Multi-criteria group consensus under linear cost opinion elasticity

Ben‐Arieh, David; Easton, Todd

doi:10.1016/j.dss.2006.11.009

Cited by 330 publications

(108 citation statements)

References 15 publications

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“…Constraints (4-2) and (4-3) indicate the deviation between e i 's and G's expected compensation prices. Constraints (4-6)- (4)(5)(6)(7)(8)(9)(10)(11) are the preferences expressed by each e i and G. Obviously, Model (4) is just a concrete example of Model (2). The Taking resident e 4 for example (see Figure 5.4), the expected compensation price is the interval of [62, 67], namely his (her) acceptable price for arriving the relocation consensus is [62,67].…”

Section: General Model and Its Economic Interpretationmentioning

confidence: 99%

“…Constraint (2)(3)(4) indicates that the allowable compensation deviation between e i and G is [o − ε i , o + ε i ], which is regarded as the range e i is prepared to accept. The utility value λ is the ultimate goal of the negotiation, and to some extent, it can also be considered as a measure of the consensus level.…”

Section: Consensus Modeling With Multi-stage Fluctuation Utility Funcmentioning

confidence: 99%

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Consensus Measure with Multi-stage Fluctuation Utility Based on China’s Urban Demolition Negotiation

Gong

Chiclana

et al. 2016

Group Decis Negot

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Utility functions are often used to reflect decision makers' (DMs') preferences. They have the following two merits: one refers to the representation of the DM's utility (satisfaction) level, the other one to the measuring of the consensus level in a negotiation process. Taking the background of China's urban house demolition, a new kind of consensus model is established by using different types of multi-stage fluctuation utility functions, such as concave, convex, S-shaped, reversed S-shaped, reversed U-shaped as well as their combinations, to reveal negotiators' dynamic physiological preferences and consensus level. Moreover, the effects of the decision-making budget and the individual compensation tolerance on the consensus level are also discussed in this paper. Compared with previous research, the proposed model takes both the negotiation cost and DM's preference structure into consideration, and most importantly, it is computational less complex.

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Section: General Model and Its Economic Interpretationmentioning

confidence: 99%

Section: Consensus Modeling With Multi-stage Fluctuation Utility Funcmentioning

confidence: 99%

Consensus Measure with Multi-stage Fluctuation Utility Based on China’s Urban Demolition Negotiation

Gong

Chiclana

et al. 2016

Group Decis Negot

View full text Add to dashboard Cite

show abstract

“…Many different group DM systems exist depending on how the experts interact with each other (Guo et al 2002;Herrera and Martínez 2001;Huynh and Nakamori 2005). Providing a consensus among experts having varying amounts of dominance in group DM is studied in Ben-Arieh and Easton (2007). The main idea presented in Viedma et al (2005) is to establish a consensus DM model that allows the combination of information from different linguistic domains.…”

Section: Related Workmentioning

confidence: 97%

Local decision making and decision fusion in hierarchical levels

Beldek

Leblebi̇ci̇oğlu

2009

TOP

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“…Ben-Arieh and Easton (2007) argued that especially in multicriteria decision problems it is very unlikely that all DMs will have the same opinions (preferences). They proposed a method that minimizes the costs of arriving at a consensus called the minimum cost consensus using the concept of linear opinion elasticity.…”

mentioning

confidence: 99%

The Use of Ranking Veto Concept to Mitigate the Compensatory Effects of Additive Aggregation in Group Decisions on a Water Utility Automation Investment

Daher

Almeida

2011

Group Decis Negot

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The use of additive models for aggregating group decisions implies they have a compensatory effect in the process of aggregating all decision makers' (DMs') preferences. In this kind of model, the final result may produce some extremely undesirable alternatives for one or more DMs. Such alternatives may emerge with a higher ranking than desirable ones, thus generating conflicts and regrets. To overcome this problem the concept of ranking veto is introduced based on a reduction factor combined with the utility of the alternative in order to penalize conflicting alternatives and reduce disagreements in an additive model. A water utility problem was considered as a numerical application to illustrate the model. A decision group method based on MAUT, utility thresholds and a reduction factor is proposed to support group decision in selecting regions that will receive investments in automation over the next 4 years.

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Multi-criteria group consensus under linear cost opinion elasticity

Cited by 330 publications

References 15 publications

Consensus Measure with Multi-stage Fluctuation Utility Based on China’s Urban Demolition Negotiation

Consensus Measure with Multi-stage Fluctuation Utility Based on China’s Urban Demolition Negotiation

Local decision making and decision fusion in hierarchical levels

The Use of Ranking Veto Concept to Mitigate the Compensatory Effects of Additive Aggregation in Group Decisions on a Water Utility Automation Investment

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