An overview on feedback mechanisms with minimum adjustment or cost in consensus reaching in group decision making: Research paradigms and challenges

Zhang, Hengjie; Zhao, Sihai Dave; Kou, Gang; Li, Cong-Cong; Dong, Yucheng; Herrera, Francisco

doi:10.1016/j.inffus.2020.03.001

Cited by 237 publications

(51 citation statements)

References 104 publications

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“…142 However, in the existing studies of the ARAS method, there has not been much attention to the group consensus. In fact, in the consensus LIU AND XU | 3553 framework, there are many technologies that can be combined with the ARAS method, such as the consensus measuring, 143 the consensus reaching mechanism, 144 the response to the noncooperative behavior, 145 large-sale consensus, 146 and so forth. From adding consensus framework to the ARAS method, the ARAS-based GDM method will be more diverse, meanwhile, it will enhance the reliability of the decision outcomes.…”

Section: Challenges On the Consensus Among The Expertsmentioning

confidence: 99%

An overview of ARAS method: Theory development, application extension, and future challenge

Liu

Zhang

2021

Int J Intell Syst

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Multi‐attribute decision‐making (MADM) is one of the most important parts in decision‐making theory, and related research is becoming more and more popular over the past few years. Investigating that the information could be qualitative and quantitative, and the different measurement units cause difficulties in some MADM problems, the additive ratio assessment system (ARAS) method was proposed. The method tries to solve MADM problems through a simple way efficiently, and at the same time eliminates the influence of different measurement units. Till now, the method has received extensive attention and has been extended to different information environments and application fields. To know about the development of the method and improve the method efficiently, this paper reviews the studies on the ARAS method from the perspectives of basic information (including the bibliometrics analyses and the outline of ARAS method), the development on theory (including the development on MADM mechanism, different information environments, and combination with different methods), the development on the application and the future challenge. From the overview, the basic situations and the development of the ARAS method are presented clearly, and the analyses of the challenges can also provide useful and sufficient instructions for the future application and improvement of the method.

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Section: Challenges On the Consensus Among The Expertsmentioning

confidence: 99%

An overview of ARAS method: Theory development, application extension, and future challenge

Liu

Zhang

2021

Int J Intell Syst

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“…Other multi-criteria group decision making (GDM) approaches, such as Pythagorean m-polar fuzzy soft sets (Riaz and Tehrim 2020a, b, c), the nearest consistent metrics (NCM)-based GDM (Lin et al 2020a), the soft consensus-based GDM (Zhang et al 2019), minimum adjustment-based GDM (Zhang et al 2020), non-cooperative-based GDM (Chao et al 2021) and more sophisticated algorithms for individual judgments aggregation (Kou et al 2014;Lin et al 2020b), may have greater flexibility to handle data with imprecise messages and also identify specific directions for subsidiary supervision and management.…”

Section: Conclusion and Future Researchmentioning

confidence: 99%

Identifying the key factors of subsidiary supervision and management using an innovative hybrid architecture in a big data environment

Hsu

Chen

et al. 2021

Financ Innov

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In a highly intertwined and connected business environment, globalized layout planning can be an effective way for enterprises to expand their market. Nevertheless, conflicts and contradictions always exist between parent and subsidiary enterprises; if they are in different countries, these conflicts can become especially problematic. Internal control systems for subsidiary supervision and management seem to be particularly important when aiming to align subsidiaries’ decisions with parent enterprises’ strategic intentions, and such systems undoubtedly involve numerous criteria/dimensions. An effective tool is urgently needed to clarify the relevant issues and discern the cause-and-effect relationships among them in these conflicts. Traditional statistical approaches cannot fully explain these situations due to the complexity and invisibility of the criteria/dimensions; thus, the fuzzy rough set theory (FRST), with its superior data exploration ability and impreciseness tolerance, can be considered to adequately address the complexities. Motivated by efficient integrated systems, aggregating multiple dissimilar systems’ outputs and converting them into a consensus result can be useful for realizing outstanding performances. Based on this concept, we insert selected criteria/dimensions via FRST into DEMATEL to identify and analyze the dependency and feedback relations among variables of parent/subsidiary gaps and conflicts. The results present the improvement priorities based on their magnitude of impact, in the following order: organizational control structure, business and financial information system management, major financial management, business strategy management, construction of a management system, and integrated audit management. Managers can consider the potential implications herein when formulating future targeted policies to improve subsidiary supervision and strengthen overall corporate governance.

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“…Hence, assisting FMEA members in reaching an acceptable consensus degree is essential. As far as we know, consensus building has been reported in numerous GDM contexts, [45][46][47][48][49][50][51][52][53][54][55][56][57] but it has been ignored in most FMEA researches, except in Zhang et al 14,21 Therefore, we construct a consensus-building process to improve the readability management quality. Meanwhile, we design a novel feedback adjustment process by considering the FMEA members' psychological behaviours modelled by prospect theory to help FMEA members reach consensus.…”

Section: Description Of the Consensus-based Linguistic Fmea Problemmentioning

confidence: 99%

Consensus building in linguistic failure mode and effect analysis: A perspective based on prospect theory

Fan

Zhu

Wei

et al. 2020

Quality & Reliability Eng

Self Cite

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As one of many scientific and efficient risk assessment approaches, failure mode and effect analysis (FMEA) has been widely applied across various fields. There are two core issues in the FMEA approach: identifying the latent failure modes of the systems, products, processes and services and the risk assessment and the prioritization of those failure modes. Then, corrective measures must be taken in a timely and accessible manner to prevent the occurrence of failure modes with higher risk levels. In practice, several FMEA members from different fields are usually involved in the FMEA implementation process; the risk assessment information given by them may vary greatly. Therefore, it is necessary to integrate a consensus-building process into FMEA. Meanwhile, the psychological behaviours of FMEA members have had a great impact on the final prioritization of failure modes. Prospect theory is an effective approach for describing individual psychological behaviours. Therefore, this paper presents a novel linguistic FMEA approach to address the consensus issue from the perspective of prospect theory. In the proposed linguistic FMEA approach, a consensus measurement approach based on prospect theory is constructed. Then, a novel feedback adjustment mechanism is designed in which FMEA members can adjust not only their assessment information but also their reference points to achieve an acceptable consensus degree. Eventually, a practical application is used to show the validity and applicability of the proposed linguistic FMEA approach.

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An overview on feedback mechanisms with minimum adjustment or cost in consensus reaching in group decision making: Research paradigms and challenges

Cited by 237 publications

References 104 publications

An overview of ARAS method: Theory development, application extension, and future challenge

An overview of ARAS method: Theory development, application extension, and future challenge

Identifying the key factors of subsidiary supervision and management using an innovative hybrid architecture in a big data environment

Consensus building in linguistic failure mode and effect analysis: A perspective based on prospect theory

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