Minimum information-loss transformations to support heterogeneous group decision making in a distributed linguistic context

Abstract

In the problems that linguistic assessments are conducted by adopting multiple sources of information representations, the management of unification of heterogeneous information and information loss are necessary. To support a useful fusion of heterogeneous distributed information in linguistic group decision making, a minimum information-loss transformation framework is proposed in this paper. First, distributed linguistic distance measurements are defined to measure information loss among heterogeneous distributed linguistic preference information, and then several minimum information-loss transformation models (MILTMs) with desirable properties are proposed. Furthermore, the application of the MILTMs in addressing the fusion of heterogeneous distributed linguistic information in a multi-attribute group decision context is discussed, and the flexibility of distributed linguistic information is studied to justify the MILTMs through numerical examples and comparative analyses.