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New application of fuzzy logic in social science studies


Citation

Rostami, Yousef and Wong, Shaw Voon and Ibrahim, Ali Azizi (2005) New application of fuzzy logic in social science studies. In: International Advanced Technology Congress: Conference on Computer Integrated Systems, 6 - 8 Dec. 2005, Putrajaya, Malaysia. .

Abstract / Synopsis

Behavior and thinking of the people, with their differences on emotions and intuitions, produce the ambiguity in social studies. Fuzzy logic is an approach to computing based on "degrees of truth" rather than the usual "true or false" (1 or 0) Boolean logic on which the modern computer is based. The fuzzy sets theory, described by the membership function (Zadeh, 1965), is suggested as an alternative approach to deal with the vagueness of planning goals and the uncertainties in the number of researches and papers (Jowitt, 1984; Koo et al, 1991; Julien, 1994; Chang and Wang, 1996). Fuzzy sets are a communication medium that speaks to both the logical nature of the science and the complexity of the humanities and social sciences. Instead of using point estimation in conventional probability theory, fuzzy set theory can be used to granulate a concept into a set with membership function and uses the range of input data. With fuzzy logic propositions can be represented with degrees of truthfulness and falsehood. This is strongly connected with human’s inherent ability to make conclusions using uncertain information. In addition natural language like most other activities in life and indeed the universe is not easily translated into the absolute terms of 0 and 1.Fuzzy regression model (FRM) is an alternative to estimate the relation between variables among the forecasting models, when the data are not sufficient to identify the relation and we have uncertainty in data.In this paper we analyzed the demand model for international ecotourism in Malaysia as a social science field study by using Fuzzy Regression Model.


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Additional Metadata

Item Type: Conference or Workshop Item (Paper)
Divisions: Faculty of Engineering
Keywords: Fuzzy logic; Social science; Fuzzy Regression Model (FRM)
Depositing User: Erni Suraya Abdul Aziz
Date Deposited: 11 Jun 2015 12:31
Last Modified: 11 Jun 2015 12:31
URI: http://psasir.upm.edu.my/id/eprint/38733
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