manner with the relevant stakeholders to build the momentum for change.

Random “Decision and Experienced Utility”, Adaptive “Consumer Memory and Choice”: The Impact of Mind Fluctuations and Cognitive Biases on Consumption and Classification

In the study of consumer behavior, we believe that a distinction should be made between the subjective mental activity of consumption and the objective process of consumption experience, and that the deviation and fluctuation of “decision utility” and the randomness of “experience utility” have essential effects on consumer behavior. For one thing, purchase often precedes experience, and consumers cannot precisely predict utility and its distribution but can only make decisions based on adaptive expectations. Secondly, there is often uncertainty in the experience of goods, making predictions more difficult. Thirdly, the revision of consumer decision utility is carried out through memory in an adaptive process. We introduce two stochastic forms of decision and experience utility functions and build a two-period model. In the model, consumers make decisions based on a pre-determined decision utility function in the first period and a randomly realized experience utility function in the second period. The analysis shows that the volatility of “decision utility” and “experience utility” affects consumers in opposite directions; the former may trigger expansionary consumption, while the latter makes consumers more cautious. Finally, the consumption behaviors in the model can be divided into 24 categories based on the dimensions of chance, systematicity, luck, and deviance, corresponding to various scenarios. The total number of consumer behavior categories is a full ranking of the size relationship of the four factors mentioned above, thus 24 categories. For example, when good luck is accompanied by chance underestimation versus systematic underestimation, it leads to a better process experience for the consumer.

An ICE aggregation method based on optimal rally points in fuzzy environment

In order to avoid the hesitation of choosing between aggressive and benevolent strategies, we propose two cross-efficiency models to get interval cross-efficiency (ICE) from the relatively neutral angle in fuzzy environment, and then propose a novel aggregation method for ICE to solve the full ranking of Decision-Making Units (DMUs). Firstly, regard the expected value of fuzzy data as the input and output of Data Envelopment Analysis (DEA) method based on fuzzy set theory. Secondly, construct the cross-efficiency models based on the fuzzy expected values from the relatively neutral angle, and generate the lower and upper bounds of ICE for all DMUs, which determines the interval cross-efficiency matrix (ICEM). Thirdly, project all ICE onto the plane as points, then seek the optimal rally point for each DMU based on ICEM as the comprehensive ICE. Fourthly, rank the comprehensive ICE to obtain the complete ranking of DMUs by using the optimal number sorting method. Finally, the proposed model is applied to the evaluation of manufacturing enterprises, and the results are compared with different models to prove its effectiveness.

The scientific publication score - a new tool for summarizing evidence and data quality criteria of biomedical publications.

The number of biomedical research articles increases by over 2.5million publications each year, making it difficult to stay up to date. In this study, we introduce a standardized search and evaluation tool to combat this issue. Employing crowdsourcing, a large database of publications is gathered. Using a standardized data entry format, coined the "scientific publication score" (SPS), specific publication results can be easily aggregated, thereby allowing fast and accurate comparisons for clinical questions. The SPS combines two quality dimensions. The first captures the quality of evidence of the study using the evidence criteria defined by the Centre for Evidence-Based Medicine, Oxford, UK. The second is more fine-grained and considers the magnitude of statistical analyses on individual and specific results. From 2014 to 2019, experts of the European Bone and Joint Infection Society (EBJIS) were asked to enter data of relevant publications about prosthetic joint infection. Data and evidence levels of specific results were averaged, summarized and ranked. A total of 366 publications were divided into two groups: (I)risk factors (e.g.,host-related factors, pre- and postoperative issues) with 243 publications and (II)diagnostic methods (e.g.,laboratory tests, imaging methods) with 123 publications. After ranking, the highest score for risk factors of prosthetic joint infection were calculated by the SPS for anemia (mean SD0.91), malignancy (mean SD0.29) and previous alloarthroplasty (mean SD0.35). A comparison of the full SPS ranking with the ranking determined at the 2018 International Consensus Meeting (ICM) on Musculoskeletal Infection resulted in a Spearman rank correlation coefficient of 0.48 and a value of 0.0382. The diagnostic methods ranked highest by the SPS were aspirate leucocyte count (mean SD1.21), interleukin6 (mean SD1.07) and aspirate (neutrophils over 80 %) (mean SD0.63). The comparison to the ICM ranking yielded a Spearman rank correlation coefficient of 0.91 and a value of 0.0015. Our pilot study evaluated a new tool for the quality assessment of specific results based on the quality of the source publication. The SPS is suitable for a ranking of specific results by evidence and data quality criteria important for systematic reviews.

Gender Disparities in Academic Pain Medicine: A Retrospective, Cross-Sectional Bibliometric Analysis.

This study was conducted to characterizethe gender disparities within academic pain management departments in the United States, specifically focusing on its relation to research andacademic leadership. This will allow for targeted improvements in efforts made to reduce gender gaps within academic pain medicine. This is a retrospective, cross-sectional analysis study evaluating pain management faculty of various positions at academicinstitutionsacrossthe United States. Weutilizedpublicly available data on faculty positions and sex to analyze research impact, H-index, number of publications and citations through bibliometric and linear regression analysis. Our analysis found thatfemalefaculty hadsignificantlyless research output to male faculty. The three research measurement indices used in this study including H-index, number of publications, and number of citations were significantly lower in females than in males among associate and full professor faculty ranking. Multivariable analysis did not display any significant disparities of research output at the division director and department chair level. As in many areas of medicine, there continues to be a significant gender disparity in academic pain management departments, particularly with regard to leadership positions and research impact within the field.Our study found that female pain physicians had a significantly less research output based on the three variables of H indices, number of publications, and number of citations compared to their male counterparts. This has been shown to have the impact on discrepancies in female faculty ranking. Interestingly, these variables were not significantly different between male and female faculty members of the same level of leadership except for program director. There are various contributory reasons for these disparities, including implicit biases, lack of mentorship, and familial obligations. Addressing some of these factors can help narrow theschism and promote greater gender equality within academic pain management.

Competitiveness in Formula One

We define a measure of competitiveness that is based on full rankings and corrects for partitioning of the parties in exogenously defined clusters. We use the measure to study the change in competitiveness of Formula One racing during the period 1993–2019.

The Integration of Efficacy and Safety Outcomes Identifies the Net Clinical Benefit of Targeted Agents in Upfront Therapy of Chronic Lymphocytic Leukemia: Results of Network Meta-Analysis

In the absence of head-to-head randomized trials, indirect comparisons of therapy with targeted agents (TAs) across trials may well be informative in the management of chronic lymphocytic leukemia (CLL). However, such published analyses have mostly described progression-free survival (PFS) and provided sparse data on overall survival (OS) or toxicity. We, therefore, conducted a network meta-analysis (NMA) of published upfront CLL TA studies which incorporated a variety of efficacy and safety outcomes, in an attempt to predict the net benefit of TAs. We searched MEDLINE, EMBASE, and Cochrane databases for randomized controlled trials (RCTs) assessing the efficacy and safety of Bruton kinase inhibitors (BTKis) and venetoclax-based regimens in treatment-naïve CLL. Efficacy outcomes (PFS, OS, time to next treatment [TTNT], overall response rate [ORR], complete remission [CR], minimal residual disease [MRD]), safety, and toxicity were evaluated. The surface under the cumulative ranking curve (SUCRA) was obtained for each treatment. The SUCRA provides a numeric representation (range from 0 to 100%), allowing for an overall ranking. The closer the SUCRA value to 100%, the higher the likelihood that therapy is in the top rank, whereas the closer to 0 the SUCRA value, the more likely treatment is in the bottom rank. Nine RCTs met the criteria of low risk for bias according to the Cochrane Handbook for Systematic Reviews of Interventions. Five were excluded because they lacked a common comparator arm (RESONATE2, ALLIANCE, ECOG-ACRIN, FLAIR, SEQUOIA). Four trials were suitable for network analysis (ILLUMINATE, ELEVATE-TN, CLL14, GLOW). Chlorambucil-obinutuzumab (CO) was the control arm across these studies (n=610). In all, these included 1547 patients and evaluated ibrutinib-obinutuzumab (IO) (ILLUMINATE; n=113), venetoclax-obinutuzumab (VO) (CLL14; n=216), acalabrutinib (A) monotherapy (ELEVATE-TN; n=179), A plus obinutuzumab (AO)(ELEVATE-TN; n=179) ibrutinib-venetoclax (IV)(GLOW; n=195). In this NMA, we used the longest published follow-up for each trial (GLOW 27.7 months, CLL14 52,4 months, ILLUMINATE 45 months, ELEVATE-TN 46.9 months). Patients with disrupted TP53 were excluded from the GLOW study and hence from our analysis. AO had the highest probability of efficacy in terms of PFS (SUCRA,0.9923), OS (SUCRA, 0.9010), and ORR (SUCRA,0.8670). In contrast, venetoclax-based regimens had the highest likelihood of achieving a better quality clinical and MRD responses. IV ranked first in terms of CR (SUCRA 0.9208) and VO in terms of undetectable MRD (SUCRA,0.9020). In the analysis of TTNT, IO was preferable (SUCRA,0.9810)(Tab 1). We developed a ranking chart for efficacy outcomes. The average (Av) SUCRA for PFS, OS, ORR, and CR was used to create an efficacy rankogram (k ranks ranged from 1 to 6). In the aggregate efficacy analysis, AO ranked first (Av SUCRA,0.8544) and CO last (Av SUCRA, 0.0575). The full Av SUCRA ranking order was AO, IV, VO, IO, A, CO (Av SUCRA, 0.8544, 0.5793,0.5747,0.4456,0.3864, 0.0575) (Fig 1). When outcomes of toxicity were included, the ranking order changed. The average SUCRA values for toxicities (assessed by the common terminology criteria adverse events (CTCAE) as grade 3 and above, which included neutropenia, infections, diarrhoea, and atrial fibrillation facilitated the development of a safety rankogram in which reduced toxicity is associated with an increased SUCRA value. The toxicity SUCRA ranking selected A first. The full toxicity SUCRA ranking is as follows : A, CO, AO, IV VO and IO (Av SUCRA; 0.70160.6650,0.4390,0.4377, 0.4325 and 0.3242)(Fig 2). A mixed efficacy-toxicity (E-T) rankogram was generated from the efficacy and toxicity Av SUCRA scores; this showed that AO ranked first followed by A, CO, IV, VO, and IO (ET-Av SUCRA: 0.6467, 0.5440,0.5247,0.5085,0.5035, 0.3850)(Fig 3). This is the first NMA that can aid the selection of upfront therapy in CLL patients with intact TP53. In patients who are physically fit a model based solely upon efficacy may be able to inform treatment decisions.On the other hand , a model based exclusively on toxicity outcomes may guide therapy selection in unfit patients. If a combined efficacy/toxicity model is utilized , it identifies a preference for acalabrutinib-based regimens. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

A decision‐making support system module for customer segmentation and ranking

AbstractMany businesses have their own method of segmentation and of customer evaluation to help them give the appropriate managerial attention to each segment and each customer. This paper proposes a more refined and objective decision‐making support system module that allows segmentation and full ranking of customers. It offers several improvements and advantages over the state‐of‐the‐art methods. The module is based on customer criteria with quantitative values that can be extracted from the organizational information system. Customer scores are calculated objectively on the basis of measurable criteria without the need for human evaluation. The module determines the relative location of each customer within that customer's segment (for example, Platinum, Gold, Silver, and Bronze), tracks changes that occur over time, and enables a full and precise ranking of the customers according to company‐defined criteria. The module can be computerized and results can be generated quickly at any time, using up‐to‐date data. The module's design was based on feedback from a survey conducted among 39 managers and its applicability was successfully demonstrated in a real‐world case study. The decision‐makers in the organization where the case study was conducted stated that they would apply the proposed method quarterly (four times a year) instead of once a year. It was also found that the proposed method saves about 90% of the time and resources required to prepare a customer portfolio management and customer ranking compared to the subjective method that was used.

A prediction model for ranking branch-and-bound procedures for the resource-constrained project scheduling problem

The branch-and-bound (B&B) procedure is one of the most widely used techniques to get optimal solutions for the resource-constrained project scheduling problem (RCPSP). Recently, various components from the literature have been assembled by Coelho and Vanhoucke (2018) into a unified search algorithm using the best performing lower bounds, branching schemes, search strategies, and dominance rules. However, due to the high computational time, this procedure is only suitable to solve small to medium-sized problems. Moreover, despite its relatively good performance, not much is known about which components perform best, and how these components should be combined into a procedure to maximize chances to solve the problem. This paper introduces a structured prediction approach to rank various combinations of components (configurations) of the integrated B&B procedure. More specifically, two regression methods are used to map project indicators to a full ranking of configurations. The objective is to provide preference information about the quality of different configurations to obtain the best possible solution. Using such models, the ranking of all configurations can be predicted, and these predictions are then used to get the best possible solution for a new project with known network and resource values. A computational experiment is conducted to verify the performance of this novel approach. Furthermore, the models are tested for 48 different configurations, and their robustness is investigated on datasets with different numbers of activities. The results show that the two models are very competitive, and both can generate significantly better results than any single-best configuration.

SGSNet: A Lightweight Depth Completion Network Based on Secondary Guidance and Spatial Fusion.

The depth completion task aims to generate a dense depth map from a sparse depth map and the corresponding RGB image. As a data preprocessing task, obtaining denser depth maps without affecting the real-time performance of downstream tasks is the challenge. In this paper, we propose a lightweight depth completion network based on secondary guidance and spatial fusion named SGSNet. We design the image feature extraction module to better extract features from different scales between and within layers in parallel and to generate guidance features. Then, SGSNet uses the secondary guidance to complete the depth completion. The first guidance uses the lightweight guidance module to quickly guide LiDAR feature extraction with the texture features of RGB images. The second guidance uses the depth information completion module for sparse depth map feature completion and inputs it into the DA-CSPN++ module to complete the dense depth map re-guidance. By using a lightweight bootstrap module, the overall network runs ten times faster than the baseline. The overall network is relatively lightweight, up to thirty frames, which is sufficient to meet the speed needs of large SLAM and three-dimensional reconstruction for sensor data extraction. At the time of submission, the accuracy of the algorithm in SGSNet ranked first in the KITTI ranking of lightweight depth completion methods. It was 37.5% faster than the top published algorithms in the rank and was second in the full ranking.

Accelerated MM Algorithms for Inference of Ranking Scores from Comparison Data

Accelerated Algorithms for RankingAssigning ranking scores to items based on observed comparison data (e.g., paired comparisons, choice, and full ranking outcomes) has been of continued interest in a wide range of applications, including information search, aggregation of social opinions, electronic commerce, online gaming platforms, and more recently, evaluation of machine learning algorithms. The key problem is to compute ranking scores, which are of interest for quantifying the strength of skills, relevancies, or preferences, and prediction of ranking outcomes. One of the most popular statistical models of ranking outcomes is the Bradley–Terry model for paired comparisons and its extensions to choice and full ranking outcomes. In “Accelerated MM Algorithms for Inference of Ranking Scores from Comparison Data,” M. Vojnovic, S.-Y. Yun, and K. Zhou show that a popular MM algorithm for inference of ranking scores for generalized Bradley–Terry ranking models suffers a slow convergence issue, and they propose a new accelerated algorithm that resolves this shortcoming and can yield substantial convergence speedups.

A regret cross-efficiency ranking method considering consensus consistency

Data envelopment analysis (DEA) is a powerful nonparametric approach for assessing the performance of decision making units (DMUs). Traditional DEA models usually utilize a self-evaluation mode to evaluate DMUs, which have the following shortcomings such as efficiency overestimation and incomplete ranking. As the extension of traditional DEA, cross-efficiency (CE) evaluation remedies these limitations. It connects self-evaluation with peer-evaluation to appraise DMUs and rank them. However, most existing studies on CE evaluation utilize the arithmetic average method to aggregate self-evaluation efficiency and peer-evaluation efficiency, while ignoring the significance of self-efficiency and the irrational psychology of decision-makers (DMs). Observed by the aforementioned phenomena, this paper employs regret theory to portray the regret aversion behavior of DMs and further proposes a novel regret cross-efficiency aggregation (RCEA) method. Considering aggressive and benevolent cross-efficiency models, we apply the RCEA method to calculate the regret-based aggressive cross-efficiency and the regret-based benevolent cross-efficiency, and introduce a parameter reflecting the psychological preference of DMs to aggregate them. To attain consensus between DMUs’ expectations for efficiency and the ultimate integration results, we design a regret cross-efficiency adjustment algorithm. This algorithm provides a more acceptable evaluation result and derives a full ranking for all DMUs. Finally, the validity and feasibility of the proposed model are illustrated by evaluating the efficiency of scientific research activities in 13 universities in China.

The iterative deferred acceptance mechanism

Lately, there has been an increase in the use of sequential mechanisms, instead of the traditional direct counterparts, in college admissions in many countries, including Germany, Brazil, and China. We describe these mechanisms and identify their shortcomings in terms of incentives and outcome properties. We introduce a new mechanism, which improves upon these shortcomings. Unlike direct mechanisms, which ask students for a full preference ranking over colleges, our mechanism asks students to sequentially make choices or submit partial rankings from sets of colleges. These are used to produce a tentative allocation at each step. If at some point it is determined that a student can no longer be accepted into previous choice, then she is asked to make another choice among colleges that would tentatively accept her. Participants following the simple strategy of choosing the most-preferred college in each step is an ex-post equilibrium that yields the Student-Optimal Stable Matching.