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The article explores the role of politicians in representative democracies and their impact on decision-making processes. It highlights the potential for bad apples, or unethical and incompetent politicians, to compromise the democratic process. The article suggests that conscientious politicians may need to compromise their moral convictions to resist bad apples and gain necessary support. It also discusses the benefits of public deliberation in empowering politicians to make informed compromises and resist the influence of bad apples. The article emphasizes the complexities and challenges of achieving political competence in representative democracies. [Extracted from the article]

Purpose: Part building orientation (PBO) is an important factor affecting the quality of laser powder bed fusion (L-PBF), which can affect the surface quality and manufacturing cost. The purpose of this paper is to propose a PBO optimization method to optimize the surface roughness and molding time of parts at the same time on the premise of small calculation scale and arbitrary resolution. Design/methodology/approach: Efficient and accurate evaluation is an important index of PBO optimization method. In this paper, a PBO optimization method based on scaling enumeration method is proposed, and the surface roughness and molding time of L-PBF parts are modeled as the objective evaluation function of PBO optimization process. To realize multi-objective optimization, an expert system is established, and the fuzzy multiple-attribute group decision-making theory is used to provide weights for each objective evaluation function. Findings: Research shows that the scaling-enumeration method can optimize the surface roughness and molding time at the same time and get the best PBO. Compared with the traditional method, the surface roughness and molding time are reduced by 1.1% and 0.58%, respectively, and the operation scale of the scaling-enumeration method is reduced by 99% compared with the traditional method. PBO with arbitrary angular resolution can be achieved. Originality/value: This paper presents a new method to optimize the forming direction of L-PBF parts. This method has small operation scale and accurate results, so it is meaningful for industrial application. [ABSTRACT FROM AUTHOR]

In today's increasingly complex stock era, resilient community planning for uncertainty risks is important for building resilient cities and communities and for realizing the refinement, process, and systematization of planning. Therefore, this paper proposes the concept of community planning resilience based on resilience theory and explores a framework system for evaluating the planning resilience of renewal and transformation community by taking renewal and transformation future community planning of Zhejiang as the research object. At the same time, the technical path of realizing public participation in community planning is explored through the method, mechanism, and technical platform of fuzzy group decision-making. Finally, the feasibility of the aforementioned framework and approach is experimentally explored and verified based on a typical case of renewal and renovation of future community planning. In order to make community regeneration planning more planning resilient, this paper develops a community planning group decision-making study from the perspective of community planning resilience, which provides a new research perspective for the field of community resilience and provides a reference for community planning evaluation and the construction of resilient communities. Practical Applications: In China's advancing urbanization, major cities are grappling with diverse risks, necessitating urban planning and governance with heightened resilience to face unforeseen black swan events and foreseeable gray rhinoceros challenges. This urgency is particularly pronounced in community renewal, prompting a vital shift from rigid to resilient planning methodologies. To address this shift, this paper advocates for resilient community construction to be a focal point in both policy and practical implementation of community regeneration. A novel approach utilizing the fuzzy decision-making method is proposed to establish a comprehensive resilient community renewal decision-making technology support system. This system employs a fuzzy comprehensive evaluation method, integrating an empowerment approach to simplify the complexities of a multicriteria index system. By mitigating the risks inherent in resilience planning, our approach enhances the scientific foundation and feasibility of decision-making in community renewal. A case study on the Future Community in Zhejiang Province validates the effectiveness of our approach, providing practical insights for constructing resilient community renewal models and system technology platforms. This research contributes to the ongoing exploration of strategies and technologies capable of addressing diverse challenges in urban development, providing references in enhancing urban resilience. [ABSTRACT FROM AUTHOR]

To address complex issues, facilitated modelling aims to represent and accommodate plural worldviews from many stakeholders and experts. In these contexts, group homogeneity can become problematic when participants' plurality of perspectives and information is missing and people attending facilitated sessions have similar problem perceptions and interests. This is a challenge because it can lead to narrow discussion, groupthink and undermine output quality. Despite not being uncommon, effective approaches to deal with homogeneity are hardly reported. This paper presents a new role—the New Devil's Advocate—in which some facilitators leave their neutrality-oriented stance and act as the missing stakeholders. The paper illustrates a first application to a group model building process aimed at supporting the development of energy efficiency policies in the UK. To evaluate the results, workshop transcripts were coded, participants' and facilitators' feedback collected, and the modelling output assessed with respect to the New Devil's Advocate interventions during the workshop. Although the role performance appears to increase facilitators' workload and be influenced by role performers' personality and background, the analysis shows positive results as a promising practice to address homogeneity. Additionally, it offers a practical experience of how facilitation teams may temporarily abandon neutrality and intervene on content. [ABSTRACT FROM AUTHOR]

Large-scale group decision-making (LSGDM) involves aggregating the opinions of participating decision-makers into collective opinions and selecting optimal solutions, addressing challenges such as a large number of participants, significant scale, and a low consensus. In real-world scenarios of LSGDM, various challenges are often encountered due to factors such as fuzzy uncertainties in decision information, the large size of decision groups, and the diverse backgrounds of participants. This paper introduces a dual fine-tuning-based LSGDM method using an online review. Initially, the sentiment analysis is conducted on online review data, and the identified sentiment words are graded and quantified into a fuzzy data set to understand the emotional tendencies of the text. Then, the Louvain algorithm is used to cluster the decision-makers. Meanwhile, a method combining Euclidean distances with Wasserstein distances is introduced to accurately measure data similarities and improve clustering performances. During the consensus-reaching process (CRP), a two-stage approach is employed to adjust the scores: to begin with, by refining the scores of the decision representatives via minor-scale group adjustments to generate a score matrix. Then, by identifying the scores corresponding to the minimum consensus level in the matrix for adjustment. Subsequently, the final adjusted score matrix is integrated with the prospect–regret theory to derive the comprehensive brand scores and rankings. Ultimately, the practicality and efficiency of the proposed model are demonstrated using a case study focused on the purchase of solar lamps. In summary, not only does the model effectively extract the online review data and enhance decision efficiency via clustering, but the dual fine-tuning mechanism in the model to improve consensus attainment also reduces the number of adjustment rounds and avoids multiple cycles without achieving the consensus. [ABSTRACT FROM AUTHOR]

Background: Globally, a growing number of calls to formalize and strengthen evidence-support systems have been released, all of which emphasize the importance of evidence-informed decision making. To achieve this, it is critical that evidence producers and decision-makers interact, and that decision-makers' evidence needs can be efficiently translated into questions to which evidence producers can respond. This paper aims to create a taxonomy of demand-driven questions for use by evidence producers, intermediaries (i.e., people working in between researchers and decision-makers) and decision-makers. Methods: We conducted a global cross-sectional survey of units providing some type of evidence support at the explicit request of decision-makers. Unit representatives were invited to answer an online questionnaire where they were asked to provide a list of the questions that they have addressed through their evidence-support mechanism. Descriptive analyses were used to analyze the survey responses, while the questions collected from each unit were iteratively analyzed to create a mutually exclusive and collectively exhaustive list of types of questions that can be answered with some form of evidence. Results: Twenty-nine individuals completed the questionnaire, and more than 250 submitted questions were analysed to create a taxonomy of 41 different types of demand-driven questions. These 41 questions were organized by the goal to be achieved, and the goals were grouped in the four decision-making stages (i) clarifying a societal problem, its causes and potential impacts; (ii) finding and selecting options to address a problem; (iii) implementing or scaling-up an option; and (iv) monitoring implementation and evaluating impacts. Conclusion: The mutually exclusive and collectively exhaustive list of demand-driven questions will help decision-makers (to ask and prioritize questions), evidence producers (to organize and present their work), and evidence-intermediaries (to connect evidence needs with evidence supply). [ABSTRACT FROM AUTHOR]

Purpose: This study aims to investigate the healthcare sector of the United Arab Emirates (UAE) to explore the significance of servant leadership and collaborative culture in fostering social sustainability. The primary objective of this paper is to investigate how servant leadership and a collaborative culture contribute to social sustainability in health care in the UAE. With a focus on promoting well-being within healthcare organizations, the paper aims to uncover the synergies between servant leadership, collaborative culture, and social sustainability. Design/methodology/approach: This paper conducted a multilayer literature review of existing literature on servant leadership, collaborative culture and social sustainability in health care, both globally and specifically in the UAE context, and a conceptual model was proposed. Findings: Servant leadership proves to be a culturally pertinent and effective leadership model within the UAE due to its alignment with cultural values, emphasis on community support, and the robust health-care system that contributes to individual well-being. This combination establishes a solid foundation for fostering a healthy and sustainable society. Research limitations/implications: Limitations and implications are discussed. The current research has not identified the boundary conditions under which servant leadership and collaborative culture may be more or less effective. This could involve exploring industry-specific influences or contextual factors. Theoretical and practical implications are discussed. Originality/value: The research seeks to unravel the interconnections between servant leadership, collaborative culture and social sustainability. To the best of the author's knowledge, none of the studies have explored the interrelationships of these constructs, particularly in the UAE context. [ABSTRACT FROM AUTHOR]

Floating cities have emerged as an efficient long-term solution over unsustainable practiced solutions to combat the rising seas problem; nevertheless, the world lacks an international, official, and comprehensive framework regarding floating cities. Although previous research approached modular floating city design; however, resulted in configurations with various critical design restrictions mainly regarding interlocking capabilities and space utilization. The purpose of this paper is to offer a new systematic strategy for configuring modular and expandable floating cities without such restrictions. This paper explores Euclidean tilings as a strategy to offer numerous configurations based on regular, semi-regular, and demi-regular tilings. Selecting the ideal configuration is complicated; therefore, both quantitative and qualitative data methods were implemented to attain the objectives. Via an extensive literature review, this research derives key factors for configuring floating cities, then sets a brainstorming session with experts for group decision making before providing findings upon calculations via analytic hierarchy process, one of the most used quantitative data methods of multiple-criteria decision analysis. Through comprehensive literature review: seakeeping, modularity, zoning and circulation, and feasibility have been identified as the most significant criteria in floating city research. It explores the qualities and limitations of triangular, squared, hexagonal, octagonal, and dodecagonal platforms. Regarding criteria, seakeeping was the most significant criterion for platform selection by 53.6%. Regarding platforms, the hexagonal platform scored the highest with 25.31%. Relying on this method and the design considerations presented, numerous dynamic configurations can be offered and assessed through specific contexts without any of the past restrictions. [ABSTRACT FROM AUTHOR]

Political goals are often sought by groups, whether they be political parties, trade associations, interest groups, or of any other sort. The formalities of modern political systems rely on individual voting, but group identities shape both individual behavior and the workings of government. To build a metaphor from the natural sciences, fluids (groups) are composed of molecules (individuals), but the fluid has properties of its own that affect its motion (actions) and interactions with other fluids (groups). This paper develops a model based on fluid dynamics relationships to explain group interactions based on observable group characteristics. It complements many strands of literature by explaining the circumstances in which existing models are valid, and in which they are incomplete. In so doing, it does not refute any existing models, but links them together and addresses their shortcomings. [ABSTRACT FROM AUTHOR]

The Self-Sustaining Village is a Danish eco-community whose mission is to develop communal sustainable living. This paper evaluates its sustainable living through a questionnaire survey of residents that measures their carbon footprint based on self-reported consumption. The survey also measures their life satisfaction. Results show that residents have a carbon footprint that is 60% below the national average and have a higher life satisfaction than the national average. Results from long-term participant observation explain the lower carbon footprints relating to energy, transport, food and other material items by the existence of particular physical and social infrastructures that shape life in the Self-Sustaining Village. Residents live more sustainably because their collective decisions make sustainable choices the standard or default options. They do so without having to make conscious choices individually and without sacrificing their private comfort for the environment and the climate. These villagers live up to their sustainable ideals and enjoy a richer social life that provides a higher life satisfaction than if they lived separately as independent households. The Self-Sustaining Village provides us with a model in which people live happier with less. [ABSTRACT FROM AUTHOR]

This paper uses a laboratory experiment to investigate the efficacy of different content moderation policies designed to combat misinformation on social media. These policies vary the way posts are monitored and the consequence imposed when misinformation is detected. We consider three monitoring protocols: (1) individuals can fact check information shared by other group members for a cost; (2) the social media platform randomly fact checks each post with a fixed probability; (3) the combination of individual and platform fact checking. We consider two consequences: (1) fact-checked posts are flagged, such that the results of the fact check are available to all who view the post; (2) fact-checked posts are flagged, and subjects found to have posted misinformation are automatically fact checked for two subsequent rounds, which we call persistent scrutiny. We compare our data to that of Pascarella et al. (Social media, (mis)information, and voting decisions. Working paper, 2022), which studies an identical environment without content moderation. We find that allowing individuals to fact check improves group decision making and welfare. Platform checking alone does not improve group decisions relative to the baseline with no moderation. It can improve welfare, but only in the case of persistent scrutiny. There are marginal improvements when the two protocols are combined. We also find that flagging is sufficient to curb the negative effects of misinformation. Adding persistent scrutiny does not improve the quality of decision-making; it leads to less engagement on the social media platform as fewer group members share posts. [ABSTRACT FROM AUTHOR]

Crane-lift planning is critical but challenging to ensure construction safety and efficiency for the installation of heavy components. It engages a group decision-making process that involves various stakeholders to exchange knowledge of crane capabilities, site characteristics, and operational feasibility. However, conventional planning methods based on drawings, animations, and standalone virtual prototypes are inefficient in enabling collaborative decision-making due to rigid visualization and inflexible interactions among different stakeholders. This paper aims to develop a collaborative heavy lift planning (Co-HeLP) system that engages crane suppliers, site managers, and crane operators in an immersive and shared environment for crane layout optimization and lifting simulation. Specifically, the system incorporates a multiuser networking unit to enable stakeholders to design crane-lift layouts concurrently and synchronize changes in real time for facilitating information-sharing and collaborative planning. In addition, a cross-platform virtual reality (VR) unit is developed to provide stakeholders with role-tailored interactions of crane allocation and first-person operation. Automatic feasibility checking is also formulated to assist stakeholders in appraising jib reach limits and loading capacities more efficiently. The system is demonstrated using a real-life modular building project that involves the installation of multiple bulky modules. To evaluate the system, two groups of industry practitioners from crane suppliers and main contractors conducted heavy lift planning using Co-HeLP, where their responses were collected using questionnaire-based surveys and interviews. Results show that the system is rated highly on automatability (0.77/1), collectiveness (0.81/1), and applicability (0.85/1), with the advantages of full-scale visualization and multirole interactions. This paper opens up new possibilities for stakeholders to jointly manage crane-lift constraints and build consensus on layout plans through a mutually interactive and role-independent shared environment. More broadly, the paper derives a generic multiuser VR mechanism to address fragmented coordination and inefficient knowledge transfer, thereby facilitating collective planning and management in construction. [ABSTRACT FROM AUTHOR]

The semiconductor industry is a resource-intensive sector that heavily relies on energy, water, chemicals, and raw materials. Within the semiconductor manufacturing process, the diffusion furnace, ion implantation machine, and plasma etching machine exhibit high energy demands or operate at extremely high temperatures, resulting in significant electricity consumption, which is usually carbon-intensive. To address energy conservation concerns, the industry adopts batch production technology, which allows for the simultaneous processing of multiple products. The energy-efficient parallel batch scheduling problem arises from the need to optimise product grouping and sequencing. In contrast to existing heuristics, meta-heuristics, and exact algorithms, this paper introduces the Deep Q-Network (DQN) algorithm as a novel approach to address the proposed problem. The DQN algorithm is built upon the agent's systematic learning of scheduling rules, thereby enabling it to offer guidance for online decision-making regarding the grouping and sequencing of products. The efficacy of the algorithm is substantiated through extensive computational experiments. [ABSTRACT FROM AUTHOR]

The phenomenon of algal bloom seriously affects the function of the aquatic ecosystems, damages the landscape of urban river and lakes, and threatens the safety of water use. The introduction of a multi-attribute decision-making method avoids the shortcomings of traditional algal bloom management that relies on manual experience. However, the weight-calculation part of this method still receives the artificial influence of human factors, which reduces the accuracy and scientific rigor of the decision. This paper presents a group decision-making method based on information self-learning which makes decision weights automatically clustered and assigned. A general framework of decision-making management is constructed for the algal bloom management process. In the decision-making process, an improved density-based clustering algorithm is used to automatically cluster and rank the decision data in the form of the three-parameter interval number, and ultimately obtain the optimal management method that meets the management objectives. Finally, the method was applied at the monitoring station of Sanjiadian Reservoir in Beijing, China. Based on the treatment objectives and water quality monitoring data of the station, relevant experts were invited to evaluate the management solutions, and the information self-organizing algorithm of this paper was used to automatically rank the decision-making methods, and finally obtain the most suitable management method for the station. Comparison with the water quality data and treatment inputs after the previous man-made selection of treatment options, and discussion among experts, show that the decision-making method is feasible and effective, and contributes to the sustainable treatment of algal blooms. [ABSTRACT FROM AUTHOR]

Recently, there has been a growing interest in distributed generation (DG) technologies, driven by various factors such as fuel price uncertainties, environmental constraints, and increasing power consumption along with transmission capacity shortages, in modern power systems. DG, which involves utilizing clean and renewable energy sources for power generation within the distribution system, has gained significant attention globally. Many developing countries, including Libya, are considering the adoption of DG technologies as part of their energy system expansion plans. Libya, located in North Africa and characterized by vast desert lands, has abundant solar radiation, making solar energy a promising and sustainable source of power. However, despite this energy potential, the southern part of Libya faces frequent power outages. In order to effectively maintain service quality, it is essential to conduct quantitative evaluation of wireless sensor networks. the evaluation of wireless sensor networks involves addressing the multiple attribute group decision-making (MAGDM) problem. To tackle the challenges of MAGDM, an extension of the classical EDAS (Evaluation based on Distance from Average Solution) method is proposed in this paper. The proposed method incorporates interval-valued intuitionistic fuzzy sets (IVIFSs), which provide a more flexible and comprehensive representation of uncertainty, to handle the complexities of MAGDM. The paper begins with a brief review of essential concepts related to IVIFSs. Then, the weights of attributes are determined using the CRITIC method. Subsequently, the IVIF-EDAS method is established by integrating the EDAS method with IVIFSs, and all the calculation procedures are described. [ABSTRACT FROM AUTHOR]

With the development of the internet economy, e-commerce has rapidly risen, and a large number of small and micro e-commerce enterprises have emerged. However, these enterprises have low financial information transparency, small scale, and high development uncertainty. Therefore, combining the characteristics of the internet economy, it is of great significance to dynamically evaluate credit risk. This not only helps to enhance the quality and rationality of credit risk evaluation results, but also helps to improve financing efficiency and reduce financing risks. The credit evaluation for small and micro enterprises is a multiple-attribute group decision-making (MAGDM). Recently, the TODIM (an acronym in Portuguese of interactive and multicriteria decision making) and TOPSIS method has been inaugurated to cope with MAGDM issues. The 2-tuple linguistic neutrosophic sets (2TLNSs) are inaugurated as an effective tool for characterizing uncertain information during the credit evaluation for small and micro enterprises. In this paper, the 2-tuple linguistic neutrosophic TODIM-TOPSIS (2TLNN-TODIM-TOPSIS) method is inaugurated to solve the MAGDM under 2TLNSs. Finally, a numerical case study for credit evaluation for small and micro enterprises is inaugurated to confirm the proposed method. The prime contribution of this paper are outlined: (1) The information entropy based on score function and accuracy function are built on the 2TLNSs to obtain weight information; (2) an integrated the 2-tuple linguistic neutrosophic TODIM-TOPSIS (2TLNN-TODIM-TOPSIS) method is established to cope with MAGDM; (3) An illustrative example for credit evaluation for small and micro enterprises has accomplished to illustrate the 2TLNN-TODIM-TOPSIS; (4) some comparative analysis are employed to verify the 2TLNN-TODIM-TOPSIS method. [ABSTRACT FROM AUTHOR]

Since the new century, the main theme of my country's higher education is to improve the quality of teaching. To this end, the education administrative department and the vast number of colleges and universities have done a lot of work. Looking back, this teaching quality construction involving thousands of colleges and universities across the country has attracted much attention from all walks of life. The government as the organizer promotes the active participation of colleges and universities in the form of teaching evaluation under the leadership of administrative authority and the "quality engineering" project with resources and reputation, which has played a huge role in improving the teaching quality of colleges and universities. The teaching quality evaluation in higher education is a classical multi-attribute group decision-making (MAGDM) issue. Recently, the Exponential TODIM (ExpTODIM) method has been used to cope with MAGDM issues. The 2-tuple linguistic neutrosophic sets (2TLNSs) are used as a tool for characterizing uncertain information during the teaching quality evaluation in higher education. In this paper, the 2-tuple linguistic neutrosophic number ExpTODIM (2TLNN-ExpTODIM) is built to solve the MAGDM under 2TLNSs. In the end, a numerical case study for teaching quality evaluation in higher education is given to validate the proposed method. The main contribution of this paper is constructed: (1) the Exponential TODIM (ExpTODIM) method is extended to the PLTSs; (2) the 2-tuple linguistic neutrosophic number ExpTODIM (2TLNN-ExpTODIM) is built to solve the MAGDM under 2TLNSs; (3) Finally, a numerical case study for teaching quality evaluation in higher education is given to validate the proposed method. [ABSTRACT FROM AUTHOR]

Renovating old neighborhoods for the benefit of people has become increasingly important in urban renewal. Nevertheless, old neighborhood renovations are currently considered a group decision-making issue under public participation, involving diverse decision-making subjects. Conflicts within a group are a common problem during group decision-making. In this paper, conflict is examined in the decision-making process for renovating old neighborhoods and novel ideas are provided for quantifying conflict. Public participation in old neighborhood renovations is assessed using conflict degree calculations in group decision-making. Based on the preferences of decision-making experts, a MIP DEA–DA (Mixed Integer Programming Data Envelopment Analysis–Discriminant Analysis) based partial binary tree cyclic clustering model is constructed for clustering experts, and an aggregated group conflict indicator and an aggregated conflict vector are computed, allowing for the quantification of conflict during the renovation process of the old neighborhood based on actual situations. Results indicate that there is primarily a conflict between the benefits of decision-making subject interests and the professionalism of decision-making renovations. This paper contributes to improving public participation, promoting the application of group decision-making theory in old neighborhood renovation, reducing conflict between decision-makers, and speeding up urban renewal. [ABSTRACT FROM AUTHOR]

Group decision making (GDM) can make full use of information in the group and consider perspectives of multiple stakeholders, which matches the characteristic of enterprise human resource (HR) allocation. This paper tries to optimize the enterprise HR allocation by establishing a fair-satisfied-based GDM model with two objectives of fairness and satisfaction respectively. Firstly, double hierarchy linguistic term set is used to collect the evaluation information of decision makers (DMs). Then, two quantification methods for DMs' satisfaction perception and fairness perception are respectively provided respectively based on the Prospect theory. Moreover, combining with the Pareto optimal idea, the fair-satisfied-based GDM model is designed and applied to deal with an enterprise HR allocation problem. Finally, some comprehensive analyses are made to validate the proposed method. [ABSTRACT FROM AUTHOR]

This paper aims to support decision-makers improve their ability to accurately capture and represent their judgment in a wide range of situations. To do this, we propose a new type of fuzzy set called a p , q -cubic quasi-rung orthopair fuzzy set ( p , q -CQOFS). The p , q -CQOFS allows for a more flexible and detailed expression of incomplete information through the use of an additional parameter. The paper describes the concept of p , q -CQOFS and its relationship to other types of fuzzy sets, introduces score and accuracy functions for p , q -CQOFS and analyzes some of its mathematical properties, defines the Hamming distance measure between two p , q -CQOFSs and examines some of its important properties, investigates the basic operations of p , q -CQOFSs and extends these laws to aggregation operators, and introduces weighted averaging and geometric aggregation operators for combining p , q -cubic quasi-rung orthopair fuzzy data. [ABSTRACT FROM AUTHOR]

New energy vehicles (NEVs) are the main direction for the development of the global automobile industry. Evaluating and analyzing the competitiveness of new energy vehicle enterprises (NEVEs) is of great significance for promoting their development. In order to explore the current situation of NEVEs in Henan Province, this paper firstly constructs a competitiveness evaluation index system for NEVEs, comprising both quantitative and qualitative indexes. Then, a new definition of consistency, the consistency measure level, and corresponding improvement methods for interval multiplicative preference relations (IMPRs) are proposed. On this basis, fuzzy group decision-making models with IMPRs are constructed to deal with the ambiguity and uncertainty of the decision information, where consistency and consensus are both considered. In our case study, decision results are derived using Lingo 11.0 software. The results of this paper show that the degree of specialization has the greatest impact on the competitiveness of NEVEs, and some NEVEs are deficient in this regard. Related suggestions based on expert evaluation results are also provided. In addition, a comparison with other consistency improvement methods of IMPRs reveals that the methods proposed utilize the original information provided to decision-makers to the utmost degree. [ABSTRACT FROM AUTHOR]

Artificial intelligence and blockchain can improve the effectiveness of leadership decision-making in two dimensions. Artificial intelligence technology can improve the scientificity of leadership decision-making, and blockchain technology can guarantee the democracy of leadership decision-making. Society pushes everyone to be gregarious. Group recommendation is thus one of the research focuses in recent years. Prior group recommendation algorithms fail to consider either the influence of group structure on computing scale or the impressions users of higher weights leave on other group members. To address the aforementioned challenges, this paper proposes a group recommendation model based on members’ influence and leader impact. In this paper, a model has been proposed to compute members’ influence on each other based on interactions and presence. The decisions of leaders identified by the proposed model are the basis for further group recommendation, which yields satisfactory recommendations for most group members as leaders’ judgments are more professional. Experimental results on real-world datasets demonstrate better accuracy of the proposed method compared to those of the mainstream group recommendation algorithms. [ABSTRACT FROM AUTHOR]

Warehouses represent key links in domestic and international commodity flows. The increasing shortage of workers and increasing costs on the one hand, and the increasing number and stricter demands of users on the other hand lead warehouse managers to realize their operations as efficiently as possible. A proposed model has an objective of enabling companies to monitor warehouse performance in an authoritative, reliable, and simple way and define appropriate corrective measures accordingly. The proposed empirical research consists of three stages, where in the first stage a combination of Principal Component Analysis-Data Envelopment Analysis methods was applied in order to determine efficient warehouses based on 90 decision making units. In the second phase, a completely new method called Interval Fuzzy Rough Pivot Pair-wise Relative Criteria Importance Assessment method used for determining criteria weights was developed and applied, which is one of the most important novelties of this study. In the last phase, the Interval Fuzzy Rough Measurement of Alternatives and Ranking according to the Compromise Solution method was applied to rank the alternatives. Twelve criteria were observed to evaluate 21 alternatives. Based on the results, it was concluded that salary stood out as the most important criterion, while amortization stood out as the least significant criterion. On the other hand, alternatives A9 and A10 stood out as the best-ranked alternatives while A1, A2, and A3 stood out as the least efficient ones. The paper provides clear scientific contributions that are reflected in the reduction of the gap that was observed after reviewing the literature where there is a lack of papers dealing with this task. Also, the combination of methods applied in the paper has not been used so far, so it can be said that this paper represents an excellent basis for further research. The model has practical contributions as it allows decision-makers to make quality decisions regarding the operation of their warehouses in different time periods or observation periods, as well as it represents a decision support tool that can be used for better warehouse management. • New model for warehouse performance evaluation has been proposed. • An integrated PCA-DEA-IFR PIPRECIA-IFR MARCOS Model was developed. • New approach IFR PIPRECIA was developed and presented in literature for first time. • The model enables more accurate and precise decision-making in logistics. [ABSTRACT FROM AUTHOR]

Data envelopment analysis (DEA) is one of the widely used methods to measure the efficiency scores of decision making units (DMUs). Conventional DEA is unable to consider both uncertainty in data and decision makers' (DMs) judgments in the evaluations. This study, to address the shortcomings of the conventional DEA, proposes a new best worst method (BWM)- robust credibility DEA (BWM-RCDEA) model to estimate the efficiency scores of DMUs considering DMs' preferences and uncertain data, simultaneously. First, to handle uncertainty in input and output variables, fuzzy credibility model has been applied. Additionally, uncertainty in constructing fuzzy sets is modeled using robust optimization with fuzzy perturbation degree. In this paper, two new types of RCDEA models are proposed: RCDEA model with exact perturbation in fuzzy inputs and outputs and RCDEA model with fuzzy perturbation in fuzzy inputs and outputs. In addition, to deal with flexibility of weights and incorporating DMs' judgement into the RCDEA model, a bi-objective BWM-RCDEA model is introduced. Finally, the proposed bi-objective model is solved using min–max approach. To illustrate the usefulness and capability of the proposed model, efficiency scores of 39 distribution companies in Iran is investigated and results are analyzed and discussed. Finally, based on the results, recommendations have been made for policy makers. [ABSTRACT FROM AUTHOR]

Crowdsourcing deals with solving problems by assigning them to a large number of non-experts called crowd using their spare time. In these systems, the final answer to the question is determined by summing up the votes obtained from the community. The popularity of these systems has increased by facilitating access for community members through mobile phones and the Internet. One of the issues raised in crowdsourcing is how to choose people and how to collect answers. Usually, users are separated based on their performance in a pre-test. Designing the pre-test for performance calculation is challenging; The pre-test questions should be selected to assess characteristics in individuals that are relevant to the main questions. One of the ways to increase the accuracy of crowdsourcing systems is by considering individuals’ cognitive characteristics and decision-making models to form a crowd and improve the estimation of their answer accuracy to questions. People can estimate the correctness of their responses while making a decision. The accuracy of this estimate is determined by a quantity called metacognition ability. Metacoginition is referred to the case where the confidence level is considered along with the answer to increase the accuracy of the solution. In this paper, by both mathematical and experimental analysis, we would answer the following question: Is it possible to improve the performance of a crowdsourcing system by understanding individuals’ metacognition and recording and utilizing users’ confidence in their answers? [ABSTRACT FROM AUTHOR]

The large-scale multi-attribute group decision-making (LSMAGDM) problem has become a hot research topic in the field of decision science. An R-numbers large-scale multi-attribute group decision-making (R-LSMAGDM) model is proposed to be constructed in this paper based on the advantages of R-numbers in capturing risks. First, the most commonly used clustering method, k-means, is introduced to determine the sub-groups. Then, a new sub-group weighting determination model is constructed by considering sub-group size and sub-group entropy. Next, we also build an optimized consensus-reaching model by improving the calculation method of the mean value. Then, the R-numbers weighted Hamy mean (RNWHM) operator is proposed to aggregate the sub-group information. In addition, the logarithmic percentage change-driven objective weighting (LOPCOW) method and the compromise ranking of alternatives from distance to ideal solution (CRADIS) method are used for attribute weighting calculation and alternative ranking, respectively. Finally, the effectiveness of the model is verified by an application example of hydrogen fuel cell logistics path selection. [ABSTRACT FROM AUTHOR]

In multi-criteria group decision-making (MCGDM) problems with incomplete fuzzy preference relations (FPRs), the consistency based on bounded rationality is preferred to depend on the historical preferences of decision makers to generate complete FPR matrices. There are two factors in influencing the effect of historical preferences on complete FPR matrices and further decision makers' satisfaction of group solutions. One is the effectiveness of historical preferences and the other is their relative weights. On this basis, this paper proposes a new MCGDM method with FPRs based on the filtration-and-weighting-based triangular bounded consistency (FWBTBC), where historical FPRs between three adjacent pairs of alternatives are characterized by triangles and these two factors are considered comprehensively. A triangle filtration process is designed based on the Grubbs criterion to filter the abnormal triangles in the collected ones. A weight determination process is developed, where a distance measure between remaining triangles is constructed to calculate their weights. Based on the combination of the two processes, the FWBTBC of FPRs is created and then the proposed method is presented. The validity of the proposed method is demonstrated by its application in analyzing a drainage tube supplier selection problem for a tertiary hospital located in Hefei, Anhui, China. [ABSTRACT FROM AUTHOR]

Purpose: Digital lean implementation can solve the dual problem of stagnating quality and rising costs in healthcare. Although technology adoption in healthcare has increased in the post-COVID world, value unlocking using technology needs a well-thought-out approach to achieve success. This paper provides a prescriptive framework for successfully implementing digital lean in healthcare. Design/methodology/approach: This study uses a mixed-method approach to achieve three research objectives. Whilst it uses a narrative review to identify the enablers, it uses qualitative thematic analysis techniques to categorise them into factors. The study utilises the delphi method for the thematic grouping of the enablers in the broader groups. The study used an advanced ordinal priority approach (OPA) to prioritise these factors. Finally, the study uses concordance analysis to assess the reliability of group decision-making. Findings: The study found that 20 identified enablers are rooted in practice factors, followed by human resource management (HRM) factors, customer factors, leadership factors and technology factors. These results further counter the myth that technology holds the utmost significance in implementing digital lean in healthcare and found the equal importance of factors related to people, customers, leadership and best practices such as benchmarking, continuous improvement and change management. Originality/value: The study is the first of its kind, providing the prescriptive framework for implementing digital lean in healthcare. The findings are useful for healthcare professionals and health policymakers. [ABSTRACT FROM AUTHOR]

As a new form of tourism, forest health has gradually attracted the attention of scholars and the public, becoming a new focus of research. There have been many qualitative studies on the product forms and development models of forest health tourism, but there is still a gap in the evaluation indicators and development systems for the development potential of forest health tourism. Using the research results of forest health tourism at home and abroad for reference, we will build an evaluation system for the development potential of forest health tourism, providing new research perspectives and operational practical support for the development of forest health tourism. The development potential evaluation of forest health tourism is a classical MAGDM problems. Recently, the TODIM and VIKOR technique has been employed to cope with MAGDM issues. The single-valued neutrosophic sets (SVNSs) are employed as a tool for characterizing uncertain information during the development potential evaluation of forest health tourism. In this paper, the single-valued neutrosophic number TODIM–VIKOR (SVNN–TODIM–VIKOR) technique is built to solve the MAGDM under SVNSs. In the end, a numerical case study for development potential evaluation of forest health tourism is given to validate the proposed technique. The prime contribution of this paper is managed: (1) the information entropy based on score values are constructed to obtain weight values under SVNSs; (2) an integrated SVNN–TODIM–VIKOR technique is constructed to address the MAGDM issue; and (3) an illustrative example for development potential evaluation of forest health tourism has accomplished to verify the SVNN–TODIM–VIKOR technique. [ABSTRACT FROM AUTHOR]

In a permissionless social network environment, it is difficult for a user to assess the trustworthiness of other users. Thus, trust management becomes a crucial issue for securing social activities. The conventional method for assessing trust involves an authority to identify fakes. However, generative AI is gradually reducing the ability to distinguish by producing vivid news or images. Additionally, modern social networks are becoming flatter and more diverse, making conventional trust management methods unfeasible. There is an urgent need to design a suitable blockchain consensus for decentralized trust management. In this paper, we propose a novel three-layer autonomous trust management model for online social users based on blockchain technology and group decision making. Instead of solving a hash puzzle, which consumes too much computational resources for social network users, this paper proposes a lightweight and verifiable blockchain consensus proof of trust for decentralized trust management. Furthermore, performance analysis and simulation results show that our model is valid and flexible for both synthetic networks and real social networks. [ABSTRACT FROM AUTHOR]

In this paper, we examine an individual consistency control in consensus building for group decision-making that was based on a paper of Li, Rodriguez, Martinez, Dong, and Herrera. we point out their important but questionable results and then present our comments. Our results will help researchers understand the structure of individual consistency control in consensus building under fuzzy environments. [ABSTRACT FROM AUTHOR]

When deciding where to visit next while traveling in a group, people have to make a trade-off in an interactive group recommender system between (a) disclosing their personal information to explain and support their arguments about what places to visit or to avoid (e.g., this place is too expensive for my budget) and (b) protecting their privacy by not disclosing too much. Arguably, this trade-off crucially depends on who the other group members are and how cooperative one aims to be in making the decision. This paper studies how an individual's personality, trust in group, and general privacy concern as well as their preference scenario and the task design serve as antecedents to their trade-off between disclosure benefit and privacy risk when disclosing their personal information (e.g., their current location, financial information, etc.) in a group recommendation explanation. We aim to design a model which helps us understand the relationship between risk and benefit and their moderating factors on final information disclosure in the group. To create realistic scenarios of group decision making where users can control the amount of information disclosed, we developed TouryBot. This chat-bot agent generates natural language explanations to help group members explain their arguments for suggestions to the group in the tourism domain [more specifically, the initial POI options were selected from the category of "Food" in Amsterdam (see Sect. 3.2 for the details)]. To understand the dynamics between the factors mentioned above and information disclosure, we conducted an online, between-subjects user experiment that involved 278 participants who were exposed to either a competitive task (i.e., instructed to convince the group to visit or skip a recommended place) or a cooperative task (i.e., instructed to reach a decision in the group). Results show that participants' personality and whether their preferences align with the majority affect their general privacy concern perception. This, in turn, affects their trust in the group, which affects their perception of privacy risk and disclosure benefit when disclosing personal information in the group, which ultimately influences the amount of personal information they disclose. A surprising finding was that the effect of privacy risk on information disclosure is different for different types of tasks: privacy risk significantly impacts information disclosure when the task of finding a suitable destination is framed competitively but not when it is framed cooperatively. These findings contribute to a better understanding of the moderating factors of information disclosure in group decision making and shed new light on the role of task design on information disclosure. We conclude with design recommendations for developing explanations in group decision-making systems. Further, we propose a theory of user modeling that shows what factors need to be considered when generating such group explanations automatically. [ABSTRACT FROM AUTHOR]

In this paper, we have established Cosine Trigonometric Operational Law (CTOL) and constructed Cosine Trigonometric Neutrosophic Weighted Averaging (CT-NWA) operator in the domain of generalized neutrosophic environment, which has been endorsed in a Multi-Criteria Group Decision Making (MCGDM) technique to select the best Anti-Pegasus software. In this study, the expertize recommendations and public opinion are well taken in context of debatable Pegasus issue in the Indian political, bureaucratic and democratic aspects. The problem has been addressed and resolved by using the integrated methodology of Analytical Network Process (ANP) and Evaluation based on Distance from Average Solution (EDAS) strategies. Here, we have optimized the weight factors and the gradation values of the decision makers as well as the sub-criteria of each criterion using Linear Programming (LP) model. Finally, we have performed the comparative analysis from various aspects to justify the reliability of our results. [ABSTRACT FROM AUTHOR]

With the increasing uncertainty in information, fuzzy theory has become a crucial approach for solving Multiple Attribute Group Decision Making (MAGDM) problems. This paper proposes two methods for addressing MAGDM problems with interval-valued Fermatean fuzzy information. Firstly, a series of Hamacher operators on the interval-valued Fermatean fuzzy numbers (IVFFNs) are derived, based on the fundamental operational norms of IVFFNs and the definitions of Hamacher operators. Secondly, two decision-making methods are proposed in this paper, based on the newly introduced operators, and they are effectively applied to address the enterprise recruitment problem. Finally, we investigate the influence of parameters on the final decision and observe that variations in parameters have small impact on the outcome. This feature simplifies the decision-making process by eliminating the need for extensive parameter tuning. Furthermore, a comparative analysis is conducted between the proposed methods and the research of other scholars, demonstrating the effectiveness of our approach and validating its reliability. [ABSTRACT FROM AUTHOR]

In the decision making of electricity sales packages, it is usually the specific situation of similar customers that provides the basis of a decision-making plan for target customer package selection, so it is particularly important to integrate the opinions of similar customers. Therefore, a multi-attribute group decision-making method for an electricity sales package is proposed, which is based on two-stage density clustering (TSDC) and minimum adjustment distance consensus. Firstly, in order to provide support for identifying similar customers among target customers, a sample customer set clustering method is proposed, which is based on a customer portrait label system and TSDC. Secondly, based on the entropy method, the attribute weight of the electricity sales package is determined. Based on the weight and the multi-attribute group decision-making consensus process, the minimum adjustment distance consensus of the sample customers' fuzzy evaluation matrix for the electricity sales package is proposed. Then, a full-ranking decision method for an electricity sales package based on target customer satisfaction is proposed. Finally, customers in a certain area of China are selected as an example. This example is used to verify the accuracy and effectiveness of the decision-making method of electricity sales package proposed in this paper. [ABSTRACT FROM AUTHOR]

This paper undertakes an analysis and discussion of the methodological challenges and insights derived from three longitudinal qualitative studies, all conducted in Chile during the COVID-19 pandemic and subject to comprehensive theoretical-methodological reflection processes centred on their respective designs. This analysis makes a significant contribution to interdisciplinary discussions within social research, with a particular emphasis on longitudinal trajectories. First, we present a comparative analysis of three studies in social work, utilising Saldaña's questions addressing changes and learning in longitudinal studies. The first study explores the labour trajectories of researchers, the second focuses on the educational trajectories of students, and the last examines therapeutic alliance trajectories between social workers and families within the child protection system. Following this, we delve into the methodological decisions made by the research group during the execution of these longitudinal studies. This encompasses an examination of participant involvement, temporal definitions of the adopted designs, and the most suitable methodological tools for analysing change processes over time. The outcomes of this comparative analysis reveal the distinctive characteristics of the three longitudinal studies, providing insights into how the time dimension is explored within them. We highlight key criteria essential for consideration in longitudinal qualitative research, particularly regarding participants and methodology. In conclusion, we advocate for an expanded reflection within the realm of longitudinal qualitative methodology, encompassing aspects such as design choices, approaches to data analysis, integration of technology in information processing, and strategies for maintaining participant engagement. [ABSTRACT FROM AUTHOR]

Crude oil, the backbone of modern industry, holds unparalleled significance as a global energy cornerstone. Unlocking its potential hinges on effective pretreatment techniques, ensuring purity, and maximizing efficiency. This study extends the established Spherical Fuzzy Set paradigm to explore the domain of Disc Spherical Fuzzy Sets (D-SFSs) in critical decision-making for crude oil preparation. Investigating D-SFSs within the Aczel Alsina norm, the research employs comparison rules, conversion rules, and distance metrics. Primary operations of the Aczel Alsina norm in D-SFSs are examined, laying the groundwork for introducing unique aggregation operations within this framework. The paper's primary aim is to propose a hybrid method, termed MEREC-SWARA-MARCOS-D-SFSs Multiple Attribute Group Decision Making, which integrates the aforementioned aggregation procedures. A case study on crude oil pretreatment validates the effectiveness of the proposed method. Furthermore, a comprehensive comparison with CoCoSo underscores the reliability of the method. This study represents a significant stride in enhancing decision-making by providing a robust framework to tackle complex situations, particularly in the critical domain of crude oil pretreatment. [ABSTRACT FROM AUTHOR]

Most practical decision-making problems commonly contain several alternatives evaluated by more than one decision maker (DM) according to the built criteria system. It is known as multi-criteria group decision making problems. The decision-making results should be based on highly similar individual opinions to derive the most suitable choice. Nevertheless, the divergence of individual judgments is usually significant due to various subjective and objective reasons. In this case, some DMs need to modify their judgments to derive final results, the consensus adjustment in group decision making. Like supply chain management, the centralized consensus process can minimize individual adjustments under the set consensus condition. Based on the hypothesis of rational humans that individuals always pursue maximum benefits, this paper proposes an iterative consensus process in a centralized environment. According to non-consensus adjustments, the iterative procedure contains non-transferable and transferable two parts. When the non-transferable consensus adjustment procedure is completed, the transferable consensus adjustment procedure is activated using an asymmetrical Nash bargaining game allocation mechanism. Additionally, a case study is provided to demonstrate the efficaciousness and practicality of the new method. Meanwhile, sensitivity and comparison analyses are done. [ABSTRACT FROM AUTHOR]

Like common recommender systems, group recommendation usually suffers from popularity bias where popular items are more likely to be suggested and exposed to users over long-tailed ones. The skewed data distribution caused discrimination against a great amount of unpopular items, which will be further intensified during the group decision-making process. Despite previous studies devoted to addressing popularity bias issue in recommendations, rarely have other works concentrated on such problem in group recommender systems. In this paper, we identify the negative impact of item popularity in a causality manner and propose a Popularity Reweighting Framework for Group Recommendation (PopGR). Specifically, a popularity-aware weighting function is adopted to mitigate the bias problem by incorporating the popularity level of items along with their intrinsic characteristics into group modeling. Experiments conducted on two real world benchmark datasets justify the effectiveness of our model to alleviate bias while maintaining reasonable ranking accuracy. [ABSTRACT FROM AUTHOR]

In this paper, an approach is proposed for assessing the performance of experts in the group from two perspectives: (1) individual consistencies and (2) deviations from the group decision. The quality of performance of the experts is based on combining the standard and rough analytic hierarchy process (AHP) with the technique for order of preference by similarity to the ideal solution (TOPSIS). The statistical method CRITIC is used to derive weights for the TOPSIS method before the experts are assessed based on demonstrated consistency and deviations from the group. Common performance indicators, such as consistency ratio, Euclidean distance, compatibility, and Spearman's correlation coefficient, are proposed for re-grouping experts before making the final decisions. A genetic algorithm enables the efficient solving of this complex clustering problem. Implementing the described approach and method can be useful in comparable assessment frameworks. A critical aspect is conducting a thorough pre-assessment of the competence of potential decision makers, often referred to as experts who may not consistently exhibit apparent expertise. The competence of decision makers (which does not have to be associated with compatibility) is evidenced by selected consistency parameters, and in a way, a pre-assessment of their competence follows Plato's 'government of the wise' principle. In the presented study, the compatibility of individuals in the group with the collective position (group decision) is measured by parameters related to their compatibility with the group solution and statistical deviation while ranking decision elements. The proposed multi-model-based approach stands out for its resilience in conducting thorough pre-assessment of the quality (competence) of potential decision makers, often regarded as experts who might not consistently display evident expertise. The wetland study area in Serbia is used as an example application, where seven measures for reducing the risk of drought were evaluated by twelve experts coming from different sectors and with different backgrounds and expertise. [ABSTRACT FROM AUTHOR]

The interval-valued intuitionistic fuzzy sets (IVIFSs), based on the intuitionistic fuzzy sets (IFSs), combine the classical decision method and its research and application is attracting attention. After a comparative analysis, it becomes clear that multiple classical methods with IVIFSs' information have been applied to many practical issues. In this paper, we extended the classical EDAS method based on the Cumulative Prospect Theory (CPT) considering the decision experts (DEs)' psychological factors under IVIFSs. Taking the fuzzy and uncertain character of the IVIFSs and the psychological preference into consideration, an original EDAS method, based on the CPT under IVIFSs (IVIF-CPT-EDAS) method, is created for multiple-attribute group decision making (MAGDM) issues. Meanwhile, the information entropy method is used to evaluate the attribute weight. Finally, a numerical example for Green Technology Venture Capital (GTVC) project selection is given, some comparisons are used to illustrate the advantages of the IVIF-CPT-EDAS method and a sensitivity analysis is applied to prove the effectiveness and stability of this new method. [ABSTRACT FROM AUTHOR]

The proliferation of IoT devices has ushered in a new era of smart homes, where efficient energy management is a paramount concern. Multiagent artificial intelligence-of-things (MAIoT) has emerged as a promising approach to address the complex challenges of smart home energy management. This research study examines MAIoT's components, functioning, benefits, and drawbacks. MAIoT systems improve energy efficiency and user comfort by combining multiagent systems and IoT devices. However, privacy, security, interoperability, scalability, and user acceptability must be addressed. As technology advances, MAIoT in smart home energy management will offer more sophisticated and adaptable solutions to cut energy consumption and promote sustainability. This article describes how energy status and internal pricing signals affect group intelligent decision making and the interaction dynamics between consumers or decision makers. In a multiagent configuration based on the new concept of artificial intelligence-of-things, this intelligent home energy management challenge is simulated and illustrated using software and hardware. Based on sufficient experimental simulations, this paper suggested that residential clients can significantly improve their economic benefit and decisionmaking efficiency. [ABSTRACT FROM AUTHOR]

In this paper, by using the notion of fuzzy points and equality algebras, the notions of fuzzy point equality algebra, equality-subalgebra, and ideal were established. Some characterizations of fuzzy subalgebras were provided by using such concepts. We defined the concepts of (∈, ∈) and (∈, ∈ ∨ q)-fuzzy ideals of equality algebras, discussed some properties, and found some equivalent definitions of them. In addition, we investigated the relation between different kinds of (α, β)-fuzzy subalgebras and (α, β)-fuzzy ideals on equality algebras. Also, by using the notion of (∈, ∈)-fuzzy ideal, we defined two equivalence relations on equality algebras and we introduced an order on classes of X, and we proved that the set of all classes of X by these order is a poset. [ABSTRACT FROM AUTHOR]

In this paper, we defined the concept of SUP-hesitant fuzzy interior ideals in Γsemigroups, which is generalized of hesitant fuzzy interior ideals in Γ-semigroups. Additionally, we study fundamental properties of SUP-hesitant fuzzy interior ideals in Γ-semigroups. Finally, we investigate characterized properties of those. [ABSTRACT FROM AUTHOR]

This paper investigates N-bipolar hypersoft topology (N-BHST), a novel extension of both the well-established N-hypersoft topology (N-HST) and hypersoft topology (HST). Deviating significantly from its precursor, the N-bipolar hypersoft (N-BHS) set, N-BHST introduces a multi-opinion approach to decision-making, augmenting robustness and adaptability. This innovative framework addresses identified limitations in N-bipolar soft topology (N-BST), especially in managing multi-argument approximate functions. The study analyzes various operators (closure, interior, exterior, and boundary) within the N-BHST framework, elucidating their interrelationships. Additionally, an examination is carried out on the enhancement of multi-criteria group decision-making (MCGDM) using N-BHST, setting it apart from existing models. A numerical example is presented to illustrate its application in real-world decision scenarios. [ABSTRACT FROM AUTHOR]

Social insects are ecologically and evolutionarily the most successful organisms on earth and can achieve robust collective behaviors through local interactions among group members. Colony migration has been considered as a leading example of collective decision making in social insects. In this paper, a piecewise colony migration system with recruitment switching is proposed to explore underlying mechanisms and synergistic effects of colony size and quorum on the outcomes of collective decision. The dynamical behavior of the nonsmooth system is studied, and sufficient conditions for the existence and stability of equilibrium are provided. The theoretical results suggest that large colonies are more likely to emigrate to a new site. More interesting findings include but are not limited to that (a) the system may exhibit oscillation when the colony size is below a critical level and (b) the system may also exhibit a bistable state, i.e., the colony migrates to a new site or the old nest depending on the initial size of recruiters. Bifurcation analysis shows that the variations of colony size and quorum threshold greatly impact the dynamics. The results suggest that it is important to distinguish between two populations of recruiters in modeling. This work may provide important insights on how simple and local interactions achieve the collective migrating activity in social insects. [ABSTRACT FROM AUTHOR]