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Type-One Fuzzy Logic for Quantitatively Defining Imprecise Linguistic Terms in Politics and Public Policy
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Author(s): Ashu M. G. Solo (Maverick Technologies America Inc., USA), Madan M. Gupta (University of Saskatchewan, Canada), Noriyasu Homma (Tohoku University, Japan)and Zeng-Guang Hou (The Chinese Academy of Sciences, China)
Copyright: 2014
Pages: 17
Source title:
Political Campaigning in the Information Age
Source Author(s)/Editor(s): Ashu M. G. Solo (Maverick Technologies America Inc., USA)
DOI: 10.4018/978-1-4666-6062-5.ch015
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Abstract
During a presidential forum in the 2008 U.S. presidential campaign, the moderator, Pastor Rick Warren, wanted Senator John McCain and then-Senator 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.” Imprecise terms like these in natural languages should be considered to have “qualitative definitions,” “quantitative definitions,” “crisp quantitative definitions,” and “fuzzy quantitative definitions.” This chapter provides much more information on the preceding.
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