Browsing by Author "Liang, Haiming"

Now showing 1 - 9 of 9
  • Thumbnail Image
    ItemOpen Access
    Asynchronous Opinion Dynamics with Online and Offline Interactions in Bounded Confidence Model
    (European Social Simulation Association, ESSA, 2017-10-31) Ding, Zhaogang; Dong, Yucheng; Liang, Haiming; Chiclana, Francisco
    Nowadays, in the world, about half of the population can receive information and exchange their opinions with others in online environments (e.g. the Internet), while the other half obtain information and exchange their opinions in offline environments (e.g. face to face) (see eMarketer Report, 2016). The speed at which information is received and opinions are exchanged in online environments is much faster than in offline environments. To model this phenomenon, in this paper we consider online and offline as two subsystems in opinion dynamics, and there is asynchronization when the agents in these two subsystems update their opinions. We show that asynchronization strongly impacts the steady-state time of the opinion dynamics, the opinion clusters and the interactions between the online subsystem and offline subsystem. Furthermore, these effects are often enhanced the larger the size of the online subsystem.
  • Thumbnail Image
    ItemOpen Access
    Consensus Reaching with Minimum Cost of Informed Individuals and Time Constraints in Group Decision Making
    (IEEE, 2022-04-06) Liang, Haiming; Kou, Gang; Dong, Yucheng; Chiclana, Francisco; Herrera-Viedma, Enrique
    Consensus reaching process (CRP) is important and present in a wide range of application areas. In practical CRP, the managers (e.g., enterprise) often hire some informed individuals (e.g., persuaders) to promote the efficiency of consensus reaching. This paper proposes a CRP with minimum cost of informed individuals and time constraint in large-scale group decision making (LSGDM) with bounded confidence effects. The consensus model with bounded confidence effects (CBC model) is formulated. Then, desirable properties of the CBC model are discussed to facilitate its resolution. Next, an extended Particle Swarm Optimization (PSO) algorithm is designed to solve the CBC model. Finally, a numerical analysis, a comparison analysis and a simulation analysis are provided to illustrate the feasibility and effectiveness of the proposed approach.
  • Thumbnail Image
    ItemOpen Access
    Consensus Reaching with Time Constraints and Minimum Adjustments in Group with Bounded Confidence Effects
    (IEEEXplore, 2019-09-06) Liang, Haiming; Dong, Yucheng; Ureña, Raquel; Chiclana, Francisco; Herrera-Viedma, Enrique; Ding, Zhaogang
    In the bounded confidence model it is widely known that individuals rely on the opinions of their close friends or people with similar interests. Meanwhile, the decision maker always hopes that the opinions of individuals can reach a consensus in a required time. Therefore, with this idea in mind, this paper develops a consensus reaching model with time constraints and minimum adjustments in a group with bounded confidence effects. In the proposed consensus approach, the minimum adjustments rule is used to modify the initial opinions of individuals with bounded confidence, which can further influence the opinion evolutions of individuals to reach a consensus in a required time. The properties of the model are studied, and detailed numerical examples and comparative simulation analysis are provided to justify its feasibility.
  • Thumbnail Image
    ItemOpen Access
    Consistency improvement with a feedback recommendation in personalized linguistic group decision making
    (IEEEXplore, 2021-06-30) Li, Cong-Cong; Liang, Haiming; Dong, Yucheng; Chiclana, Francisco; Herrera-Viedma, Enrique
    Consistency is an important issue in linguistic decision making with various consistency measures and consistency improving methods available in the literature. However, existing linguistic consistency studies omit the fact that words mean different things for different people, that is, decision makers' personalized individual semantics (PISs) over their expressed linguistic preferences are ignored. Therefore, the aim of this article is to propose a novel consistency improving approach based on PISs in linguistic group decision making. The proposed approach combines the characteristics of personalized representation and integrates the PIS-based model in measuring and improving the consistency of linguistic preference relations. A detailed numerical and comparative analysis to support the feasibility of the proposed approach is provided.
  • Thumbnail Image
    ItemOpen Access
    Evolution of Credit Scores of Enterprises in a Social Network: A Perspective Based on Opinion Dynamics
    (IEEE, 2024-02-09) Liang, Haiming; Xu, Weijun; Chiclana, Francisco; Yu, Shui; Dong, Yucheng; Herrera-Viedma, Enrique
    The use of social network to model the evolution of credit scores of networked enterprises is still a challenging task. This article develops an opinion dynamics model of the evolution of credit scores of enterprises in a social network. Firstly, based on the number of potential cooperated enterprises and the initial credit scores, the leader and follower enterprises are identified. Then, taking into consideration the cooperated benefit and discrimination cost, the cooperated utility between any two enterprises is calculated, which is used to compute the weights that one enterprise assigns to other enterprises. An opinion dynamics model on the evolution of credit scores of enterprises, inspired on the classical Friedkin–Johnsen’s social network model, is developed. Some desirable properties of the proposed opinion dynamics model are theoretically stated and proved. Finally, a numerical example is provided to illustrate the feasibility of the proposed opinion dynamics model, while a simulation analysis to investigate the joint influences of the connection probabilities and the network structure on the evolution of credit scores of enterprises is reported.
  • Thumbnail Image
    ItemOpen Access
    Multiple attribute strategic weight manipulation with minimum cost in a group decision making context with interval attribute weights information
    (IEEEXplore, 2018-10-26) Liu, Yating; Dong, Yucheng; Liang, Haiming; Chiclana, Francisco; Herrera-Viedma, Enrique
    In multiple attribute decision making (MADM), strategic weight manipulation is understood as a deliberate manipulation of attribute weights setting to achieve a desired ranking of alternatives. In this paper, we study the strategic weight manipulation in a group decision making context with interval attribute weights information. In group decision making, the revision of the decision makers’ original attribute weights information implies a cost (the difference between the original information and the revised one). Driven by a desire to minimize the cost, we propose the minimum cost strategic weight manipulation model, which is achieved via optimization approaches, with the 0-1 mixed linear programming model being proved appropriate in this context. Meanwhile, some desired properties to manipulate a strategic attribute weight based on the ranking range under interval attribute weights information are proposed. Finally, numerical analysis and simulation experiments are provided with a two-fold aim: (1) to verify the validity of the proposed models, and (2) to show the effects of interval attribute weights information and the unit cost, respectively, on the cost to manipulate strategic weights in the MADM in a group decision context.
  • Thumbnail Image
    ItemEmbargo
    An opinion control rule with minimum adjustments to support the consensus reaching in bounded confidence model
    (Elsevier, 2016) Dong, Yucheng; Chiclana, Francisco; Herrera-Viedma, Enrique; Cabrerizo, F. J.; Ding, Zhaogang; Liang, Haiming
    Opinion dynamics provides a modeling tool for the public opinion management. The existing studies mainly focused on building the evolution model of opinions. However, the control of public opinions has been a key problem in practical opinion dynamics. The objective of this paper is to propose an opinion control rule to support the consensus reaching. Based on the bounded confidence model, the consensus model with the minimum adjustment is proposed. Next, based on the proposed consensus model, we propose the opinion control rule to support the consensus reaching. Furthermore, a numerical example is given to illustrate the feasibility of the proposed opinion control rule. Through simulation experiments, we investigate the effects of adjustment thresholds and bounded confidences on the opinion control rule.
  • No Thumbnail Available
    ItemEmbargo
    Optimal resources allocation to support the consensus reaching in group decision making
    (Elsevier, 2024-04-30) Fan, Sha; Liang, Haiming; Li, Cong-Cong; Chiclana, Francisco; Pedrycz, Witold; Dong, Yucheng
    In group decision making (GDM), the minimum cost consensus model (MCCM) to assist a group to reach a consensus with the minimum cost has gained widespread attention. However, determining the unit costs for adjusting decision makers’ opinions in the MCCM is a challenging problem that limits its practical applications. Meanwhile, the MCCM is not modeled as a resources allocation problem in an explicit manner, and the opinions in the MCCM do not represent utilities/satisfactions, leading to the unclear implications of opinions’ adjustments. To overcome these limitations of the MCCM, this paper proposes the optimal resources allocation consensus model (ORACM) to assist the moderator to allocate resources without determining unit costs to support consensus reaching, through the introduction of the resources allocation problem and utility functions in its modeling. Furthermore, we present a theoretical analysis framework to reveal the properties of the ORACM and the connection between the ORACM and the MCCM, justifying the theoretical advantages of the ORACM. Moreover, the ORACM is applied to the transboundary river pollution control negotiations of Sichuan's Tuojiang River, and the effectiveness and feasibility of the ORACM are further validated with detailed simulation and comparison analyses.
  • Thumbnail Image
    ItemOpen Access
    Strategic weight manipulation in multiple attribute decision making
    (Elsevier, 2017-03-18) Dong, Yucheng; Liu, Yating; Liang, Haiming; Chiclana, Francisco; Herrera-Viedma, Enrique
    In some real-world multiple attribute decision making (MADM) problems, a decision maker can strategically set attribute weights to obtain her/his desired ranking of alternatives, which is called the strategic weight manipulation of the MADM. In this paper, we define the concept of the ranking range of an alternative in the MADM, and propose a series of mixed 0-1 linear programming models (MLPMs) to show the process of designing a strategic attribute weight vector. Then, we reveal the conditions to manipulate a strategic attribute weight based on the ranking range and the proposed MLPMs. Finally, a numerical example with real background is used to demonstrate the validity of our models, and simulation experiments are presented to show the better performance of the ordered weighted averaging operator than the weighted averaging operator in defending against the strategic weight manipulation of the MADM problems.