In this paper we show that the possibilistic mean values produce computation results that may differ in a non trivial may from those obtained with the fuzzy extension principle. The evidence is carried out by comparing some examples derived from several models in finance and economics.

In this paper we show how a fuzzification process can benefit of the F-transform and possibility distributions.

This paper adopts a promising concept of uncertainty, incorporating both stochastic processes and fuzzy theory to capture the somewhat vague and imprecise ideas the manager has about the future expected cash flows, the profitability of the project, the costs of the project and many other...

We analyze a decomposition of the fuzzy numbers (or intervals) which seems to be of interest in the study of some properties of fuzzy arithmetic operations and, in particular, in the analysis of fuzziness, of shape-preservation (symmetry) and distributivity of multiplication and division. By the...

In financial markets people have to cope with a lot of uncertainty while making decisions. Many models have been introduced in the last years to handle vagueness but it is very difficult to capture together all the fundamental characteristics of real markets. Fuzzy modeling for finance seems to...

In investment appraisal, uncertainty can be managed through intervals or fuzzy numbers because the arithmetical properties and the extension principle are well established and can be successfully applied in a rigorous way. We apply interval and fuzzy numbers to the Average Internal Rate of...

The fuzzy transform (F-transform), introduced by I. Perfilieva, is a powerful tool for the construction of fuzzy approximation models; it is based on generalized fuzzy partitions and it is obtained by minimizing a quadratic (L₂-norm) functional. In this paper we describe an analogous...