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.