[Formula: see text]: A method for synthetic opinions to yield a robust fuzzy expert system

In many domains, decision-making is challenging, as experts are often limited in availability. However, without a sufficient number of expert opinions, the associated solutions would not be robust. Motivated by this, [Formula: see text] , a Method for SYnthetic Opinions has been developed to produce a robust Fuzzy Expert System (FES) by specifying [Formula: see text] , the number of (synthetic) experts per rule. For every one of these “synthetic experts”, [Formula: see text] produces an opinion from a normal distribution characteristic of a human expert. Correspondingly, the FES is used to produce an opinion from an antecedent vector whose elements are sampled from a uniform distribution. Synthetic and human opinion vectors, resulting from all rules and number of experts per rule, are driven to agree through optimization of weights associated with the fuzzy rules. The weight-optimized [Formula: see text] was tested against sets of human expert opinions in two distinct domains, namely, an industrial development project (IDP) and passenger car performance (PCP). Results showed that the synthetic and human expert opinions correlated between 91.4% and 98.0% on an average over [Formula: see text] , across five outcomes of the IDP. Likewise, for PCP, respective correlations varied between 85.6% and 90.8% for [Formula: see text] across the two performance measures. These strong correlations indicate that [Formula: see text] is capable of producing synthetic opinions to yield a robust FES • This method, known as [Formula: see text] , generates synthetic expert opinions to achieve robustness in an FES. • [Formula: see text] was validated against sets of human expert opinions in two distinct domains. • Strong correlations were observed between the synthetic and human expert opinions.