Soft consensus and network dynamics in group decision making

Fedrizzi, Mario; Pereira, Ricardo Alberto Marques

doi:10.1002/(sici)1098-111x(199901)14:1<63::aid-int5>3.0.co;2-s

Cited by 57 publications

(26 citation statements)

References 19 publications

(7 reference statements)

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“…In this section we present a brief review of the original soft consensus model introduced in [27]. Our point of departure is a set of individual fuzzy preference relations.…”

Section: The Soft Dissensus Measure and The Consensus Dynamicsmentioning

confidence: 99%

“…. , a m } for the set of decisional alternatives is discussed in [27] and [29]. Here, for the sake of simplicity, we assume that the alternatives available are only two (m = 2), which means that each individual preference relation R i has only one degree of freedom, denoted by x i = r i 12 .…”

Section: The Soft Dissensus Measure and The Consensus Dynamicsmentioning

confidence: 99%

“…The resulting soft consensus dynamics acts on the network of single preference structures by a combination of a collective process of diffusion and an individual mechanism of inertia. Introduced as an extension of the crisp model of consensus dynamics described in [27], the fuzzy soft consensus model [29] substitutes the standard crisp preferences by fuzzy triangular preferences. The fuzzy extension of the soft consensus model is based on the use of a distance measure between triangular fuzzy numbers.…”

Section: Introductionmentioning

confidence: 99%

“…The paper is organized as follows. In section 2 we briefly review the soft consensus model proposed in [27] and we show how to derive the soft consensus dynamics on the basis of a cost function W combining a soft measure of collective dissensus with an individual mechanism of opinion changing aversion. In section 3, assuming fuzzy triangular preferences as in [29], we describe the new distance measure and introduce the cost function W of the fuzzy soft consensus model.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

The Dynamics of Consensus in Group Decision Making: Investigating the Pairwise Interactions between Fuzzy Preferences

Fedrizzi

Pereira

Brunelli

2010

Preferences and Decisions

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In this paper we present an overview of the soft consensus model in group decision making and we investigate the dynamical patterns generated by the fundamental pairwise preference interactions on which the model is based. The dynamical mechanism of the soft consensus model is driven by the minimization of a cost function combining a collective measure of dissensus with an individual mechanism of opinion changing aversion. The dissensus measure plays a key role in the model and induces a network of pairwise interactions between the individual preferences. The structure of fuzzy relations is present at both the individual and the collective levels of description of the soft consensus model: pairwise preference intensities between alternatives at the individual level, and pairwise interaction coefficients between decision makers at the collective level. The collective measure of dissensus is based on non linear scaling functions of the linguistic quantifier type and expresses the degree to which most of the decision makers disagree with respect to their preferences regarding the most relevant alternatives. The graded notion of consensus underlying the dissensus measure is central to the dynamical unfolding of the model. The original formulation of the soft consensus model in terms of standard numerical preferences has been recently extended in order to allow decision makers to express their preferences by means of triangular fuzzy numbers. An appropriate notion of distance between triangular fuzzy numbers has been chosen for the construction of the collective dissensus measure. In the extended formulation of the soft consensus model the extra degrees of freedom associated with the triangular fuzzy preferences, combined with non linear na- ture of the pairwise preference interactions, generate various interesting and suggestive dynamical patterns. In the present paper we investigate these dynamical patterns which are illustrated by means of a number of computer simulations.

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“…In this section we present a brief review of the original soft consensus model introduced in [27]. Our point of departure is a set of individual fuzzy preference relations.…”

Section: The Soft Dissensus Measure and The Consensus Dynamicsmentioning

confidence: 99%

Section: The Soft Dissensus Measure and The Consensus Dynamicsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The Dynamics of Consensus in Group Decision Making: Investigating the Pairwise Interactions between Fuzzy Preferences

Fedrizzi

Pereira

Brunelli

2010

Preferences and Decisions

Self Cite

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show abstract

“…Instead, DMs may express their preferences by imprecise formats, such as value ranges, linguistic variables and fuzzy numbers 9 . Considerable efforts have been attached to the MAGDM with inaccurate or incomplete information and many approaches to solve this problem have been reported in the existing literature 11,10,5,12,13,14,15,16,17,18,19 .…”

Section: Introductionmentioning

confidence: 98%

A decision support model for multi-attribute group decision making using a multi-objective optimization approach

Xiong¹,

Chen²,

Yang³

et al. 2013

IJCIS

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The multi-attribute group decision making (MAGDM) problem has received considerable attention in the decision analysis field and fruitful achievements have been reported in the literature. This paper focuses on the MAGDM in which the subjective absolute judgement on alternatives with respect to evaluating attributes are represented by fuzzy numbers. This paper employs the consensus degree to measure the agreement level of a MAGDM solution and develops a new measure degree-departure degree to evaluate how far the decision makers from their initial decision preferences. Based on these two conflicting measure degrees, the decision process of MAGDM is modelled as a multi-objective optimization problem. A decision support model (DSM) for MAGDM is proposed. The proposed DSM, incorporating five implementing phases, aims at obtaining acceptable decision solution(s) by solving the multi-objective optimization problem and conducting an interactive procedure with decision makers. In case study, this paper takes the alternative selection problem about hydroelectric project to illustrate the phases and procedure of the proposed DSM.

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A decision making approach to relevance feedback in information retrieval: A model based on soft consensus dynamics

Pasi

Pereira

1999

Int. J. Intell. Syst.

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Soft consensus and network dynamics in group decision making

Cited by 57 publications

References 19 publications

The Dynamics of Consensus in Group Decision Making: Investigating the Pairwise Interactions between Fuzzy Preferences

The Dynamics of Consensus in Group Decision Making: Investigating the Pairwise Interactions between Fuzzy Preferences

A decision support model for multi-attribute group decision making using a multi-objective optimization approach

A decision making approach to relevance feedback in information retrieval: A model based on soft consensus dynamics

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