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Soft Computing in Economics and Finance pp 41–105Cite as

The Methods for Uncertainty Modeling

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Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 6))

Abstract

There are many modern methods for uncertainty modeling developed in last decades. Generally, they are not in conflict with the traditional probabilistic approach since they deal with another (non-probabilistic) types of uncertainties. Moreover, in the solution of real-world problems, the probabilistic and the other types of uncertainties often become apparent simultaneously. Therefore, the synthesis of traditional and modern methods for uncertainty modeling usually provides best results in applications. In this chapter, we present an overview of modern methods based on the fuzzy sets theory (including type-2 fuzzy sets and intuitionistic fuzzy sets), interval analysis, and the Dempster-Shafer theory of evidence (DST). We show the interrelations between these methods and emphasize some problems that impede their applications. We do not intend to present here a comprehensive overview of all modern methods as now they are well presented in numerous books and handbooks. Therefore, in this chapter we present the modern methods for uncertainty modeling only to the extent needed for understanding the applications presented in the following chapters.

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  1. Ludmila Dymowa
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Dymowa, L. (2011). The Methods for Uncertainty Modeling. In: Soft Computing in Economics and Finance. Intelligent Systems Reference Library, vol 6. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17719-4_3

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  • DOI: https://doi.org/10.1007/978-3-642-17719-4_3

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