The Dombi t-norm-based Group Consensus Measure and Its Applications

In this study, we elaborate on the idea of parametric group consensus measures. Here, we employ the additive generators of Dombi t-norms to construct fuzzy entropies, which are then utilized to generate group consensus measures. Since these additive generators are single-parameter functions, the resulting group consensus measures are also single-parameter mappings. We demonstrate that, given a set of inputs from decision-makers, the group consensus measure behaves as a bounded and non-decreasing function of its parameter. With this in mind, we address the question of which parameter value yields a group consensus measure that most accurately reflects the average perceived consensus level of the group. Next, we present a necessary and sufficient condition for the existence of an optimal value of the parameter in question, and provide algorithmic procedures to find it.