Contingent Valuation of Non-Market Goods Based on Intuitionistic Fuzzy Clustering: Part I
In order to value the non-market goods, we consider the uncertain preference of the respondents for non-market goods, individual often have trouble trading off the good or amenity against a monetary measure. Valuation in these situations can best be descr
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Abstract In order to value the non-market goods, we consider the uncertain preference of the respondents for non-market goods, individual often have trouble trading off the good or amenity against a monetary measure. Valuation in these situations can best be described as fuzzy in terms of the amenity being valued. We move away from a probabilistic representation of uncertainty and propose the use of intuitionistic fuzzy contingent valuation. That is to say we could apply intuitionistic fuzzy logic to contingent valuation. Since intuitionistic fuzzy sets could provide the information of the membership degree and the nonmembership degree, it has more expression and flexibility better than traditional fuzzy sets in processing uncertain information data. In this paper, we apply intuitionistic fuzzy logic to contingent, developing an intuitionistic fuzzy clustering and interval intuitionistic fuzzy clustering approach for combining preference uncertainty. We develop an intuitionistic fuzzy random utility maximization framework where the perceived utility of each individual is intuitionistic fuzzy in the sense that an individual’s utility belong to each cluster to some degree. Both the willingness to pay (WTP) and willingness not to pay (WNTP) measures we obtain using intuitionistic fuzzy approach are below those using standard probability methods. Keywords Random utility maximization · Contingent valuation uncertainty · Intuitionistic fuzzy c-means clustering
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Preference
Z. Gong (B) · B. Wu College of Mathematics and Information Science, Northwest Normal University, Lanzhou 730070, People’s Republic of China e-mail: [email protected]
B.-Y. Cao and H. Nasseri (eds.), Fuzzy Information & Engineering and Operations Research & Management, Advances in Intelligent Systems and Computing 211, DOI: 10.1007/978-3-642-38667-1_26, © Springer-Verlag Berlin Heidelberg 2014
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Z. Gong and B. Wu
1 Introduction The contingent valuation method (CVM) is a widely used technique to estimate economic values for all kinds of ecosystem and environmental services. It can be used to estimate both use and non-use values, and it is the most widely used method for estimating non-use values. Most CV surveys rely on a dichotomous choice question to elicit willingness to pay (WTP) and willingness not to pay (WNTP). Calculation of the Hicksian compensating or equivalent welfare measure is based on the assumption that the survey respondent knows her utility function with certainty [1]. The assumption of preference certainty is a strong one because CV seeks to elicit values for environmental resources from respondents who may lack the experience to make such assessments. While Hanemann [1] provide an explanation of what preference uncertainty means in the context of the CV method, several authors have adopted varying but complex approaches for dealing with preference uncertainty in non-market valuation [2]. Apparent precision of standard WTP and WNTP estimates faces the underlying uncertainty of preferences and may lead to bias outcomes.
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