A Multi-Agent Linguistic-Style Large Group Decision-Making Method Considering Public Expectations

Zhu, Guiju; Cai, Chenguang; Pan, Bin; Wang, Pei

doi:10.1007/s44196-021-00037-6

Cited by 6 publications

(3 citation statements)

References 40 publications

(60 reference statements)

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“…For a certain number of DMs, the number of consensus iterations increases with the number of alternatives; however, the rate In future studies, we can incorporate DMs into a social network to explore the influence of group interaction on decision-making and consensus processes [40][41][42]. Focusing on the application of the GDM theory to practical decisionmaking problems is essential [43][44][45][46][47]. Therefore, we may also consider the GDM model using a data-driven method to achieve a more accurate degree of simulation.…”

Section: Discussionmentioning

confidence: 99%

Mining Personalized Individual Semantics of Self-confidence Participants in Linguistic Group Decision-Making

Jing

Chao

2022

Int J Comput Intell Syst

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Words representing individual preferences in group decision-making (GDM) are always associated with different meanings. Consequently, mining personalized semantics of decision-makers (DMs) hidden in preference expressions, and establishing a corresponding management mechanism, is an effective way to reach group consensus through computing with word methodology. However, the aforementioned consensus-reaching process may be hindered by self-confidence. To address this limitation, this study proposes a linguistic group decision model with self-confidence behavior. First, we identified the corresponding self-confidence levels for each DM. Next, we integrated different linguistic representation models into unified linguistic distribution-based models. We then obtained individual personalized semantics based on a consistency-driven optimization method, and designed a feedback-adjustment mechanism to improve the adjustment willingness of DMs and group consensus level. Finally, we conducted a quantitative experiment to demonstrate our model’s effectiveness and feasibility.

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Section: Discussionmentioning

confidence: 99%

Mining Personalized Individual Semantics of Self-confidence Participants in Linguistic Group Decision-Making

Jing

Chao

2022

Int J Comput Intell Syst

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“…To accommodate the subjective expression of the public's needs, it is crucial to choose a reasonable method for expressing public opinions. According to the existing literature, linguistic evaluation possesses characteristics such as easy operation, strong flexibility, and the ability to reflect subjective opinions, making it a common choice for expressing public opinions [10,11]. The high dispersion of public opinion primarily stems from differences between individuals, including variations in knowledge background, level of attention, and degree of interest.…”

Section: Introductionmentioning

confidence: 99%

Evidential-Reasoning-Type Multi-Attribute Large Group Decision-Making Method Based on Public Satisfaction

Cai,

Wang,

Wang

et al. 2024

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To address public participation-oriented, large group decision-making problems with uncertain attribute weights, we propose a multi-attribute decision-making method considering public satisfaction. Firstly, a large group is organized to provide their opinions in the form of linguistic variables. Public opinions can be categorized into two types based on their content: one reflects the effectiveness of an alternative implementation and the other reflects the public expectations. Secondly, the two types of public opinions are sorted separately by linguistic variables. The evaluation of alternatives and the evaluation of expectations in different attributes are determined, both of which are expressed in the form of linguistic distributions. These two evaluations are then compared to determine the public satisfaction of the attributes in different alternatives. Thirdly, based on the deviation of public satisfaction in different attributes, a weight optimization model is constructed to determine the attribute weights. Fourthly, leveraging the interval credibility of attribute satisfaction for various alternatives, an evidential reasoning non-linear optimization model is established to obtain the comprehensive utility evaluation value for each alternative, which is used for ranking. Finally, a numerical example is employed to validate the feasibility and effectiveness of the proposed approach. According to the results of the numerical example, it can be concluded that the proposed approach can be effectively applied to large group decision-making problems that consider public satisfaction. Based on the comparison of methods, the proposed approach has certain advantages in reflecting public opinions and setting reference points, which can ensure the reliability of the decision results.

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“…In practice, the most common way for expressing the fuzzy stable demand is to use the linguistic information. And the use of the linguistic information usually implies the demand of the techniques of computing with words (CW) [28][29][30][31][32][33]. In this paper, we will use the 2-tuple fuzzy linguistic representations [28] to deal with the linguistic information.…”

Section: Introductionmentioning

confidence: 99%

Two-Sided Matching Decision-Making in an Incomplete and Heterogeneous Context: A Optimization-Based Method

Qin

Fan

Liang

et al. 2022

Int J Comput Intell Syst

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Driven by the development of platform economy, two-sided matching decision-making (TSDM) has become one of the most important applications in the field of intelligent computation system. Many recommendation systems based on TSDM have facilitated our life. However, in the existing studies on TSDM, it is usually assumed that all the individuals provide their preference orderings accurately, and have the same stable demand. In this paper, we develop an optimization method to solve the TSDM problem with incomplete weak preference ordering and heterogeneous fuzzy stable demand (i.e., TSDM-IH method). First, based on the incomplete weak preference ordering by the individuals in the two-sided matching party, we calculate the expectation ordinal values for each individual. Then, we calculate the perceived difference matrices and the perceived value matrices for the individuals in the two-sided matching party. Next, we analyze the fuzzy expression on the fuzzy stable demand for each individual, and obtain the constraint for obtaining the stable alternatives. Furthermore, we develop an optimization model which maximizes the perceived values of individuals in the two-sided matching party. Finally, a numerical example is given to illustrate the feasibility of the TSDM-IH method.

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A Multi-Agent Linguistic-Style Large Group Decision-Making Method Considering Public Expectations

Cited by 6 publications

References 40 publications

Mining Personalized Individual Semantics of Self-confidence Participants in Linguistic Group Decision-Making

Mining Personalized Individual Semantics of Self-confidence Participants in Linguistic Group Decision-Making

Evidential-Reasoning-Type Multi-Attribute Large Group Decision-Making Method Based on Public Satisfaction

Two-Sided Matching Decision-Making in an Incomplete and Heterogeneous Context: A Optimization-Based Method

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