Social Network Group Decision Research Articles

Consensus reaching with trust evolution in social network group decision making

The trust degrees among individuals play an important role in social network group decision making (SNGDM). They are assumed to be unchanged with the time in most of extant SNGDM studies. However, in some SNGDM scenarios, the trust degrees of individuals will evolve with the time due to the changes of the opinion similarities among individuals. In this paper, we discuss the consensus reaching issue in SNGDM with trust evolution. Specifically, we first formulate a consensus reaching problem with trust evolution, and then propose its resolution framework. In this framework, we establish the trust evolution model, in which the trust degrees among individuals at the next time are determined by the individuals’ historical trust degrees and the opinion similarities at this time. Based on this, we design a novel consensus reaching process, called CRP-OD-TE, which first includes the opinion dynamics-based endogenous feedback mechanism, and the trust evolution-based exogenous feedback mechanism. Furthermore, two applications are provided to illustrate the proposed CRP-OD-TE. Finally, we complete some simulations and comparative analysis. The results show that (1) the introduction of opinion dynamics into CRP as the endogenous feedback mechanism can accelerate the process of consensus reaching; and (2) the evolution of trust degrees has a significant impact on the group consensus reaching, which may be beneficial or destructive.

Social Network Group Decision-Making Method Based on Q-Rung Orthopair Fuzzy Set and Its Application in the Evaluation of Online Teaching Quality

As q-rung orthopair fuzzy set (q-ROFS) theory can effectively express complex fuzzy information, this study explores its application to social network environments and proposes a social network group decision-making (SNGDM) method based on the q-ROFS. Firstly, the q-rung orthopair fuzzy value is used to represent the trust relationships between experts in the social network, and a trust q-rung orthopair fuzzy value is defined. Secondly, considering the decreasing and multipath of trust in the process of trust propagation, this study designs a trust propagation mechanism by using its multiplication operation in the q-ROFS environment and proposes a trust q-ROFS aggregation approach. Moreover, based on the trust scores and confidence levels of experts, a new integration operator called q-rung orthopair fuzzy-induced ordered weighted average operator is proposed to fuse experts’ evaluation information. Additionally, considering the impact of consensus interaction on decision-making results, a consensus interaction model based on the q-ROF distance measure and trust relationship is proposed, including consistency measurement, identification of inconsistent expert decision-making opinions and a personalized adjustment mechanism. Finally, the SNGDM method is applied to solve the problem of evaluating online teaching quality.

An Optimal Feedback Model to Prevent Manipulation Behavior in Consensus Under Social Network Group Decision Making

In this article, a novel framework to prevent manipulation behavior in consensus reaching process under social network group decision making is proposed, which is based on a theoretically sound optimal feedback model. The manipulation behavior classification is twofold: first, “ individual manipulation ” where each expert manipulates his/her own behavior to achieve higher importance degree (weight); and second, “ group manipulation ” where a group of experts force inconsistent experts to adopt specific recommendation advices obtained via the use of a fixed feedback parameter. To counteract “ individual manipulation ,” a behavioral weights assignment method modeling sequential attitude ranging from “ dictatorship ” to “ democracy ” is developed, and then a reasonable policy for group minimum adjustment cost is established to assign appropriate weights to experts. To prevent “ group manipulation ,” an optimal feedback model is investigated where objective function is the individual adjustments cost and constraints related to the group threshold of consensus. This approach allows the inconsistent experts to balance group consensus and adjustment cost, which enhances their willingness to adopt the recommendation advices and consequently the group reaching consensus on the decision-making problem at hand. A numerical example is presented to illustrate and verify the proposed optimal feedback model.

An efficient consensus reaching framework for large-scale social network group decision making and its application in urban resettlement

Urban resettlement projects involve a large number of stakeholders and impose tremendous cost. Developing resettlement plans and reaching an agreement amongst stakeholders about resettlement plans at a reasonable cost are some of the key issues in urban resettlement. From this perspective, urban resettlement is a typical large-scale group decision-making (GDM) problem, which is challenging because of the scale of participants and the requirement of high consensus levels. Observing that residents who are affected by a resettlement project often have tight social connections, this study proposes a framework to improve the consensus reaching and uses the minimum consensus cost to reduce the total cost for urban resettlement projects with more than 1000 participants. Firstly, we construct a network topology that consists of two layers to deal with incomplete social relationships amongst large-scale participants. An inner layer consists of participants whose preference similarities and trust relations are known. Meanwhile, an outside layer includes participants whose trust relations cannot be determined. Secondly, we develop a classification method to classify participants into small subgroups based on their preference similarities. We can then connect the participants whose trust relations are unknown (the outside layer) with the ones in the inner layer using the classification results. To facilitate effective consensus reaching in large-scale social network GDM, we develop a three-step approach to reconcile conflicting preferences and accelerate the consensus process at the minimum cost. A real-life urban resettlement example is used to validate the proposed approach. Results show that the proposed approach can reduce the total consensus cost compared with the other two practices used in the actual urban resettlement operations.

Sustainable competitiveness evaluation of container liners based on granular computing and social network group decision making

In the shipping industry, the sustainability of the shipping companies has received more attention besides the economic benefits. A suitable evaluation model can provide a credible sustainable improvement test and guide the operation and management of shipping companies. This study is concerned with a granular evaluation model for the sustainable competitiveness evaluation of container liners, where the interval linguistic version of the analytic hierarchy process is designed for comparing alternatives comprehensively. To obtain ideal consistent preference relations, we propose a modification method by constructing information granules and using particle swarm optimization. Moreover, based on the combination of individual preference relations and experts’ weights obtained from the social network, the aggregated overall opinion of alternatives is achieved. Furthermore, a case study evaluating the sustainable competitiveness of container liners is conducted to support the proposed evaluation framework’s feasibility and practicality.

Social Trust Driven Consensus Reaching Model With a Minimum Adjustment Feedback Mechanism Considering Assessments-Modifications Willingness

Social network group decision making (SNGDM) has emerged as a new decision tool to effectively model the social trust relationships among decision makers. The impact of the social trust relationships on assessments-modifications in the consensus reaching in the SNGDM is seldom considered. This study aims at addressing this issue. The main starting point is the assumption that a decision maker will not be willing to accept the assessments-modifications suggestions that significantly differ from his/her trusted decision makers’ assessments in a social trust network. Thus, this study proposes a social trust driven minimum adjustments consensus model (STDMACM) for SNGDM. Simultaneously, a social trust driven consensus maximum optimization model (STDCMOM) is proposed for maximizing the consensus level among decision makers under the above assumption. Based on both STDCMOM and STDMACM, an interactive consensus reaching process is presented, in which the assessments-modifications suggestions generated from the STDMACM are used, when the maximum consensus level obtained from STDCMOM is acceptable, as the references for guiding the consensus reaching; otherwise, assessments-modifications suggestions are generated from the designed STDCMOM. The validity of the social trust driven consensus reaching process with respect to its consensus convergence rate and consensus success ratio is verified with a simulation and comparison analysis.

Consensus Model with Double Feedback Mechanism Based on Dynamic Trust Relationship in Social Network Group Decision-Making

Consensus Model with Double Feedback Mechanism Based on Dynamic Trust Relationship in Social Network Group Decision-Making

A knowledge coverage-based trust propagation for recommendation mechanism in social network group decision making

Trust is a typical relationship in social network, which in group decision making problems relates to the inner relationship among experts. To obtain a complete trust relationship of a networked group of experts, firstly, a novel knowledge coverage-based trust propagation operator is proposed to estimate the trust relationship between pairs of unknown experts. The novelty of this trust propagation operator resides in its account of the domain knowledge coverage of experts. Desirable properties regarding boundary conditions, generalisation and knowledge coverage absorption are studied. The comparison with existing operators of boundary conditions shows the rationality of the proposed operator. Next, a knowledge coverage-based multi-paths trust propagation model for constructing complete trust network is investigated. The proposed approach aggregates all trust paths to collect all trust information and penalise trust decay. Secondly, a trust order induced recommendation mechanism is proposed by combining subjective and objective weights. Thus, experts can accept consensus recommendations by subjective and objective trust. This recommendation mechanism allows the inconsistent experts to accept the advices they trust. The validity and rationality of the proposed recommendation mechanism is mathematically proved, and a numerical example is utilised to illustrate the calculation process of the proposed method.

A minimum adjustment consensus framework with compromise limits for social network group decision making under incomplete information

In consensus-based social network group decision making (SN-GDM) problems, experts tend to refuse modifications if they exceed the limit of their maximum compromise, which may lead to a failed consensus. However, few consensus models have addressed the compromise limit behaviors in SN-GDM. In this paper, we propose a minimum adjustment consensus framework with compromise limits for SN-GDM under incomplete information. First, a two-stage transformation method is proposed to indirectly estimate the social influence based on the preference orderings of experts, and experts’ weights are obtained from the constructed social network using social network analysis techniques. Then, a nonlinear optimization model based on social influence is developed to complete incomplete preferences. After obtaining the experts’ weights and completed preferences, we present a novel feedback mechanism to facilitate the consensus-reaching. The feedback mechanism establishes a minimum adjustment model with compromise limits to generate recommendations for inconsistent experts. Finally, an illustrative example and a comparative study are conducted to show the validity and advantages of the proposed consensus framework.

A maximum self-esteem degree based feedback mechanism for group consensus reaching with the distributed linguistic trust propagation in social network

This paper focuses on consensus reaching process (CRP) under social network in which the trust relationship expressed by linguistic information. A new feedback mechanism in social network group decision making (SN-GDM) is proposed, which mainly consists of the following two aspects: (1) The propagation of distributed linguistic trust is investigated to study trust relation among experts; (2) A maximum self-esteem degree based feedback mechanism is developed to produce personalized advice for reaching higher group consensus. To do so, a novel linguistic trust propagation method is proposed to obtain the complete trust relationship among group. The self-esteem degree is used to define the extent that an individual makes concessions. Then, a maximum self-esteem degree based optimal feedback mechanism is built to produce personalized advice to help inconsistent experts make change of their opinion. Its novelty lies in the establishment of an optimization model with the nonlinear group self-esteem degree function as the objective function while group consensus threshold as the restrictions. Therefore, the inconsistent experts will reach a group consensus with the minimum loss of self-esteem degree, and then, it achieves the optimal balance between individual self-esteem and group consensus. Finally, a ranking process is applied to derive the appropriate consensus solution.

Consensus reaching for social network group decision making by considering leadership and bounded confidence

With the rapid development of information, communication and techniques, social network group decision making problems which allow information exchange and communication among experts are more and more common in recent years. How to use social relationships generated by social networks to promote consensus among experts has been becoming a hot topic in the field of group decision making. In this paper, we consider a new type of group decision making problems in which experts will provide his/her interval fuzzy preference relations over alternatives under social network environment and propose a new model to help experts reach consensus. In the proposed model, we first define the individual consensus measure and the group consensus measure, and then use a network partition algorithm to detect sub-networks of experts, based on which the leadership of experts can be identified. Afterwards, by considering the leadership and the bounded confidence levels of experts, a new feedback mechanism which can provide acceptable advice to experts who need to modify their opinions is devised and a consensus reaching algorithm is further developed. To demonstrate the performance of the proposed consensus model and algorithm, a hypothetical application and some simulation analysis are provided eventually.

Managing classification-based consensus in social network group decision making: An optimization-based approach with minimum information loss

This study proposes a classification-based consensus framework in social network group decision making, which aims to classify alternatives into several ordinal classes from best to worst. In the classification-based consensus framework, a maximum consensus-based optimization model is devised to determine the weight of decision makers by linearly combining three reliable sources: in-degree centrality, consistency and similarity indexes. This is done by maximizing the consensus level among decision makers regarding the collective classification of alternatives. Following this, a minimum information loss-based optimization model is constructed to generate the consensual collective classification of alternatives. It seeks to minimize the information loss between the additive preference relations provided by decision makers and their preference vectors. Particularly, the proposed optimization models are converted into 0–1 mixed linear programming models to easily find their optimal solutions. Finally, a numerical example and a detailed comparison analysis are provided to show the effectiveness of the proposed approach.

Water–Energy–Food nexus evaluation with a social network group decision making approach based on hesitant fuzzy preference relations

With the rapid increase and development of global population and economic, the Water–Energy–Food (WEF) nexus evaluation which is related to the sustainable development of human has become a hotspot. Whereas, the study of the WEF nexus evaluation from the perspectives of social network group decision making (SNGDM) is still a challenge. Hence, this paper aims to develop a trust-based SNGDM approach with hesitant fuzzy preference relations to the WEF nexus evaluation. In the proposed model, a new fuzzy adjacency matrix and trust score matrix based on expert’s self-confidence are defined to imply experts’ trust relationship and trust score, respectively. To improve the reliability of the final decision(s), an iterative algorithm is presented to improve the consistency of experts’ evaluations. Subsequently, the individual evaluation can be aggregated into a group one by using the trust score induced ordered weighted averaging operator while the trust scores of experts are the induced factors. Additionally, an algorithm is utilized to achieve a high level consensus in SNGDM of the WEF nexus evaluation. Finally, some comparison analyses and discussions show the feasibility and validity of the proposed method.

Particle swarm optimization for trust relationship based social network group decision making under a probabilistic linguistic environment

Group decision making (GDM) problems require consensus reaching processes; however, these can be time consuming and costly. As experts change their evaluations after exchanging opinions and being influenced by others, these influences are spread across the various expert trust relationships. Because of the experts’ knowledge limits, the evaluations on the alternatives and the trust relationships are generally described using probabilistic linguistic terms. Therefore, to simplify the decision making process and avoid decision bias, this paper proposes a particle swarm optimization method that incorporates a trust relationship based social network for GDM under a probabilistic linguistic environment. Each expert is regarded as a particle that moves toward the final evaluation and reaches the threshold. A fitness function is built to measure the consensus levels, and the updated function is improved by the trust relationships to derive the new evaluations. A numerical example is then given to illustrate the feasibility of the proposed approach and comparisons given to further elucidate its novelty and validity.

A Voting Mechanism Designed for Talent Shows in Mass Media: Weighted Preference of Group Decision Makers in Social Networks Using Fuzzy Measures and Choquet Integral

As the development of social networks tends to shape people’s view about choices, decision making theories are challenged by numerous unprecedented difficulties, from both the theories and practice. One hot topic is how to design a voting mechanism for talent shows in mass media that not only attracts public attention but also reflects an objective and fair principle. Weighted voting, where the voting power of a representative is proportional to the population in his or her district, has been widely adopted in legislative selections and talent show competitions. However, weighted voting system may cause disenfranchisement of some representatives and reduce the entertainment and interest of talent shows because of the ignorance of complex interactions among the representatives. In this paper, possible interactions among representatives are analyzed by investigating the associated social networks and subsequently some fuzzy measures are utilized to quantify these interactions. Specifically, the weights determination model is adopted in this situation for defining fuzzy measures to avoid the disenfranchisement, and a multiple-group hierarchy decision model is developed to solve social network group decision making problems where the Choquet integral is employed to reduce the impact from synergy and redundancy between representatives. Moreover, a voting mechanism for talent shows in mass media is provided. Finally, an illustrative example, and a close look at the current algorithmic issues and future trends from different angles are provided.

Reaching a minimum adjustment consensus in social network group decision-making

In this paper, we propose a minimum adjustment consensus framework for the social network group decision-making (SN-GDM) with incomplete linguistic preference relations (ILPRs). The extant studies ignore the influence of network structure on the decision-makers’ (DMs’) weights, and set a fixed parameter to adjust DM’s preferences that may lead to the inefficiency of reaching a consensus. To solve these issues, we first propose a weight allocation method with the structural hole theory by analyzing the tie strength and topology structure of DM’s social networks. After obtaining DMs’ weights, the consistency/consensus indexes at three levels are constructed and used to identify the inconsistent DMs. Then, a novel minimum adjustment consensus model (MACM) for ILPRs is proposed to obtain the optimal adjustment parameters, which are used to recommend customized adjustments in the feedback mechanism. The existence of optimal solutions and the convergence of the proposed consensus models under certain conditions are also proved. Finally, the validity of the proposed method is verified by an application example. Different from the extant MACMs, we optimized the adjustment parameters just for inconsistent DMs instead of all DMs’ adjusted preference values. With less number of consensus rounds and lower costs, we also improved the classical feedback mechanism and established its connection with the current MACMs.

An Extension of Social Network Group Decision-Making Based on TrustRank and Personas

With the development of social networking big data, social network group decision-making (SN-GDM) has been widely applied in many fields. This paper focuses on three main components: (1) the determination of the decision makers’ (DMs) weights based on different social influence; (2) the anti-deception mechanism; and (3) the persona method. We introduce the TrustRank algorithm and the persona method into SN-GDM. Based on the TrustRank algorithm, both trusted and deceptive DMs in a seed set are artificially identified and given initial static scores to derive the influence of each DM. Additionally, the persona method is introduced to cluster DMs and achieve personalized decision-making. Further, we present a numerical example and comparison to demonstrate the efficiency of the framework in coping with non-socially shared preferences in SN-GDM. As expected, our findings indicate that our framework reduces the influence of deceptive DMs on the decision results.

An Optimization-Based Approach to Social Network Group Decision Making with an Application to Earthquake Shelter-Site Selection.

The social network has emerged as an essential component in group decision making (GDM) problems. Thus, this paper investigates the social network GDM (SNGDM) problem and assumes that decision makers offer their preferences utilizing additive preference relations (also called fuzzy preference relations). An optimization-based approach is devised to generate the weights of decision makers by combining two reliable resources: in-degree centrality indexes and consistency indexes. Based on the obtained weights of decision makers, the individual additive preference relations are aggregated into a collective additive preference relation. Further, the alternatives are ranked from best to worst according to the obtained collective additive preference relation. Moreover, earthquakes have occurred frequently around the world in recent years, causing great loss of life and property. Earthquake shelters offer safety, security, climate protection, and resistance to disease and ill health and are thus vital for disaster-affected people. Selection of a suitable site for locating shelters from potential alternatives is of critical importance, which can be seen as a GDM problem. When selecting a suitable earthquake shelter-site, the social trust relationships among disaster management experts should not be ignored. To this end, the proposed SNGDM model is applied to evaluate and select earthquake shelter-sites to show its effectiveness. In summary, this paper constructs a novel GDM framework by taking the social trust relationship into account, which can provide a scientific basis for public emergency management in the major disasters field.

Social network group decision making: Managing self-confidence-based consensus model with the dynamic importance degree of experts and trust-based feedback mechanism

Due to the development of intelligent decision-making, social network group decision-making (SNGDM) has become increasingly valued. Generally, real SNGDM cases involve not only the mathematical formulation of the social network analysis but also the experts’ psychological behaviors. Self-confidence, an expert's psychological implication of self-statement, is a significant topic in SNGDM problems, while it is overlooked in most existing research. To address this issue, this study takes experts’ self-confidence into account in SNGDM. All experts use self-confident fuzzy preference relations (SC-FPRs) to express their opinions. Subsequently, we have developed a novel self-confidence-based consensus approach for SNGDM with SC-FPRs. A dynamic importance degree of experts which combines the external trust and internal self-confidence is proposed to determine their weights. A consensus index considering self-confidence is defined to assess the consensus level among experts. Meanwhile, a trust-based feedback mechanism is presented to improve the consensus efficiency. The rule of the feedback mechanism is that experts dynamically adjust their self-confidence levels while revising the preferences. Using a self-confidence score function, an alternative that has the highest self-confidence score can be selected as the best solution. An illustrative example and some comparisons are given to verify the feasibility and effectiveness of the proposed method.

A trust propagation and collaborative filtering based method for incomplete information in social network group decision making with type-2 linguistic trust

Abstract A theoretical framework to deal with incomplete preference information in social network group decision making (SN-GDM) is put forward. Firstly, the concept of the type-2 linguistic trust function is defined to model direct trust relationship of group experts in SN-GDM. It adopts the bilattice structure with of trust/distrust values that are all described by distributed linguistic function. Secondly, the type-2 uninorm trust propagation operator is investigated to connect the indirect trust relationship between group experts. The path-ordering induced order weighted averaging (P-IOWA) operator is proposed to aggregate to multiple trust propagation paths into a collective one. Then, the completed trust relationship between group experts is constructed to estimate the incomplete preference information in decision matrix. Combining the social network trust with the collaborative filtering, a comprehensive estimation method for incomplete information is proposed. In detail, the estimation method based the social network trust uses the actual reputation between experts as ‘historic’ actions. While the estimation method by the collaborative filtering utilizes the similar degree of decision as ‘current’ information. Then, the proposed comprehensive estimation method resembles the Bayesian approach, and consequently it can enhance the reliability for the evaluation of the incomplete elements in GDM. Hence, a consensus reaching process for GDM with incomplete preference information is proposed. Finally, an example is given to illustrate the use of the proposed method.