A consensus reaching process for large‐scale group decision making with heterogeneous preference information

Abstract

Many group decision making (GDM) models enable experts to use only one preference information representation form. It is natural to allow experts to express preferences in various formats considering the heterogeneity of experts. In this case, how to reach the consensus of a group from heterogeneous preference information is an attractive research issue. This study proposes a consensus reaching process for large‐scale GDM with heterogeneous preference information. First, we review various preference formats including preference orderings, numerical assessments, interval‐valued assessments, and linguistic assessments. To facilitate the heterogeneous information aggregation, we classify experts into subgroups according to their preference types rather than the similarities of preference values, and then aggregate the homogeneous preference values in each subgroup. The subgroup priorities derived by homogeneous methods are then aggregated into global priorities. An ordinal consensus measuring process based on individual orderings is introduced. To reach the ordinal consensus, optimization models are constructed to ensure each subgroup's preferences equivalent to the global preferences, and the recommended ranges and strength of preference modification are given to experts. Finally, the proposed method is validated by an illustrative example about blockchain platform selection.

A cloud‐based and web‐based group decision support system in multilingual environment with hesitant fuzzy linguistic preference relations

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A novel belief χ2 ${\chi }^{2}$ divergence for multisource information fusion and its application in pattern classification

“…Multisource information fusion is an invaluable method used to combine the different information and generate a synthetic inference. 1 Therefore, multisource information fusion can be applied to a variety of areas, such as data deduplication, 2 intelligence analysis, 3 medical diagnosis, 4 picture fuzzy set, 5 reliability evaluation, 6,7 decision-making, [8][9][10][11] representation learning, 12 complex event processing, 13 and so on. [14][15][16] However, there is a challenge in multisource information fusion field that uncertain or even false results may be got when the data is interfered.…”

Robust flocking of multiple intelligent agents with multiple disturbances

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