Soft Consensus Research Articles

Attitude-Based Intuitionistic Fuzzy Graph Model for Conflict Resolution With Soft Consensus: Application to Dam Construction Projects

Construction of dam projects built on international rivers usually faces many conflicts due to different interests, values, and preferences of social entities. The graph model for conflict resolution (GMCR) is a powerful and flexible tool to model and analyze strategic conflicts. In this study, we introduce a GMCR model to address construction conflicts among different social entities. To process uncertain and vague information in preferences, this study allows decision-makers to use intuitionistic fuzzy information to express their preference information. First, we define necessary definitions, including the intuitionistic relative certainty of preference (IRCP), intuitionistic satisficing threshold (IST), and intuitionistic unilateral improvement (IUI). When using a graph model to represent real-world disputes, a decision-maker (DM) may have different attitudes toward other DMs. To analyze the influence of attitudes, we originally introduced the concept of soft consensus in GMCR. Then, three kinds of attitudes, i.e., positive attitude, neutral attitude, and negative attitude, are identified. Next, we propose four intuitionistic stability definitions considering the attitudes of DMs. Finally, an illustrative example regarding the Xayaburi Dam dispute that occurred in Indochina Peninsula is provided to demonstrate the applicability of the proposed model.

Robust minimum cost consensus models with uncertain asymmetric costs based on linear uncertain-constrained tolerance level

The ability of the minimum cost consensus model (MCCM) to promote consensus reaching in the domain of group decision-making (GDM) has been extensively studied. Recently, the MCCM has been enhanced by introducing the consensus principle and tolerance level to achieve a soft consensus. However, the potential impact of asymmetric and uncertain unit adjustment costs on the effectiveness of the consensus reaching process (CRP) has been overlooked. This paper aims to investigate the implications of uncertain asymmetric costs for achieving consensus with a certain level of tolerance, where new robust MCCMs with uncertain asymmetric costs are constructed under four uncertainty sets for the unit adjustment costs. Considering the linear uncertain-constrained tolerance level and consensus level, we incorporate the insight of an expert with a cost-free threshold into models. Additionally, through a pollutant emission application, the proposed robust MCCMs are able to effectively handle uncertainties arising from costs and improve the quality of the CRP compared to the traditional models. Finally, we conduct simulation experiments and sensitivity analysis to illustrate the effectiveness of the proposed models on achieving a consensus by identifying appropriate parameters.

Is Biblical Studies Stuck in Antiquarianism? The Case of Behemoth and Leviathan

Abstract In this article, a trajectory is traced that demonstrates that the soft consensus that Behemoth and Leviathan are the hippo and croc, respectively, is part of a larger phenomenon whereby these originally monstrous beasts have become domesticated and demythologized or their mythological nature rationalized. The great beasts as natural Egyptian animals only goes back to a 17th century Huguenot who was an etymologist, orientalist, antiquarian, and minister. This process of/domestication/demythologization in a weak form actually can be discerned in the Hebrew Bible itself but reaches its zenith during the Enlightenment. In the article, a trajectory of protest against this process is mapped out. And, finally, monsters are shown to be important theodicean agents in the ancient world as well as representing important mechanisms of cultural identity.

Minimum cost consensus modeling under dynamic feedback regulation mechanism considering consensus principle and tolerance level

Since the hard consensus is difficult and costly to achieve in group decision making (GDM), it is reasonable and necessary for decision makers (DMs) to reach a soft consensus within a certain level of tolerance and consensus. To achieve this purpose, the concepts and definitions of consensus principle, tolerance level and consensus level are proposed in this paper. The consensus principle keeps the consensus reaching process (CRP) always moving in a better direction, and the tolerance level characterizes the individual’s psychological receptivity, while the consensus level guarantees the quality of consensus results, then the minimum cost consensus models (MCCMs) based on these three points are proposed. Thereafter, we add a feedback regulation mechanism based on tolerance threshold rewards to the proposed MCCMs, the mechanism connects individual weight with tolerance threshold, avoiding the subjective disadvantage caused by the two values need to be given in advance. Meanwhile, the framework and specific algorithm of CRP under the tolerance threshold reward feedback regulation mechanism are given, providing a precise method for solving the realistic consensus problems. Finally, the consensus reaching case of the carbon emission benchmark shows the reasonable effectiveness of the two different models proposed in this paper, and the comparative analysis shows the differences and applicability of the two models.

Democratic consensus reaching process for multi-person multi-criteria large scale decision making considering participants’ individual attributes and concerns

Consensus reaching is a key issue in group decision-making, because conflicts of interest among groups are common. Democratic consensus refers to achieve a soft consensus among collective as well as ensure the effective participation and satisfaction of individuals. Multi-person multi-criteria large scale decision making (MpMcLSDM) usually involves a huge number of decision makers (DMs/participants), and different DMs usually have different interests. Thus, how to effectively manage individuals to promote democratic consensus is a current research challenge. To do that, this research develops a democratic consensus reaching process (DCRP) for MpMcLSDM problems. In the proposed approach, a clustering method that considers both the opinion similarity and individual concern similarity of DM is firstly given to decrease the complexity of MpMcLSDM issues. Subsequently, we propose to assign equal initial weight to each cluster to protect the interests of minorities. Meanwhile, a consensus contribution-based dynamic interactive weight updating method is implemented in the DCRPs to promote a high level of democratic consensus. Besides, a compromise degree-based consensus feedback strategy is developed to improve the efficiency of the DCRPs. The proposed feedback mechanism effectively considers the individual concern and adjustment willingness of DMs in the DCRPs. Finally, a case study and some comparisons are given to show the effectiveness and innovation of this research.

Maximum satisfaction consensus with budget constraints considering individual tolerance and compromise limit behaviors

In the consensus reaching process (CRP), decision-makers (DMs) often present behaviors of tolerance and limited compromise when adjusting their opinions, which may lead to the actual decision deviating from the solution recommendation obtained by applying the current minimum cost consensus theory. In this paper, we propose a consensus-reaching approach based on maximum satisfaction under budget constraints for DMs with these two behaviors. To obtain a feasible budget constraint of the CRP, a reference budget consensus model (RBCM) incorporating both of these behaviors is first developed. Under the constraint of the limited budget, we propose a maximum satisfaction consensus model (MSCM) to maximize the satisfaction level of all DMs based on the goal programming theory. To further achieve different levels of consensus, we propose a soft MSCM under a certain threshold using soft consensus measures. Moreover, the proposed consensus models are illustrated by the data of China’s Taihu Lake pollution control. The results suggest that: First, the individual tolerance behavior has a higher impact on the consensus outcome than the compromise limit, whereas the latter is more likely to affect the satisfaction of each DM. Second, as the budget increases, the consensus opinion and group satisfaction level will increase toward a higher average cost until they remain constant. Last, a higher consensus threshold will reduce DMs’ satisfaction levels, and an appropriate threshold should be selected as the control baseline of the CRP in the real application.

A Delphi study of human factors methods for the evaluation of adaptation in safety-related organisations

Advances in safety science point towards an approach to the management of safety-related risks within complex sociotechnical systems that focuses on the understanding of adaptation. The study of adaptation would benefit from the use of structured and formalised approaches at, and across, all levels of the organisational hierarchy (individual, team and organisation). However, with a large number of potential Human Factors methods available to the safety practitioner, it is unclear which methods are most appropriate for an exploration of this important tenet of systems thinking. A 3-round ranking Delphi study has been conducted to survey the range of methods to determine a soft consensus amongst subject matter experts. In each of the three rounds the list was successively refined. Cognitive Work Analysis (CWA) and Critical Decision Method (CDM) were recommended for the micro level (which could be complemented by HTA). At the meso level, CDM and CWA together with complementary methods, such as Systems Theoretic Accident Modelling and Processes (STAMP), Functional Resonance Analysis Method (FRAM) and the Event Analysis for Systemic Teamwork (EAST) method. STAMP and FRAM were recommended at the macro level, which could be complemented with Hierarchical Task Analysis (HTA), Ethnographic Analysis, EAST and the Human Factors Analysis and Classification System (HFACS). The methods, taken together, should provide a means of exploring adaptation at all of the sociotechnical system levels. This work provides safety practitioners with consolidated guidance on the methods that could be used to explore adaptation as a source of safety in complex sociotechnical systems.

A large‐scale group decision making method with a consensus reaching process under cognitive linguistic environment

AbstractGroup decision making (GDM) problems involving large‐scale experts are critical and frequent in practice. However, it is difficult to make an appropriate decision due to the disagreement among experts and the uncertain information of experts because of their different backgrounds and limited knowledge. This study aims to propose a large‐scale GDM method with a consensus reaching process under the cognitive linguistic environment. First, we divide the large‐scale group into several subgroups to simplify the problem. Then, the double normalization based multiaggregation (DNMA) method is extended to deal with the individual decision matrix of each subgroup that is composed of complex linguistic evaluations. In addition, a consensus reaching process is proposed to check the consensus level between subgroups and achieve a soft consensus through a consensus improvement method. A dynamic weight‐determining method is developed for subgroups based on their consensus levels. Finally, the framework of the large‐scale group DNMA method with a consensus reaching process is constructed. The practicality of the proposed method is illustrated by an illustrative example about the e‐book supplier selection, and the advantages of the proposed method are highlighted by comparative analyses with other decision‐making methods.

HESITANT FUZZY LINGUISTIC DNMA METHOD WITH CARDINAL CONSENSUS REACHING PROCESS FOR SHOPPING MALL LOCATION SELECTION

The hesitant fuzzy linguistic term set is an effective tool to express qualitative evaluations since it is close to human reasoning and expressing habits. In this paper, we propose a multi-expert multi-criterion decision-making method integrating the double normalization-based multi-aggregation (DNMA) method with a cardinal consensus reaching process, where the assessments of alternatives over multiple criteria are expressed as hesitant fuzzy linguistic term sets. To do so, the DNMA method involving double normalizations and three aggregation tools is extended to deal with the hesitant fuzzy linguistic information and derive the ranking of alternatives with respect to each expert. In addition, a cardinal consensus reaching process is introduced to help experts reach an acceptable consensus level. In other words, the soft consensus is considered in the multi-expert multi-criterion decision-making process. Subsequently, an extended Borda rule is developed to aggregate the subordinate ranks and integrated scores of alternatives, and then deduce the comprehensive ranking of alternatives. A case study is given to illustrate the practicability of the proposed method for selecting the optimal geographical location of a larger-scale shopping mall in the new urbanization for a construction investment agency. The proposed method is compared with other ranking methods to illustrate its advantages.

Soft consensus cost models for group decision making and economic interpretations

In a group decision-making (GDM) process, experts reach a consensus after discussion and persuasion, which requires a moderator to spend time and resource to persuade experts to change their original opinions. Since a unanimous agreement is hard to achieve and costly, a consensus degree or soft consensus was used and various approaches have been proposed to measure the level of consensus in GDM. Though cost is an important factor in GDM, few works have calculated consensus cost occurred during the process. Moreover, the degree of consensus was not considered in the minimum cost consensus study. The objective of this paper is to propose consensus models under a certain degree of consensus, which considers both consensus degree and cost in GDM. To do this, we develop a generalized soft cost consensus model under a certain degree of consensus, which is built by defining a consensus level function and a generalized aggregation operator. A soft minimum cost consensus model is constructed based on arithmetic weighted average operator (AWAO), and the maximum return model is constructed through its dual model. The cost (compensation) is studied from both the perspectives of a moderator and individual experts. The relationships between the two soft consensus cost models are analyzed, and the economic significance of the models are also discussed. Numerical examples are used to explain the proposed models. In addition, to show the usability of the proposed models in real-world context, we apply the proposed models to a loan consensus scenario using data from an online peer-to-peer lending platform.

Group decision making and soft consensus: Analyzing citation classics by means of H-Classics

Citation classics provide a view on the documents that could be considered the basis of a scientific area and that have therefore attracted historical and great interest by the researchers. Because of the potential relation that can be established between quality research and citation counts, the identification of citation classics also is a main way used to carry out a methodical assessment of research performance. In recent times, the H-Classics, a methodology that is founded on the popular h-index, has been introduced to determine the highly cited papers. This research aims to provide an insight into the development of soft consensus in group decision making via H-Classics. Particularly, we identify both the scientific actors (researchers, institutions, countries, and journals) making the biggest research contribution to the development of this scientific area and the topics covered.

Reproducibility of species recognition in modern planktonic foraminifera and its implications for analyses of community structure

Abstract. Applications of planktonic foraminifera in Quaternary palaeoceanographic and palaeobiological studies require consistency in species identification. Yet the degree of taxonomic consistency among the practitioners and the effects of any potential deviations on community structure metrics have never been quantitatively assessed. Here we present the results of an experiment in taxonomic consistency involving 21 researchers representing a range of experience and taxonomic schools from around the world. Participants were asked to identify the same two sets of 300 specimens from a modern subtropical North Atlantic sample, one sieved at >125 µm and one at > 150 µm. The identification was carried out either on actual specimens (slide test) or their digital images (digital test). The specimens were fixed so the identifications could be directly compared. In all tests, only between one-quarter and one-eighth of the specimens achieved absolute agreement. Therefore, the identifications across the participants were used to determine a consensus ID for each specimen. Since no strict consensus (>50 % agreement) could be achieved for 20–30 % of the specimens, we used a “soft consensus” based on the most common identification. The average percentage agreement relative to the consensus of the slide test was 77 % in the >150 µm and 69 % in the >125 µm test. These values were 7 % lower for the digital analyses. We find that taxonomic consistency is enhanced when researchers have been trained within a taxonomic school and when they regularly perform community analyses. There is an almost negligible effect of taxonomic inconsistency on sea surface temperature estimates based on transfer function conversion of the census counts, indicating the temperature signal in foraminiferal assemblages is correctly represented even if only two-thirds of the assemblage is consistently identified. The same does not apply to measures of diversity and community structure within the assemblage, and here we advise caution in using compound datasets for such studies. The decrease in the level of consistency when specimens are identified from digital images is significant and species-specific, with implications for the development of training sets for automated identification systems.

Recommending diverse friends in signed social networks based on adaptive soft consensus paradigm using variable length genetic algorithm

Despite the strategic role played by individuals, who act as intermediaries between distinct groups of people, the problem of recommending diverse friends in signed social networks (SSNs) still remains largely unexplored. Our model integrates homophily and diversity to develop an adaptive consensus based framework, which involves fuzzy group decision making analysis by leveraging on the signed social links and underlying users’ preferences, to offer lists of connections which are diverse as well as relevant. Our contributions are three-fold. First, we modeled the fuzzy binary adjacency relations between users, thereafter referred as decision makers (DMs), exploiting users’ preferences conferred on a set of items, and then higher order fuzzy m-ary adjacency relations are constructed to represent the grade of agreement between a set of m DMs. Further, in order to evaluate the relevance of each decision maker involved in the decision making process, we introduce a novel diversity measure based on the knowledge of socio-psychological theories and the information contained in social and interest links. Next, by employing variable-length genetic algorithm, an idea of adaptive consensus is explored to evolve groups of experts which are highly consensual as well as influential in the social network. Finally, on the basis of opinions gleaned from the members of these groups, sign of unknown links are predicted, thereby generating a top-N recommendations list of diverse friends. Extensive experimental study conducted on Epinions dataset illustrates that our proposed scheme outperforms the traditional graph-based methods.

Soft consensus model for the group fuzzy AHP decision making

The fuzzy analytic hierarchy process (AHP) is an extension to the classical AHP that enables dealing with the impreciseness and vagueness of judgments. It has been frequently used for handling complex decision making problems that demand a group rather than a single decision maker. Group decision making aggregates the judgments of individuals into a joint decision. Although consensus is the desired result in group decision making, it is difficult to achieve due to the diversity of opinions, knowledge and experiences of the decision makers. Therefore, the concept of soft consensus can be applied. We propose a new soft consensus based model for fuzzy AHP group decision making. The judgments in the model are presented as triangular fuzzy numbers. The closeness between judgments of two decision makers is measured by the individual fuzzy consensus index which in turn is based on the compatibility index from classical AHP. In each iteration, two decision makers with the most dissimilar opinions are identified and their judgments are adapted. The process is repeated until the desired consensus level is reached. The model can also take into account the weights of importance of individual decision makers. A fuzzy extension of the geometric mean method is employed for deriving fuzzy weights from a group fuzzy pairwise comparison matrix. The application of the model is provided in an example from the literature.

Ratio-based similarity analysis and consensus building for group decision making with interval reciprocal preference relations

Similarity analysis and preference information aggregation are two important issues for consensus building in group decision making with preference relations. Pairwise ratings in an interval reciprocal preference relation (IRPR) are usually regarded as interval-valued And-like representable cross ratios (i.e., interval-valued cross ratios for short) from the multiplicative perspective. In this paper, a ratio-based formula is introduced to measure similarity between a pair of interval-valued cross ratios, and its desirable properties are provided. We put forward ratio-based similarity measurements for IRPRs. An induced interval-valued cross ratio ordered weighted geometric (IIVCROWG) operator with interval additive reciprocity is developed to aggregate interval-valued cross ratio information, and some properties of the IIVCROWG operator are presented. The paper devises an importance degree induced IRPR ordered weighted geometric operator to fuse individual IRPRs into a group IRPR, and discusses the derivation of its associated weights. By employing ratio-based similarity measurements and IIVCROWG-based aggregation operators, a soft consensus model including a generation mechanism of feedback recommendation rules is further proposed to solve group decision making problems with IRPRs. Three numerical examples are examined to illustrate the applicability and effectiveness of the developed models.

Soft consensus measures in group decision making using unbalanced fuzzy linguistic information

An important question in group decision-making situations is how to estimate the consensus achieved within the group of decision makers. Dictionary meaning of consensus is a general and unanimous agreement among a group of individuals. However, most of the approaches deal with a more realistic situation of partial agreement. Defining a partial agreement of decision makers as a consensus up to some degree, the following question is how to obtain that soft degree of consensus. To do so, different approaches, in which the decision makers express their opinions by using symmetrical and uniformly distributed linguistic term sets, have been proposed. However, there exist situations in which the opinions are represented using unbalanced fuzzy linguistic term sets, in which the linguistic terms are not uniform and symmetrically distributed around the midterm. The aim of this paper was to study how to adapt the existing approaches obtaining soft consensus measures to handle group decision-making situations in which unbalanced fuzzy linguistic information is used. In addition, the advantages and drawbacks of these approaches are analyzed.

Consensus in a Fuzzy Environment: A Bibliometric Study

Abstract In today's organizations, group decision making has become a part of everyday organizational life. It involves multiple indi- viduals interacting to reach a decision. An important question here is the level of agreement or consensus achieved among the individuals before making the decision. Traditionally, consensus has been meant to be a full and unanimous agreement. How- ever, it is often not reachable in practice. A more reasonable approach is the use of softer consensus measures, which assess the consensus in a more flexible way, reflecting the large spectrum of possible partial agreements and guiding the discussion process until widespread agreement is achieved. As soft consensus measures are more human-consistent in the sense that they better reflect a real human perception of the essence of consensus, consensus models based on these kind of measures have been widely proposed. The aim of this contribution is to present a bibliometric study performed on the consensus approaches that have been proposed in a fuzzy environment. It gives an overview about the research products gathered in this research field. To do so, several points have been studied, among others: countries, journals, top contributing authors, most cited keywords, papers and authors. This allows us to show a quick shot of the state of the art in this research area.

Do we adapt so slowly that the public loses the privilege of running its own system?

There is a soft consensus on what the health system׳s characteristics should be, the firmer view on the recurring barriers to change, and how all of this, save for the digital backbone element, could have been written 30 years ago. It is this chronic system stasis when change is urgently needed that casts doubt on the public administration of health services.

Modeling consensus using logic-based similarity measures

One of the key issues when it comes to measuring similarity is the discrepancy that exists between the idealized measures and actual human perception. The aim of this paper is to explore the possibility of using logic-based similarity measures for modeling consensus. We propose a soft consensus model for calculating the consensus and proximity degrees on two different levels. The proposed model relies on logic-based similarity measures and the appropriate aggregation functions. It is a fresh approach as it includes logic when perceiving similarity. Several similarity measures based on min, product and Lukasiewicz fuzzy bi-implications are introduced for modeling consensus. We also define a measure of similarity based on interpolative Boolean algebra (IBA) equivalence, and provide its comprehensive theoretical background. In our approach, we analyze how these different logic-based measures treat similarity, and whether they are appropriate to explain the notion of consensus. Finally, we show that IBA equivalence is the only measure that is both appropriate for modeling consensus and interpretable at the same time. The proposed model is illustrated on a problem of project selection in the context of sustainable development and the numerical results are discussed.

Using Soft Consensus Clustering for Combining Multiple Clusterings of Chemical Structures

The consensus clustering has shown capability to improve the robustness, novelty and stability of individual clusterings in many areas including chemoinformatics. In this paper, graph-based consensus method (cluster-based similarity partitioning algorithm CSPA) and soft consensus clustering were examined for combining multiple clusterings of chemical structures. The clustering is evaluated based on the ability to separate active from inactive molecules in each cluster. Experiments suggest that the effectiveness of soft consensus method can obtain better results than the hard consensus method (CSPA).