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Fiducial cost-benefit analysis research: with an application to weather modification

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  • Beare, Stephen
  • Chambers, Raymond

Abstract

Environmental intervention is often seen as being high risk and high return. Traditional scientific hypothesis testing provides limited guidance to policy makers unless there is a high level of certainty in the supporting scientific evidence. Traditional cost-benefit analysis under uncertainty has shortcomings when considering high-risk investment, largely due to the choice of how to discount uncertainty outcomes. A corollary is that traditional cost-benefit analysis does not place a value on increased certainty, an important outcome of successful scientific research. A fiducial costbenefit methodology is presented in this paper, which integrates hypothesis testing and traditional cost-benefit analysis. The fiducial approach is one way of objectively placing a value on changes in the level of uncertainty that does not depend on an assumption about a decision maker's attitudes towards variability in returns. This has two important implications. First, there is a level of uncertainty at which we would reject an investment with a positive expected net rate of return on the basis that the uncertainty associated with the outcome is too great. Second, it is possible to value a program of research that reduces the uncertainty about a critical decision parameter. An example based on data from a weather modification experiment conducted in South Australia is presented. The approach is the generalised using more traditional statistical methodology.

Suggested Citation

  • Beare, Stephen & Chambers, Raymond, 2012. "Fiducial cost-benefit analysis research: with an application to weather modification," 2012 Conference (56th), February 7-10, 2012, Fremantle, Australia 124232, Australian Agricultural and Resource Economics Society.
  • Handle: RePEc:ags:aare12:124232
    DOI: 10.22004/ag.econ.124232
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    References listed on IDEAS

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    1. Jan Hannig & Thomas C. M. Lee, 2009. "Generalized fiducial inference for wavelet regression," Biometrika, Biometrika Trust, vol. 96(4), pages 847-860.
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