Despite the vast number of models that have been developed for analyzing stakeholders' preferences, it is difficult to find any true out-of-sample validation for these models. Based on the theory of rational preference, utilities are specific to the individual. Unlike subjective probability, there is no mechanism for changing utilities on the basis of observation, and no operation for getting people's utilities to converge. The proper goal of stakeholder preference modeling must therefore be the characterization of a population of stakeholders via a distribution over utility functions. Drawing on the theory of discrete choice and random utility theory, we apply probabilistic inversion methods to derive a distribution over utility functions. The utility functions may either attach to the choice alternatives directly, or may be functions of physical attributes. Because the utilities are inferred from discrete choice data, out-of-sample validation is enabled by splitting the data into a test set used to fit the model and a validation set. These techniques are illustrated using discrete choice data for the valuation of health states. Copyright © 2011 John Wiley & Sons, Ltd.
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