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Fuzzy Logic and Preference Uncertainty in Non-market Valuation


  • Lili Sun
  • G. Cornelis van Kooten


In seeking to value environmental amenities and public goods, individuals often have trouble trading off the (vague) amenity or good against a monetary measure. Valuation in these circumstances can best be described as fuzzy in terms of the amenity valued, perceptions of property rights, and the numbers chosen to reflect values. In this paper, we apply fuzzy logic to contingent valuation, employing a fuzzy clustering approach for incorporating preference uncertainty obtained from a follow-up certainty confidence question. We develop a Fuzzy Random Utility Maximization (FRUM) framework where the perceived utility of each individual is fuzzy in the sense that an individual’s utility belongs to each cluster to some degree. The model is then applied to a Swedish survey that elicited residents’ willingness to pay for enhanced forest conservation. The results from fuzzy models are generally ‘better’ than those obtained using the traditional random utility framework.

Suggested Citation

  • Lili Sun & G. Cornelis van Kooten, 2005. "Fuzzy Logic and Preference Uncertainty in Non-market Valuation," Working Papers 2005-11, University of Victoria, Department of Economics, Resource Economics and Policy Analysis Research Group.
  • Handle: RePEc:rep:wpaper:2005-11

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    Cited by:

    1. Shaw, W. Douglass & Woodward, Richard T., 2008. "Why environmental and resource economists should care about non-expected utility models," Resource and Energy Economics, Elsevier, vol. 30(1), pages 66-89, January.

    More about this item


    random utility maximization and fuzzy logic; contingent valuation and preference uncertainty; c-means clustering; forest conservation;

    JEL classification:

    • Q51 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Valuation of Environmental Effects
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions


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