Preference Relations Research Articles

A preference group consensus method with three-way decisions and regret theory under multi-scale information systems

The foundation of intelligent computing and expert systems relies on the data processing conducted within the realm of multi-scale information systems (MSISs). Data processing within MSISs caters to diverse analytical needs, where the preference hierarchy among various factors plays a vital role in the consensus reaching process (CRP). Additionally, the three-way decisions (TWD) theory serves as an efficient tool, while regret theory (RT) quantifies decision-makers' risk inclinations associated with various psychological behaviors. Thus, the paper aims to introduce an innovative approach known as the CRP-TWD-RT-MSIS method. The study draws inspiration from spatial geometry, incorporating concepts like points, lines, surfaces, and bodies to create MSISs. This process generates a matrix of fuzzy preference relations (FPRs) among distinct decision-makers through data preprocessing. In addition, the paper explores feedback mechanisms based on identification and directional rules, as well as those rooted in minimal adjustments or cost considerations. Simultaneously, it takes negotiation and discussion time into account, culminating in the development of a personalized adjustment optimization model that focuses on minimizing time costs. In the end, the method's effectiveness and superiority are validated through a case study and a comparative analysis of real data from the Chinese Weather and Meteorological Bureau's website.

Analysis of obstacles to lowering demurrage at grain terminals in South Korea

This study aimed to analyze the obstacles to lowering demurrage at a grain terminal. After conducting in-depth interviews with working-level staff in grain terminal, 12 factors were selected to derive key factors for reducing demurrage. The Consistent Fuzzy Preference Relations methodology, which can extract experts’ knowledge and experiences, was employed to rank the obstacles in lowering demurrage. The analysis showed that the number one factor was “inventory requirements of grain shippers” (with an importance of 0.097), followed by “capacity of storage facilities” and “port congestion” as the second and third factors, respectively. As for sub-factors, “climate (e.g., typhoon, rain, etc.)” and “ship size” were ranked 11th and 12th, respectively. The analysis indicates that experts recognized factors that can be changed by shippers and that are difficult to control as more important obstacles than cost-related factors. The findings of this study provide the working-staff of ports, grain terminals, and the government implications for reducing demurrage.

Incentive alignment in anchored MaxDiff yields superior predictive validity

AbstractMaximum Difference Scaling (MaxDiff) is an essential method in marketing concerning forecasting consumer purchase decisions and general product demand. However, the usefulness of traditional MaxDiff studies suffers from two limitations. First, it measures relative preferences, which prevents predicting how many consumers would actually buy a product and impedes comparing results across respondents. Second, market researchers apply MaxDiff in hypothetical settings that might not reveal valid preferences due to hypothetical bias. The first limitation has been addressed by implementing anchored MaxDiff variants. In contrast, the latter limitation has only been targeted in other preference measurement procedures such as conjoint analysis by applying incentive alignment. By integrating anchored MaxDiff (i.e., direct vs. indirect anchoring) with incentive alignment (present vs. absent) in a 2 × 2 between-subjects preregistered online experiment (n = 448), the current study is the first to address both threats. The results show that incentive-aligning MaxDiff increases the predictive validity regarding consequential product choices—importantly—independently of the anchoring method. In contrast, hypothetical MaxDiff variants overestimate general product demand. The article concludes by showcasing how the managerial implications drawn from anchored MaxDiff differ depending on the four tested variants. In addition, we provide the first incentive-aligned MaxDiff benchmark dataset in the field.

Heteromultivalent Ligand Display on Reversible Self-Assembled Monolayers (rSAMs): A Fluidic Platform for Tunable Influenza Virus Recognition.

We report on the design of heteromultivalent influenza A virus (IAV) receptors based on reversible self-assembled monolayers (SAMs) featuring two distinct mobile ligands. The principal layer building blocks consist of α-(4-amidinophenoxy)alkanes decorated at the ω-position with sialic acid (SA) and the neuraminidase inhibitor Zanamivir (Zan), acting as two mobile ligands binding to the complementary receptors hemagglutinin (HA) and neuraminidase (NA) on the virus surface. From ternary amphiphile mixtures comprising these ligands, the amidines spontaneously self-assemble on top of carboxylic acid-terminated SAMs to form reversible mixed monolayers (rSAMs) that are easily tunable with respect to the ligand ratio. We show that this results in the ability to construct surfaces featuring a very strong affinity for the surface proteins and specific virus subtypes. Hence, an rSAM prepared from solutions containing 15% SA and 10% Zan showed an exceptionally high affinity and selectivity for the avian IAV H7N9 (Kd = 11 fM) that strongly exceeded the affinity for other subtypes (H3N2, H5N1, H1N1). Changing the SA/Zan ratio resulted in changes in the relative preference between the four tested subtypes, suggesting this to be a key parameter for rapid adjustments of both virus affinity and selectivity.

Mixture independence foundations for expected utility

An alternative preference foundation for expected utility is provided. Our segregated approach considers four logically independent implications of the classic von Neumann–Morgenstern independence axiom. The monotonicity principle is, for a transitive relation, equivalent to monotonicity with respect to first-order stochastic dominance. Rank-dependent separability is similar to the comonotonic sure-thing principle used in the ambiguity literature. The remaining two properties are weak formulations of the independence principle which invoke the latter only for probability mixtures with the extreme, that is the best, respectively, the worst outcome. These four implications of independence, together with completeness, transitivity and continuity of a preference relation, characterize expected utility. Furthermore, if rank-dependent separability is dropped, expected utility still holds on each subset of three-outcomes lotteries that give positive probability to both the best and the worst outcomes.

A similarity-conformity-based clustering and decentralized feedback mechanism to manage incompatible decision-makers in large-scale group decision-making

This study proposes a similarity-conformity-based clustering and decentralized feedback mechanism to manage incompatible decision-makers (DMs) in the large-scale group construction decision-making problem. Firstly, conformity is proposed to measure the consistency of the most preferred alternative of any two DMs and then extends this concept to define the similarity-conformity level of DMs. The average conformity between DMs in a subgroup represents the cohesion degree of this subgroup. Based on these concepts, a new clustering method considers the conflicts of interest among DMs and introduces the similarity-conformity to divide them into several subgroups. Then, the number of DMs in a subgroup and the cohesion degree are combined to formulate the subgroups’ weights. A decentralized feedback mechanism is proposed to manage two categories of incompatible DMs in the consensus-reaching process. The proposed consensus model adjusts the incompatible DMs’ opinions or subgroups’ weights under four scenarios. In the proposed model, the four adjustment methods prove to be convergent, and they improve the group consensus level by ensuring the DMs’ conformity and the logical consistency of preference relation. Finally, it provides an example of the alternative selection for transportation infrastructure construction. The comparative study and the sensitivity analysis are performed to verify the effectiveness of the proposed method.

Reflexive, relevant and interactive: a STEM student-centred pedagogical approach for responsible research and innovation (RRI) training in higher education

ABSTRACT Background Whilst Responsible Research and Innovation (RRI) principles are increasingly informing and shaping Science, Technology, Engineering and Maths (STEM) research narratives and practices, the training of higher education (HE) STEM students in RRI is still developing, creating a mismatch between training provision and sector practices and expectations. Purpose Training of HE STEM students in RRI principles is a key component of the long-term changes RRI frameworks hope to achieve. However, little is known about postgraduate (PG) students’ awareness of, perceptions of and attitudes towards RRI, despite the pedagogical implications. This paper offers grounded insights and recommendations to shape this emerging area of pedagogical research and practice. Sample Forty-nine PG STEM students with a range of nationalities enrolled in six UK higher education institutions. Most students had not received prior formal RRI training, although a small number of students who had received some RRI training were selected for comparative purposes. Design and methods An exploratory, small-scale student-led, mixed-methods study that investigated PG STEM students’ engagements with RRI. Key objectives were to assess awareness and knowledge base; explore attitudes towards RRI; gauge the impact of the COVID-19 pandemic on RRI engagement; identify student preferences in relation to RRI training. Results The data points to the importance of individual characteristics and cultural backgrounds in shaping students’ engagement with RRI, as well as the significance of the context of student engagement with RRI. Although generally unaware of formal RRI frameworks, respondents provided examples of RRI that were drawn from national/cultural contexts. The perceived relevance of RRI varied with training level (Masters’ or PhD). A preference for interactive training in RRI was expressed, with some students commenting on the challenge of becoming reflexive practitioners. Conclusions This study provides important evidence to inform the pedagogical approach to RRI in HE STEM training, which the authors argue ought to be supported, reflexive, relevant, and interactive.

Does the presentation of true costs at the point of purchase nudge consumers toward sustainable product options?

Do true cost campaigns (TCCs)—which display prices at the point of purchase that include social and environmental negative externalities—nudge consumers toward more expensive sustainable products? From a theoretical point of view, the answer is promising: Communicating true costs means introducing external reference prices that provide a benchmark for consumers to assess price acceptability. Showing true costs triggers a general reference to the price of sustainability, and the higher price of sustainable products becomes at least partially explained by their lower “hidden costs” (i.e., costs to compensate for all environmental and social impacts). In two empirical studies, we demonstrate that for TCCs to be effective, the hidden costs for the sustainable products must be lower than those for the conventional alternatives. Interestingly, under this condition, TCCs have an effect in markets characterized by a larger (study 1) and a smaller (study 2) green gap. In both studies, we find that increased perceived price fairness explains the effect of TCCs, as measured by the relative preference for the sustainable compared to the conventional product. In addition, we see that the price difference between the two products plays a significant role in forming this preference judgment, independent of other factors included in the model and especially independent of TCC.

A Group Decision-Making Approach Leveraging Preference Relations Derived from Large Language Model

Group decision-making involves selecting among limited options. Experts share their perspectives in a comparative debate but then evaluate alternatives using preference relations, which can result in inconsistencies between their expressions and assessments. To address this, a method is proposed that automates the generation of these relationships from the debate comments, classifying them into positive and negative using sentiment analysis, namely with the Large Language Model. A new operator is introduced that weights these comments to calculate preference relations. Furthermore, modification of the relationships is allowed if the experts so wish. Moreover, another operator is incorporated that adjusts the weight of each expert according to his or her active participation in the discussion, assigning more weight to those who contribute more comments. Finally, this innovative method promotes coherence and equal participation in group decision-making by employing an innovative sentiment analysis detection system.

Optimization and Beyond

This paper will be concerned with hard choices—that is, choice situations where an agent cannot make a rationally justified choice. Specifically, this paper asks: if an agent cannot optimize in a given situation, are they facing a hard choice? A pair of claims are defended in light of this question. First, situations where an agent cannot optimize because of incompleteness of the binary preference or value relation constitute a hard choice. Second, situations where agents cannot optimize because the binary preference or value relation violates acyclicity do not constitute a hard choice.

The method of judging satisfactory consistency of linguistic judgment matrix based on adjacency matrix and 3-loop matrix

<abstract> <p>Language phrases are an effective way to express uncertain pieces of information, and easily conforms to the language habits of decision makers to describe the evaluation of things. The consistency judgment of a linguistic judgment matrices is the key to analytic hierarchy process (AHP). If a linguistic judgment matrix has a satisfactory consistency, then the rank of the decision schemes can be determined. In this study, the comparison relation between the decision schemes is first represented by a directed graph. The preference relation matrix of the linguistic judgment matrix is the adjacency matrix of the directed graph. We can use the $ n - 1 $ st power of the preference relation to judge the linguistic judgment matrix whether has a satisfactory consistency. The method is utilized if there is one and only one element in the $ n - 1 $ st power of the preference relation, and the element 1 is not on the main diagonal. Then the linguistic judgment matrix has a satisfactory consistency. If there are illogical judgments, the decision schemes that form a 3-loop can be identified and expressed through the second-order sub-matrix of the preference relation matrix. The feasibility of this theory can be verified through examples. The corresponding schemes for illogical judgments are represented in spatial coordinate system.</p> </abstract>

An assessment of fertilizer spraying drones based on hesitancy fuzzy similarity measures for sustainable green development

This research proposes a new similarity measure on hesitancy fuzzy graphs. The similarity measures are crucial concepts to explore the closeness between fuzzy graphs. The available imprecise and inconsistent data were more effectively handled by fuzzy similarity measures and intuitionistic fuzzy similarity measures. With time in decision-making theory, a complex frame of the background that occurs cannot be specified entirely by these fuzzy graphs but generalized fuzzy graphs like the hesitancy fuzzy graph can handle such a situation efficiently. The applicability of Hesitancy Fuzzy Graph (HFG) attracted the researchers to generalize ranking order based on the working procedures I, II of Xu’s approach and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approach. For this purpose, we first define hesitancy fuzzy preference relations, Laplacian energy, and similarity measures in these HFG. The decision-making technique is then proposed in which a weighting technique is developed by building an optimal model based on the proposed Laplacian energy and similarity measure under a hesitancy fuzzy environment. This research studies the decision-making problem in which the preference information given by the experts takes the form of Hesitancy Fuzzy Preference Relations (HFPRs) and the information about experts’ weights are completely unknown. This research utilizes the hesitancy fuzzy weighted averaging operator to aggregate all individual HFPRs into a collective HFPR. Then, based on the degree of similarity between the individual HFPRs and the collective ones, we develop an approach to determine the experts’ weights. Moreover, based on HFPRs, in which the similarity measures between the collective preference relation and hesitancy fuzzy ideal solution are used to rank the given alternatives. We provide an illustrative numerical example to illustrate the mentioned approach and also we compare the aggregate results of the two techniques.

Platform’s Recommendation Strategy Considering Limited Consumer Awareness and Market Encroachment

In today’s increasingly interconnected world, the platform owning the private label product (PL product) has formed a cocompetition relationship with the manufacturer, which may affect its recommendation strategy. Considering consumers’ limited awareness and relative preference, a game model is established to study the impacts of the platform’s recommendation strategy on pricing, consumer surplus, and social welfare. We demonstrate that consumer preference does not absolutely determine the platform’s recommendation strategy. When consumers prefer the manufacturer’s product (MB product), the platform recommends the PL product if the commission rate is low; otherwise, it recommends the MB product. When consumers prefer the PL product, the platform is willing to recommend the MB product only if the PL product’s valuation is not too large and the commission rate is high. By contrast, the manufacturer always prefers the MB product to be recommended. Furthermore, the recommendation strategy selection has different effects on different types of consumers. Although uninformed consumers always prefer the high-value product to be recommended, informed consumers may prefer the platform to recommend the low-value product. More importantly, the platform can achieve Pareto improvement by providing paid recommendation service, benefiting itself and the manufacturer. We also extend our model to conduct further analyses (e.g., differentiated consumer awareness levels, positive production cost, and corporate social responsibility), which examines the robustness of our results. Our study helps to explain the market practices and provides valuable guidelines for platform encroachment and recommendation decisions.

Relative palatability and preference of sorghum silage, moringa or sesbania leaf meals by red Maasai sheep

Relative palatability and preference of sorghum silage, moringa or sesbania leaf meals by red Maasai sheep

Consistency and consensus enhancing in group decision making with interval-valued intuitionistic multiplicative preference relations based on bounded confidence

Consistency and consensus enhancing in group decision making with interval-valued intuitionistic multiplicative preference relations based on bounded confidence

Managing incomplete general hesitant linguistic preference relations and their application

<p>Hesitant linguistic preference relations (HLPRs) are useful tools for decision makers (DMs) to express their qualitative judgements. However, the traditional HLPRs have one prominent drawback, which is to sort the linguistic values in a hesitant linguistic set. This will distort the DMs' initial judgements. In the present paper, a revised definition of HLPR, called general HLPR (GHLPR), was proposed. A characterization was explored for LPRs. Then, the characterization was extended to GHLPRs. Based on the characterization, the estimation of unknown entries in incomplete GHLPRs were carried out by two algorithms. The group decision-making problems with incomplete GHLPRs were settled by another algorithm. Finally, a case study was illustrated, and comparisons showed that our methods were more reasonable than the existent methods.</p>

Cooperative Game-based Consensus Adjustment Mechanism with Distribution Linguistic Preference Relations for Group Decision Making

Cooperative Game-based Consensus Adjustment Mechanism with Distribution Linguistic Preference Relations for Group Decision Making

A Novel Approach for Fuzzification of Rough Sets Based on Fuzzy Preference Relation: Properties and Application to Medicine Selection Problem

A Novel Approach for Fuzzification of Rough Sets Based on Fuzzy Preference Relation: Properties and Application to Medicine Selection Problem

Minimum adjustment consensus optimization models with fuzzy preference relations: The perspective of cardinal and ordinal consensus

Minimum adjustment consensus optimization models with fuzzy preference relations: The perspective of cardinal and ordinal consensus

Bayesian Approximation Methods of the Estimation for Generalized Exponential Distribution and Neutrosophic Approximation Methods

In this article, we used approximate methods to obtain Bayes method estimations for the shape and scale parameters of the generalized exponential distribution, as three approximation methods were employed: Lindley approximation and neutrosophic Lindley approximation, Gibbs sampling and neutrosophic Gibbs sampling, the most important samples based on the gamma informative prior under the squared error loss function. Through different simulation experiments a comparison was made between those estimators of these three approximate methods, from the simulation results we found a relative preference for the important sampling method over the other two methods. The results of simulation experiments were also confirmed by applying these approximate methods to real data representing the operating times of one of the machines of the publishing, printing, and translation house in Baghdad. On the other hand, we apply the same method to the neutrosophic exponential distribution, and the results will be compared to the classical case.