Skip to main content

Research Repository

Advanced Search

Comparing Intervals Using Type Reduction

Runkler, Thomas A.; Chen, Chao; Coupland, Simon; John, Robert

Authors

Thomas A. Runkler

CHAO CHEN Chao.Chen@nottingham.ac.uk
Assistant Professor

Simon Coupland

Robert John



Abstract

Many decision making processes are based on choosing options with maximum utility. Often utility assessments are associated with uncertainty, which may be mathematically modeled by intervals of utilities. Intervals of utilities may be mapped to single utility values by so-called type reduction methods which have been originally developed in the context of interval type-2 defuzzification: the method by Nie and Tan (NT), consistent linear type reduction (CLTR), consistent quadratic type reduction (CQTR), and the uncertainty weight method (UW). This paper considers the problem of comparing pairs of utility intervals using type reduction methods. Three different possible relations between pairs of intervals (disjoint, overlapping, and inclusive) are distinguished in an extensive experimental study, which yields recommendations for the choice of type reduction methods with respect to the level of risk that the decision maker is willing to take. If the focus is on mean utility, then we recommend the Nie-Tan method. For more cautious decision making, when very low utilities should be avoided, we recommend consistent linear type reduction with a high value of the cautiousness parameter or consistent quadratic type reduction. For more risky decision making with a strong focus on very high utilities we recommend consistent linear type reduction with a low value of the cautiousness parameter.

Conference Name 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
Conference Location Glasgow, UK
Start Date Jul 19, 2020
End Date Jul 24, 2020
Online Publication Date Aug 26, 2020
Publication Date 2020-07
Deposit Date Dec 2, 2021
Publisher Institute of Electrical and Electronics Engineers
Pages 1-6
Book Title 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
ISBN 9781728169330
DOI https://doi.org/10.1109/fuzz48607.2020.9177675
Public URL https://nottingham-repository.worktribe.com/output/6847322
Publisher URL https://ieeexplore.ieee.org/document/9177675