José Luis Pino Mejías

Dealing with Imprecision in Performance Evaluation Processes Using Indicators: A Fuzzy Distance-Based Approach

This paper presents an approach for dealing with imprecision in performance evaluation analysis focused particularly on Public Policy but applicable to other fields. While most previous research builds fuzzy indicators by assessing a membership function as the indicator itself, with direct subjective evaluation, this new approach is based on the fuzzification of crisp indicators which are subject to imprecision, vagueness or an incomplete perception of reality. These indicators may be quantitative or qualitative yet approximate expressions from real data in order to define results and systematize the information. The evaluation process is based on those expressions and uses fuzzy distances as a performance evaluation tool to show the degree of efficacy related to both crisp and fuzzy targets set as the reference value in the evaluation process.

Dealing with Imprecision in Consumer Theory: A New Approach to Fuzzy Utility Theory

This chapter presents a new approach to dealing with imprecision in the Classical Consumer Utility Theory based on the concept of Marginal Rate of Substitution (MRS) and using the concept of fuzzy sets and fuzzy numbers. The methodology developed applies imprecision to MRS, whereas previous studies placed the imprecision factor on final utility values and functions. The chapter considers fuzzy elements applied to MRS and uses the necessary formulations to obtain the results in Utility Theory. In this fuzzy environment, the final consumer decision problem is framed as a fuzzy nonlinear programming problem, maintaining the classical structure in which consumers maximize their fuzzy utility subject to budget constraints, and showing that the consumer optimum choice is a fuzzy set. The chapter will also address the problem of aggregation of utility functions in order to offer a multi-criteria approach.