: 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, , a Method for SYnthetic Opinions has been developed to produce a robust Fuzzy Expert System () by specifying , the number of (synthetic) experts per rule. For every one of these "synthetic experts", produces an opinion from a normal distribution characteristic of a human expert. Correspondingly, the 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 was tested against sets of human expert opinions in two distinct domains, namely, an industrial development project () and passenger car performance (). Results showed that the synthetic and human expert opinions correlated between 91.4% and 98.0% on an average over , across five outcomes of the . Likewise, for , respective correlations varied between 85.6% and 90.8% for across the two performance measures. These strong correlations indicate that is capable of producing synthetic opinions to yield a robust where sufficient human experts are not available.•This method, known as , generates synthetic expert opinions to achieve robustness in an .• was validated against sets of human expert opinions in two distinct domains.•Strong correlations were observed between the synthetic and human expert opinions.