Informatica

Decision-making under strict uncertainty involves evaluating a set of alternatives without knowledge of the probability of scenarios using crisp evaluations. Our work reformulates traditional decision rules to a fuzzy environment, retaining the interpretability of classical principles while incorporating imprecision. Our methodological proposal provides a unified, flexible, and mathematically consistent framework for decision-making under imprecise payoffs. We adapt a total ordering mechanism for trapezoidal fuzzy numbers and admissible interval orders. Our application case study to portfolio selection under fuzzy strict uncertainty demonstrates how the proposed fuzzy generalization can handle financial imprecision and investor risk attitudes through ranking functions.

Ordered Weighted Averaging (OWA) operators have been widely applied in Group Decision-Making (GDM) to fuse expert opinions. However, their effectiveness depends on the selection of an appropriate weighting vector, which remains a challenge due to limited research on its impact on Consensus Reaching Processes (CRPs). This paper addresses this gap by analysing the influence of different OWA weighting techniques on consensus formation, particularly in large-scale GDM (LSGDM) scenarios. To do so, we propose a Comprehensive Minimum Cost Consensus (CMCC) model that integrates OWA operators with classical consensus measures to enhance the decision-making process. Since existing OWA-based Minimum Cost Consensus (MCC) models struggle with computational complexity, we introduce linearized versions of the OWA-based CMCC model tailored for LSGDM applications. Furthermore, we conduct a detailed comparison of various OWA weight allocation methods, assessing their impact on consensus quality under different levels of expert participation and opinion polarization. Additionally, our linearized formulations significantly reduce the computational cost for OWA-based CMCC models, improving their scalability.