Abstract
Target-oriented utility theory interprets the utility of a consequence as the probability of the consequence exceeding some benchmark random variable. This shifts the focus of utility assessment to the identification of the benchmark and the sources of uncertainty in that benchmark. Identification of the benchmark is often easy when the benchmark is based on a status quo outcome, a preferred outcome or an undesirable outcome. Benchmarks are generally easy to communicate and easy to track. Once identified, data and models can then be used to describe the uncertainty in the benchmark. This approach can be useful in those applications where the utility function needs to be justified to others.
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Notes
- 1.
Effect size measures (Grissom and Kim 2005), while originally introduced by Fisher and Pearson, as a complement to their statistical significance measure, is now sometimes used in place of it. Arguably one of the most widely used effect size measures, Cohen’s d, is simply the mean difference between experimental and control outcome divided by the standard deviation. It is a special case of the common language effect size measure when all uncertainties are Gaussian.
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Bordley, R.F. (2018). Elicitation in Target-Oriented Utility. In: Dias, L., Morton, A., Quigley, J. (eds) Elicitation. International Series in Operations Research & Management Science, vol 261. Springer, Cham. https://doi.org/10.1007/978-3-319-65052-4_11
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