Preference Relations Research Articles

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.

Accuracy and Preferences for Legal Error

Abstract Legal procedures used to determine liability trade-off type-1 errors (e.g., false convictions) against type-2 errors (e.g., false acquittals). After noting that people’s relative preferences for type-1 errors (compared to type-2 errors) appear to be negatively correlated with technological advancements, we study how the accuracy of evidence collection methods may affect the trade-off between these two errors. Counter-intuitively, we find that under some conditions, greater accuracy may result in a higher probability of type-1 error (or type-2 error) maximizing deterrence. Then, assuming both errors are decreasing in accuracy, we characterize the type-1 error that emerges under electoral pressures (when the median voter’s preferences are implemented): convictions occur more often than is socially optimal but less often than is necessary to maximize deterrence. Moreover, as the harm from crime increases, the median voter becomes less tolerant of type-1 errors as the legal system’s accuracy increases. We also show that, because the median voter is less averse toward type-1 errors than the average citizen, an increase in accuracy may reduce welfare.

Interval-valued intuitionistic fuzzy best-worst method with additive consistency

Best-worst method (BWM) has received extensive attention because of its fewer pairwise comparisons and maintenance of consistency. Extending it to various fuzzy environments has become a hot research spot. This paper initiates a new interval-valued intuitionistic fuzzy (IVIF) BWM with additive consistency. Firstly, an IVIF fuzzy preference relation (IVIFPR) is viewed as a directed graph with IVIF weights. An approach is offered to distinguish the best and worst criteria by the defined score out-degree and accuracy out-degree of criteria. The concept of IVIF reference comparisons is introduced. The additive consistency of IVIF reference comparisons is acquired by that of IVIFPR using its left and right intuitionistic fuzzy preference relations. We derive the relationship between the IVIF reference comparisons and the IVIF priority weight vector. Based on this relationship, a linear goal programming model is constructed to generate the IVIF priority weights. Two consistency indexes are analytically obtained and the consistency ratio is then defined to check the consistency of IVIF reference comparisons. For the unacceptably additive consistency, a new approach is put forward to enhance the consistency. Thereby, the proposed IVIF BWM is summarized step by step. Three examples of applications are furnished to demonstrate its effectiveness and validation.

Nonmonotonic Modulation of the Protein-Ligand Recognition Event by Inert Crowders.

The ubiquitous event of a protein recognizing small molecules or ligands at its native binding site is crucial for initiating major biological processes. However, how a crowded environment, as is typically represented by a cellular interior, would modulate the protein-ligand search process is largely debated. Excluded volume-based theory suggests that the presence of an inert crowder would reinforce a steady stabilization and enhancement of the protein-ligand recognition process. Here, we counter this long-held perspective via the molecular dynamics simulation and Markov state model of the protein-ligand recognition event in the presence of inert crowders. Specifically, we demonstrate that, depending on concentration, even purely inert crowders can exert a nonmonotonic effect via either stabilizing or destabilizing the protein-ligand binding event. Analysis of the kinetic network of binding pathways reveals that the crowders would either modulate precedent non-native on-pathway intermediates or would devise additional ones in a multistate recognition event across a wide range of concentrations. As an important insight, crowders gradually shift the relative transitional preference of these intermediates toward a native-bound state, with ligand residence time at the binding pocket dictating the trend of nonmonotonic concentration dependence by simple inert crowders.

Q-rung orthopair hesitant fuzzy preference relations and its group decision-making application

To express the opinions of decision-makers, q-rung orthopair hesitant fuzzy sets (q-ROHFSs) have been employed extensively. Therefore, it is necessary to construct q-rung orthopair hesitant fuzzy preference relations (q-ROHFPRs) as a crucial decision-making tool for decision-makers. The goal of this paper aims to define a new consistency and consensus approach for solving q-ROHFPR group decision-making (GDM) problems. To do this, we first state the definitions of q-ROHFPRs and additive consistent q-ROHFPRs based on q-ROHFSs, an additive consistency index and acceptable additive consistent q-ROHFPRs. Second, based on minimizing the deviation, we establish an acceptable goal programming model for unacceptable additive consistent q-ROHFPRs. Third, an iterative algorithm is created for achieving acceptable consistency and reaching a rational consensus. The degree of rational consensus among individual q-ROHFPRs is quantified by a distance-based consensus index. Afterward, a non-linear programming model is formulated to derive the priority vector of alternatives, which are q-rung orthopair hesitant fuzzy numbers (q-ROHFNs). Based on this model, a GDM model for q-ROHFPRs is then developed. To demonstrate the validity and utility of the proposed GDM model, a case study on the risk assessment of hypertension is provided. The finding of sensitivity and comparison analyses supports the feasibility and efficacy of the suggested approach.

An indirect multicriteria group decision-making method with heterogeneous preference relations and reliabilities of decision-makers

This study proposes a new indirect method to solve multicriteria group decision-making (MCGDM) problems with heterogeneous preference relations and decision-makers’ reliabilities. To unify the heterogeneous preference relations, the transformation of multiplicative preference relations (MPRs), fuzzy preference relations (FPRs), intuitionistic fuzzy preference relations (IFPRs), and linguistic preference relations (LPRs) into distributed preference relations (DPRs) is analyzed. As consistency among DPRs is constructed under the assumption that decision-makers are bounded rationality, the abstract transformation of MPR, FPR, IFPR, and LPR into DPR is defined, and its reality depends on the preferences of decision-makers. Two important properties of abstract transformation, namely, internal consistency and restricted max-max transitivity, were theoretically verified. The reliabilities of decision-makers are involved in MCGDM by estimating the reliability of each criterion to improve solution quality. Based on the four types of abstract transformations, a process for analyzing MCGDM problems with heterogeneous preference relations and decision-makers’ reliabilities is developed. The proposed method is used to analyze a new business selection problem for an enterprise located in Changzhou, Jiangsu, China to demonstrate its applicability and validity.

The interplay between perceived busyness and power distance belief on subjective social status: Consequences for consumer preferences for material versus experiential purchases

AbstractThe perception of a busy life as an indicator of self‐worth varies among individuals. Postulating that this perceptual difference can be attributed to one's attitude toward social stratification, the current research examines the interplay between perceived busyness and power distance belief (PDB). We propose that perceived busyness dictates a consumer's subjective social status under low PDB but not under high PDB. Furthermore, the interaction between perceived busyness and PDB determines an individual's relative preference for purchase types: material versus experiential purchases. Study 1 verifies that consumers' perceived busyness has a positive effect on their evaluation of their social status when they have low PDB, whereas this effect disappears when they have high PDB. Study 2 tests the asymmetric effect of perceived busyness and PDB on preference for purchase types, and Study 3 further provides evidence for the mediating role of subjective social status.

Contextuality in Collective Intelligence: Not There Yet.

Type I contextuality or inconsistent connectedness is a fundamental feature of both the classical as well as the quantum realms. Type II contextuality (true contextuality or CHSH-type contextuality) is frequently asserted to be specific to the quantum realm. Nevertheless, evidence for Type II contextuality in classical settings is slowly emerging (at least in the psychological realm). Sign intransitivity can be observed in preference relations in the setting of decision making and so intransitivity in decision making may also yield examples of Type II contextuality. Previously, it was suggested that a fruitful setting in which to search for such contextuality is that of decision making by collective intelligence systems. An experiment was conducted by using a detailed simulation of nest emigration by workers of the ant Temnothorax albipennis. In spite of the intransitivity, these simulated colonies came close to but failed to violate Dzhafarov's inequality for a 4-cyclic system. Further research using more sophisticated simulations and experimental paradigms is required.

Hierarchical Q-learning-enabled neutrosophic AHP scheme in candidate relay set size adaption in vehicular networks

Cooperative candidate relay sets (CRS) in opportunistic broadcast paradigms can significantly improve the performance efficiency of traditional broadcast schemes by simultaneously scheduling multiple vehicles in the data packet transmission process. Nonetheless, multiple relay synchronization may cause enormous data redundancy and high delays in data delivery, resulting in a high cooperative transmission cost. High vehicle velocities and vehicular topology restrictions, including intersections, exacerbate the cooperative transmission cost. Motivated by a trade-off between delay and delivery ratio via reducing duplicate data packets and redundant relays, we address this gap in this paper. To this end, we propose an adaptive candidate relay set (ACRS) size scheme based on the Neutrosophic Set Analytic Hierarchy Process (NS-AHP) technique assisted by a Q-learning algorithm. The NS-AHP pair-wise comparison matrix includes; redundancy, cooperative-based efficiency, and relay set stability as the decision-making attributes. Meanwhile, we use a hierarchical Q-learning approach based on the time-varying vehicular environment to adjust the relative preference of the attributes and alternatives (i.e., various CRS sizes) over each other. To optimally learn the relative preference values of the attributes and alternatives over each other, reward functions are set dynamically at both levels. Comprehensive simulations via NS-2 and SUMO are carried out to assess the efficacy of our ACRS scheme compared to three commonly used baseline techniques of fixed size, threshold-based, and probability-based techniques. Overall, the average yields obtained from the experimental outcomes in the urban and highway scenarios show that the ACRS scheme, on average, improves the packet delivery ratio by 3.77%, 5.36%, and 1.35%; reduces the single-hop delay by 34.78%, 42.085%, and 19.39%; reduces the end-to-end delay by 34.76%, 42.72%, and 21.35%; improves the transmission throughput by 34.24%, 41.54%, and 19.305%; and reduces the redundancy ratio by 43.69%, 50.66%, and 24.32% compared to the fixed size, threshold-based, and probability-based techniques, respectively.

Group decision making with incomplete interval-valued linguistic intuitionistic fuzzy preference relations

With the increasing complexity and indeterminacy intensity of the decision making environment, there is a growing lack of widespread utility of traditional preference representations. As a practical yet valid preference expression, interval-valued linguistic intuitionistic fuzzy preference relations (IVLIFPRs) can efficiently represent the pairwise cognitions qualitatively and provide the inclusive support. This study aims at forming a novel group decision making (GDM) method based on the consistency and consensus preference analysis for IVLIFPRs. First, the new concepts of additive consistency and acceptably additive consistency for IVLIFPRs are defined. Based on these definitions, two optimization models are constructed to obtain the incomplete information and acceptably additive consistent IVLIFPRs, respectively. Then, inspired by the Markov model, the derivation approach of DMs' weights is designed. Afterward, the consensus improving process is constructed under the premise of acceptable consistency and smallest information distortion. Accordingly, the normalized priority weights of alternatives are further determined. Thus, a mechanism for the GDM method with incomplete and unacceptably consistent IVLIFPRs is developed. Finally, an application and comparison analysis show that the proposed method comprehensively considers the key steps in the GDM process, so that the logical rationality and technical superiority are well guaranteed.

Consensus reaching process for group decision-making based on trust network and ordinal consensus measure

Due to the urgent nature of emergency decision making, it is necessary to reach the consensus requirement quickly. Ordinal consensus measure explores the relation between the rankings and helps to intuitively know which alternative needs to be adjusted to accelerate the improvement of consensus. Moreover, decision makers (DMs) in the decision making problem are often connected through trust relationships which affect the DMs’ judgments in the process of DMs’ interaction. Therefore, this paper explores trust network-based group decision-making in which the consensus level is estimated by an ordinal consensus measure. We first focus on the supplementation of an incomplete trust network. One of the most common methods is to design the trust propagation operator, whereas the intensity of information propagation may be different in various scenarios. Therefore, considering the different numerical scale of the linguistic term set, a trust propagation operator with different intensity of trust propagation is designed to obtain the indirect trust relationship. In the process of supplementing the incomplete trust network, the contribution of DMs to propagating information is concerned, which can be described by the betweenness centrality, and the importance weights of DMs are determined by combining the betweenness centrality and trust in-degree. In the consensus reaching process, we first propose an improved ordinal consensus measure, which takes into account the consistency of orders of the same alternative in different rankings as well as the importance of positions of alternatives. Then, we design the identification rule and the feedback mechanism for those with low consensus levels. The identification rule is used to select the DMs which first few alternatives in the ranking are different with those in the ranking of group. And in the feedback mechanism, the referenced preference relation (FPR) obtained by the trust network is provided for the identified DMs. Afterwards, combining the referenced FPR, an optimization model is designed to give the adjustment opinion. Finally, a numerical example elaborates on the feasibility of the trust propagation operator and consensus model. The comparative analysis demonstrates the rationality and effectiveness of the proposed model.

Optimal convoy composition for virtual coupling trains at junctions: A coalition formation game approach

This paper presents a virtual coupling (VC) train convoy composition optimization framework to create a multiple formation structure maximizing the capacity benefit while considering transportation requirements and system limitations. First, the operational constraints of VC trains in the convoy formation process are mapped as headway time transitions. A coordination strategy is proposed by utilizing the natural deceleration process at junctions to minimize the negative effects of the coupling process. Multiple composition structures are created by forming varied potential convoys that adhere to these constraints and transportation schedules. Second, a utility function for convoy composition is modeled using the coalition formation game theory to quantify the operational efficiency of VC trains passing through bottleneck sections. It reveals that coupling time and convoy structure are interdependent factors that jointly impact the capacity benefits. Thirdly, a train convoy formation algorithm is proposed to determine the optimal convoy composition by comparing formation structures, designing preference relations, and estimating lower bounds. This algorithm guarantees the convergence of the optimal structure result. Finally, the proposed framework is evaluated on four typical rail transit market segments and transportation organization scenarios. The application results demonstrate that the optimized convoy structures increase capacity benefits in the four typical market segments by 26.1%, 30.7%, 24.7%, and 18.9%, respectively. VC improves the capacity of the bottleneck sections by increasing the operating density. The present study lays a theoretical foundation for strategic-level applications of virtual coupling.

AsQM: Audio Streaming Quality Metric Based on Network Impairments and User Preferences

Audio streaming services have many users because the proliferation of cloud-based audio streaming services for different content. The complex networks that support these services do not always guarantee an acceptable quality on the end-user side. In this paper, the impact of temporal interruptions on the audio streaming reproduction and the user’s preference in relation to audio content are studied. In order to determine the key parameters in the audio streaming service, subjective tests are conducted, and their results show that a user’s quality-of-experience (QoE) is highly correlated with the following application parameters: the number of temporal interruptions or stalls, its frequency and length, and the temporal location where they occur. However, most important, the experimental results demonstrate that users’ preference for audio content plays an important role in users’ QoE. Thus, a preference factor (PF) function is defined and considered in the formulation of the proposed metric named audio streaming quality metric (AsQM). Considering that multimedia service providers are based on web servers, a framework to obtain user information is proposed. Furthermore, the results show that the AsQM implemented in the audio player of an end user’s device has a low impact on energy, processing and memory consumption.

A multi-measure feature selection method for decision systems with preference relation

Feature selection focuses on selecting important features that can improve the accuracy and simplification of the learning model. Nevertheless, for the ordered data in many real-world applications, most of the existing feature selection algorithms take the single-measure into consideration when selecting candidate features, which may affect the classification performance. Based on the insights obtained, a multi-measure feature selection algorithm is developed for ordered data, which not only considers the certain information by the dominance-based dependence, but also uses the discern information provided by the dominance-based information granularity. Extensive experiments are performed to evaluate the performance of the proposed algorithm on UCI data sets in terms of the number of selected feature subset and classification accuracy. The experimental results demonstrate that the proposed algorithm not only can find the relevant feature subset but also the classification performance is better than, or comparably well to other feature selection algorithms.

A Task-Driven Sequential Overlapping Coalition Formation Game for Resource Allocation in Heterogeneous UAV Networks

A heterogeneous unmanned aerial vehicle (UAV) network where UAVs carrying different resources form coalition and cooperatively carry out tasks is of crucial importance for fulfilling diverse tasks. However, the existing coalition formation (CF) game model only optimizes the composition of UAVs in a single coalition, which results in disjoined coalitions. In order to tackle this issue, a sequential overlapping coalition formation (OCF) game is proposed by considering the overlapping and complementary relations of resource properties and the task execution order. Moreover, different from the traditional Pareto and Selfish orders, a bilateral mutual benefit transfer (BMBT) order is proposed to optimize the cooperative task resource allocation through partial cooperation among overlapping coalition members. Furthermore, using the preference relation between UAVs carrying resources and tasks requiring the same type of resource, a preference gravity-guided Tabu Search (PGG-TS) algorithm is developed to obtain a stable coalitional structure. Numerical results verify that the utility of the proposed OCF game scheme based on the PGG-TS algorithm increases by 18% against that of the non-overlapping CF game scheme, and the utility of the proposed BMBT order increases by 25%, compared with other orders.

A Localized High-Fidelity-Dominance-Based Many-Objective Evolutionary Algorithm

The practicality of Pareto-dominance in solving many-objective optimization problems becomes questionable due to its inability to factor the critical Human Decision-making (HDM) elements, including, the number of better objectives, the degree of betterment in objectives, and objectives’ relative preference. Relevant dominance principles are recently proposed to incorporate the first two HDM elements, often with the need for new tunable parameters. This paper proposes a high-fidelity-dominance principle, that factors all the three HDM elements, explicitly and simultaneously, and without requiring tuning of any parameter. This principle has been implemented in a reference vector based framework, leading to a computationally efficient many-objective Evolutionary Algorithm (MaOEA), namely localized high-fidelity-dominance based EA (LHFiD). Critically, LHFiD also has an inbuilt mechanism for on-the-fly determination of the timing for: (a) intermittent nadir point estimation that enables faster convergence, and (b) its self-termination that bears practically utility. This paper is based on an extensive study involving 41,912 experiments, in which the proposed LHFiD approach is compared with existing competitive MaOEAs. The paper reports statistically better performance in about 60% instances, making it practical and worthy of further investigation and application.

Hybrid Active-Passive Shielding of Magnetic Field of Overhead Power Lines with Triangular Phase Conductors Arrangements

The aim of the work is to design a robust hybrid active-passive magnetic field shielding system created by overhead power lines with a triangular arrangement of phase wires, designed to increase the shielding efficiency of the initial magnetic field in residential premises to the level of sanitary standards and reduce the system's sensitivity to changes in system parameters. To achieve this goal, the following tasks were solved: the structure of a hybrid system of active-passive shielding was developed, and the synthesis of a system consisting of a double-circuit active and multi-circuit passive parts was performed. The synthesis of the system is reduced to solving a vector game, in which the vector payoff is calculated based on the solutions of the Maxwell equations in the quasi-stationary approximation using the COMSOL Multiphysics software. The solution of a multicriteria game is found on the basis of algorithms for optimizing a multi-swarm of particles from Pareto optimal solutions, taking into account binary preference relations. The most important results are theoretical and experimental studies of the effectiveness of the synthesized hybrid active-passive shielding system for the magnetic field created by overhead power lines with a triangular arrangement of phase wires in the shielding zone. The significance of the results obtained lies in the fact that in the course of designing a hybrid active and multiloop passive screens, the parameters of passive and active screens were calculated; as well as practical recommendations are given on the reasonable choice of the spatial arrangement of a multi-loop passive shield and two shielding windings of a robust system of hybrid shielding of the magnetic field created by overhead power lines with a triangular arrangement of phase wires.

The Implementation of Effective Online Asynchronous Corrective Feedback on the Oral Production of English as a Foreign Language Learners in University Virtual Speaking Classes

This article had as main aim that of identifying the effectiveness of asynchronous corrective feedback on the oral production of university students taking an online integrated English course as part of their program. All research instruments and tested corrective feedback method were applied in the speaking section of the course that had a duration of sixteen weeks. The study followed a mixed-methods design, where qualitative data was gathered by means of a leaners’ speech analysis scale, while quantitative data was gathered through a questionnaire addressing language learners´ preferences in relation to corrective feedback in oral English classes. A first oral test and final oral test were also used as pre-test and post-test to measure the level of improvement after the intervention phase. Opposed to Nunan’s (1992) position towards the use of positive feedback instead of corrective feedback, the present research project was underlaid by the conception that teachers’ reactions and reflections on learners’ deviant forms of the target language are a crucial part of the whole language learning spectrum. Very interesting findings were shed, first, for the two main types of errors analyzed in the study (Grammar and Vocabulary, and Pronunciation), there was a diminution in quantity when comparing the results from the first exam to those of the final exam, representing those learners committed 10% less errors in the final oral exam. Unsurprisingly, students unanimously favored the use of corrective feedback in their language classes. However, preferences on synchronous and asynchronous corrective feedback showed no major difference.

Abstract argumentation with conditional preferences

In this paper, we study conditional preferences in abstract argumentation by introducing a new generalization of Dung-style argumentation frameworks (AFs) called Conditional Preference-based AFs (CPAFs). Each subset of arguments in a CPAF can be associated with its own preference relation. This generalizes existing approaches for preference-handling in abstract argumentation, and allows us to reason about conditional preferences in a general way. We conduct a principle-based analysis of CPAFs and compare them to related generalizations of AFs. Specifically, we highlight similarities and differences to Modgil’s Extended AFs and show that our formalism can capture Value-based AFs. Moreover, we show that in some cases the introduction of conditional preferences leads to an increase in computational complexity.

Coming to Consensus on Classification in Flexible Linguistic Preference Relations: The Role of Personalized Individual Semantics

Coming to Consensus on Classification in Flexible Linguistic Preference Relations: The Role of Personalized Individual Semantics