Identifying conflict patterns to reach a consensus – A novel group decision approach

Cheng, Li‐Chen; Chen, Yen-Liang; Chiang, Yu Chia

doi:10.1016/j.ejor.2016.04.004

Cited by 29 publications

(7 citation statements)

References 19 publications

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“…Thus, it is classified as cooperative leading behavior, which is highly beneficial for reaching a consensus. 4(1) = 0.913, which is much higher than those of other clusters. The modification behavior of 4 in the first round of the CRP helps it to be allocated the highest decision weight in the next round.…”

Section: Accepted Manuscriptmentioning

confidence: 71%

“…Thus, the decision weights of 1 and 2 are downward reinforced to prevent them causing higher levels of conflict. The modification behavior of 4 has high scores for both # and (1 − # ). Thus, it is classified as cooperative leading behavior, which is highly beneficial for reaching a consensus.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

“…Multi-attribute large-scale GDM (MALSGDM) is a combination of the basic theory and procedure of GDM with an application context in which large number of decision makers (DMs) from multiple backgrounds make their evaluations toward a set of alternatives over multiple attributes and select the best alternative after the decision results are aggregated [1][2][3]. Given the wide variety DMs' different backgrounds and expertise, DMs will have different evaluations on attributes or ordering of alternatives and conflict can easily arises [4]. Recent research has paid great attention to the consensus degree reached after decision results are collected from DMs, especially regarding decision making for public issues such as environmental problems [5] [6], policy making [7] [8], and infrastructure planning [9] [10].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A novel consensus model for multi-attribute large-scale group decision making based on comprehensive behavior classification and adaptive weight updating

Shi

Wang

Palomares

et al. 2018

Knowledge-Based Systems

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Section: Accepted Manuscriptmentioning

confidence: 71%

Section: Accepted Manuscriptmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A novel consensus model for multi-attribute large-scale group decision making based on comprehensive behavior classification and adaptive weight updating

Shi

Wang

Palomares

et al. 2018

Knowledge-Based Systems

View full text Add to dashboard Cite

show abstract

“…Formal aspects of some tasks, such as risk propagation, can be found in [22], and can be adopted, in particular, from multi-echelon supply chain problem [62], [70]. Other parts of the formalization of a multistate screening model, such as risk mitigation, consensus of risk conflict assessments, and trust relationships between screening states, are commonly adopted from advanced group decision-making studies [8], [17], [56]. The value of the proposed taxonomical view (Fig.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Cognitive checkpoint: Emerging technologies for biometric-enabled watchlist screening

Yanushkevich

Sundberg

Twyman

et al. 2019

Computers & Security

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“…To eliminate the conflict among DMs by reducing it under an acceptable degree for the decision making events, a frequently used approach consists in undertaking a Consensus Reaching Processes (CRP), aimed at obtaining a collective solution as close as possible to unanimous agreement [6,13,14,15,16,17,18].…”

Section: Introductionmentioning

confidence: 99%

Large-scale group decision making model based on social network analysis: Trust relationship-based conflict detection and elimination

Liu

Zhou

Ding

et al. 2019

European Journal of Operational Research

294

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The paper proposes a Trust Relationship-based Conflict * Table 1 is Table 18 in the manuscript. In the two experiments, the conflict threshold is set the same as Φ = 0.3. It means that within the limited iterations T max , when the calculated group conflict degree is less that the threshold, the acceptable consensus is reached and the final decision can be made. As the results recorded in Table 1, if we set 1 *Detailed Response to Reviewers the value of Φ higher than 0.3, we can make the final decision within a few iterations. Such as, if we set Φ = 0.42 in the two experiments, we can chose the final decision with the two group conflict degrees are 0.41 after three iterations and two iterations, respectively. In other words, the higher the value of conflict threshold is, the less iterations are needed and the higher the group conflict degree is allowed before making the final decision in the LSGDM event. A smaller number of iterations implies a reduction in the temporal cost. However, the higher value of group conflict degree means the higher dissatisfaction among DMs, which may lead to some other serious group events. As there are the results of the practical scenario experiment in Table 1, we removed the section for the behaviors of parameters to Section 6.3 to make it more readable. On the other hand, if we set Φ = 0.28 in the two experiments, the numerical example can not reach the consensus in the limited 6 iterations. That is, the lower the conflict threshold is, the more iterations are needed for the LSGDM event. Comment 2: This is a wording suggestion to consider: in definition 6.1 consider using urgency level/level of urgency vs. emergency level. A decision could be very urgent but in no way an emergency.

show abstract

Identifying conflict patterns to reach a consensus – A novel group decision approach

Cited by 29 publications

References 19 publications

A novel consensus model for multi-attribute large-scale group decision making based on comprehensive behavior classification and adaptive weight updating

A novel consensus model for multi-attribute large-scale group decision making based on comprehensive behavior classification and adaptive weight updating

Cognitive checkpoint: Emerging technologies for biometric-enabled watchlist screening

Large-scale group decision making model based on social network analysis: Trust relationship-based conflict detection and elimination

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