Modeling and validating stakeholder preferences with probabilistic inversion

REJ Neslo, RM Cooke

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

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.
LanguageEnglish
Pages115-130
Number of pages16
JournalApplied Stochastic Models in Business and Industry
Volume27
Issue number2
DOIs
Publication statusPublished - Apr 2011

Fingerprint

Discrete Choice
Inversion
Utility Function
Modeling
Preference Modelling
Subjective Probability
Utility Theory
Test Set
Valuation
Health
Attribute
Model
Converge
Discrete choice
Stakeholders
Utility function
Alternatives

Keywords

  • out-of-sample validation
  • discrete choice
  • random utility
  • probabilistic inversion
  • valuation of health states

Cite this

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Modeling and validating stakeholder preferences with probabilistic inversion. / Neslo, REJ; Cooke, RM.

In: Applied Stochastic Models in Business and Industry, Vol. 27, No. 2, 04.2011, p. 115-130.

Research output: Contribution to journalArticle

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