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Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
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A Conjugate Class of Utility Functions for Sequential Decision Problems.

Brett Houlding1, Frank P A Coolen2, Donnacha Bolger1

  • 1Discipline of Statistics, Trinity College Dublin, Dublin, Ireland.

Risk Analysis : an Official Publication of the Society for Risk Analysis
|April 9, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a new class of utility functions for Bayesian decision analysis. These functions simplify complex sequential decision problems by leveraging a conjugacy property, similar to its use in Bayesian inference.

Keywords:
Decision analysismatched updatingnormative choice theorypreference modelingrisk analysisutility theory

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Area of Science:

  • Bayesian statistics
  • Decision analysis
  • Probability theory

Background:

  • Conjugacy properties are widely used in Bayesian statistical analysis for tractable inference.
  • Similar properties for utility functions in Bayesian decision analysis are underdeveloped.
  • This limits the simplicity and tractability of sequential decision problems.

Purpose of the Study:

  • To explore a novel class of utility functions for Bayesian decision analysis.
  • To develop functions that are both realistic for decision-maker preferences and analytically tractable.
  • To enable simpler solutions for sequential decision problems.

Main Methods:

  • Investigated a specific class of utility functions.
  • Focused on properties that allow for analytical tractability within sequential decision frameworks.
  • Analogous to the conjugacy property used in Bayesian inference.

Main Results:

  • Identified a class of utility functions that permit tractable analysis.
  • These functions are suitable for modeling real-life decision-maker preferences.
  • Demonstrated potential for simplifying sequential decision problems.

Conclusions:

  • The proposed utility functions offer a path to simpler Bayesian decision analysis.
  • This work bridges a gap between Bayesian inference and decision analysis methodologies.
  • Facilitates more accessible modeling of sequential decisions.