GROUPTHINK: EFFECTS OF COHESIVENESS AND PROBLEM-SOLVING PROCEDURES ON GROUP DECISION MAKING

1984 ◽  
Vol 12 (2) ◽  
pp. 157-164 ◽  
Author(s):  
Michael R. Callaway ◽  
James K Esser
Keyword(s):  
Decision Making ◽  
Problem Solving ◽  
Group Decision Making ◽  
Group Decision ◽  
Group Processes ◽  
Decision Procedures ◽  
Decision Quality ◽  
Group Cohesiveness ◽  
Mixed Support ◽  
High Level

Janis' (1972) groupthink formulation was tested in the laboratory by manipulating group cohesiveness and adequacy of decision procedures in a factorial design. Internal analysis, involving redefined cohesiveness categories, provided mixed support for the groupthink hypothesis on measures of decision quality and group processes presumed to underlie the groupthink decisions. Specifically, it was found that: (1) highest quality decisions were produced by groups of intermediate cohesiveness; (2) high cohesive groups without adequate decision procedures (the groupthink condition) tended to make the poorest decisions; and (3) the presence of groupthink was characterized by a lack of disagreement and a high level of confidence in the group's decisions.

scholarly journals Towards Better Concordance among Contextualized Evaluations in FAST-GDM Problems

Mathematics ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 93
Author(s):  
Marcelo Loor ◽  
Ana Tapia-Rosero ◽  
Guy De Tré
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Group Decision Making ◽  
Group Decision ◽  
Intuitionistic Fuzzy Sets ◽  
Heterogeneous Group ◽  
Intuitionistic Fuzzy ◽  
High Level ◽  
Consensus Reaching ◽  
Novel Algorithm

A flexible attribute-set group decision-making (FAST-GDM) problem consists in finding the most suitable option(s) out of the options under consideration, with a general agreement among a heterogeneous group of experts who can focus on different attributes to evaluate those options. An open challenge in FAST-GDM problems is to design consensus reaching processes (CRPs) by which the participants can perform evaluations with a high level of consensus. To address this challenge, a novel algorithm for reaching consensus is proposed in this paper. By means of the algorithm, called FAST-CR-XMIS, a participant can reconsider his/her evaluations after studying the most influential samples that have been shared by others through contextualized evaluations. Since exchanging those samples may make participants’ understandings more like each other, an increase of the level of consensus is expected. A simulation of a CRP where contextualized evaluations of newswire stories are characterized as augmented intuitionistic fuzzy sets (AIFS) shows how FAST-CR-XMIS can increase the level of consensus among the participants during the CRP.

CLOUD DELPHI METHOD

2012 ◽  
Vol 20 (01) ◽  
pp. 77-97 ◽  
Author(s):  
XIAO-JUN YANG ◽  
LUAN ZENG ◽  
RAN ZHANG
Keyword(s):  
Decision Making ◽  
Problem Solving ◽  
Group Decision Making ◽  
Delphi Method ◽  
Group Decision ◽  
Cloud Model ◽  
Weighted Average ◽  
Implementation Process ◽  
Numerical Example ◽  
The Individual

Group decision making is an important category of problem solving techniques for complicated problems, among which the Delphi method has been widely applied. In this paper an improved Delphi method based on Cloud model is proposed in order to deal with the fuzziness and uncertainty in experts' subjective judgments. The proposed Cloud Delphi Method (CDM) describes experts' opinions by Cloud model and we aggregate the experts' Cloud opinions by synthetic algorithm and weighted average algorithm. Another key point of CDM is to stabilize and accommodate the individual fuzzy estimates by the defined stability rules rather than having to force them to converge, or reduce. The Cloud opinions and aggregation results can be exhibited in a graphically way leading experts to judge intuitively and it can decrease the number of repetitive surveys and/or interviews. Moreover, it is more scientific and easier to represent experts' opinion base on Cloud model which can combine fuzziness and uncertainty well. A numerical example is examined to demonstrate applicability and implementation process of CDM.

A Consensus Model for Group Decision Making with Hesitant Fuzzy Information

2015 ◽  
Vol 23 (03) ◽  
pp. 459-480 ◽  
Author(s):  
Zhiming Zhang ◽  
Chao Wang ◽  
Xuedong Tian
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Selection Process ◽  
Feedback Mechanism ◽  
Mathematical Tool ◽  
Fuzzy Information ◽  
Consensus Process ◽  
Decision Matrix ◽  
High Level

Hesitant fuzzy sets, permitting the membership of an element to be a set of several possible values, can be used as an efficient mathematical tool for modeling people's hesitancy in daily life. The aim of this paper is to present a consensus support model for group decision making with hesitant fuzzy information. This model is composed of two processes: a consensus process and a selection process. The consensus process is carried out to reach a high level of consensus among experts' opinions before applying a selection process. We first aggregate the hesitant fuzzy decision matrix into a group decision matrix by using the additive aggregation (AA) operator. Then the consensus measure is used to design a feedback mechanism that generates advice to the experts on how they should change their preferences to obtain a solution with a high consensus degree. In the selection process, based on the consentaneous group decision matrix, the additive weighted aggregation (AWA) operator is utilized to derive the overall attribute values of alternatives, by which the most desirable alternative can be found out. Finally, a practical example is proposed to illustrate the application of the proposed model.

scholarly journals Some Complex Intuitionistic Uncertain Linguistic Heronian Mean Operators and Their Application in Multiattribute Group Decision Making

2021 ◽  
Vol 2021 ◽  
pp. 1-31
Author(s):  
Harish Garg ◽  
Zeeshan Ali ◽  
Jeonghwan Gwak ◽  
Tahir Mahmood ◽  
Sultan Aljahdali
Keyword(s):  
Decision Making ◽  
Problem Solving ◽  
Group Decision Making ◽  
Fuzzy Set ◽  
Group Decision ◽  
Intuitionistic Fuzzy Set ◽  
Intuitionistic Fuzzy ◽  
Weighted Arithmetic ◽  
Decision Making Problem ◽  
Heronian Mean

In this paper, a new decision-making algorithm has been presented in the context of a complex intuitionistic uncertain linguistic set (CIULS) environment. CIULS integrates the concept the complex of a intuitionistic fuzzy set (CIFS) and uncertain linguistic set (ULS) to deal with uncertain and imprecise information in a more proactive manner. To investigate the interrelation between the pairs of CIULSs, we combine the concept of the Heronian mean (HM) and the complex intuitionistic uncertain linguistic (CIUL) to describe some new operators, namely, CIUL arithmetic HM (CIULAHM), CIUL weighted arithmetic HM (CIULWAHM), CIUL geometric HM (CIULGHM), and CIUL weighted geometric HM (CIULWGHM). The main advantage of these suggested operators is that they considered the interaction between pairs of objects during the formulation process. Also, a number of distinct brief cases and properties of the operators are analyzed. In addition, based on these operators, we have stated a MAGDM (“multiattribute group decision-making”) problem-solving algorithm. The consistency of the algorithm is illustrated by a computational example that compares the effects of the algorithm with a number of well-known existing methods.

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