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An objective and interactive‐information‐based feedback mechanism for the consensus‐reaching process considering a non‐support degree for minority opinions
Author(s) -
Nie Ruxin,
Tian Zhangpeng,
Wang Jianqiang,
Luo Hanyang
Publication year - 2020
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/exsy.12543
Subject(s) - computer science , quality (philosophy) , unanimity , group decision making , process (computing) , expression (computer science) , mechanism (biology) , function (biology) , operations research , law , psychology , social psychology , political science , mathematics , philosophy , epistemology , evolutionary biology , biology , programming language , operating system
The consensus‐reaching process (CRP) to achieve higher unanimity and ensure common agreement before deriving a final decision has become an important procedure in group decision‐making problems. The demand for high‐quality decision results has motivated the development of large‐scale group decision‐making (LGDM). In such cases, the issue of minority opinion has gained awareness due to the related effects on enhancing consensus and decision quality. A minority opinion cannot exert an effect unless the majority attach importance to whether that opinion is supported or not. To reflect the effect of minority opinions on consensus, this paper establishes a LGDM framework with an objective and interactive‐information‐based feedback mechanism for the CRP. Given the natural forms of human expression, multi‐granular linguistic information and a 2‐tuple linguistic model are used. First, initial weights are objectively assigned to decision‐makers (DMs) to weaken the impact of the majority. Subsequently, a non‐support degree function is newly defined to reflect the extent to which other DMs dissent from a minority opinion. More importantly, feedback rules are constructed to make corresponding adjustments to the powers of discourse among all DMs in the attempt to reach consensus. Finally, the proposed three‐phase LGDM framework is applied to new product development (NPD), and simulation experiments are conducted based on two algorithms to verify the framework's applicability and feasibility.