Type one uncertainty is a term coined by Madan M. Gupta for information that arises from the random behavior of physical systems. The pervasiveness of this type of uncertainty can be witnessed in random vibrations of a machine, random fluctuations of electrons in a magnetic field, diffusion of gases in a thermal field, random electrical activities of cardiac muscles, uncertain fluctuations in the weather pattern, and turbulent blood flow through a damaged cardiac valve. Type one uncertainty has been studied for centuries. Complex statistical mathematics has evolved for the characterization and analysis of such random phenomena. Stochastic theory is effective in dealing with type one uncertainty.
Published in Chapter:
Type-One and Interval Type-Two Fuzzy Logic for Quantitatively Defining Imprecise Linguistic Terms in Politics and Public Policy
Ashu M. G. Solo (Maverick Trailblazers Inc., USA)
Copyright: © 2020
|Pages: 28
DOI: 10.4018/978-1-7998-0377-5.ch002
Abstract
During a presidential forum in the 2008 U.S. presidential campaign, the moderator, Pastor Rick Warren, wanted Sen. John McCain and then-Sen. Barack Obama to define rich with a specific number. Warren wanted to know at what specific income level a person goes from being not rich to rich. The problem with this question is that there is no specific income at which a person makes the leap from being not rich to being rich. This is because rich is a fuzzy set, not a crisp set, with different incomes having different degrees of membership in the rich fuzzy set. Similarly, middle class and poor are fuzzy sets. Fuzzy logic is needed to properly ask and answer Warren's question about quantitatively defining rich. Similarly, fuzzy logic is needed to properly ask and answer queries about quantitatively defining imprecise linguistic terms in politics and public policy like middle class, poor, low inflation, medium inflation, and high inflation. Type-one or interval type-two fuzzy logic can be used for quantitatively defining imprecise linguistic terms. This chapter shows how to use type-one fuzzy logic and interval type-two fuzzy logic for this purpose, as well as the advantages and disadvantages of each. Imprecise terms in natural languages should be considered to have qualitative definitions, quantitative definitions, crisp quantitative definitions, fuzzy quantitative definitions, type-one fuzzy quantitative definitions, and interval type-two fuzzy quantitative definitions.