Preference‐Order Recursion for Finding Relevant Pure, Admissible and Optimal Statistical Decision Functions

Research output: Contribution to journalArticleScientificpeer-review

Researchers

Research units

  • Purdue University

Abstract

A preference‐order recursion algorithm for obtaining a relevant subset of pure, admissible (non‐dominated, efficient) decision functions which converges towards an optimal solution in statistical decision problems is proposed. The procedure permits a decision maker to interactively express strong binary preferences for partial decision functions at each stage of the recursion, from which an imprecise probability and/or utility function is imputed and used as one of several pruning mechanisms to obtain a reduced relevant subset of admissible decision functions or to converge on an optimal one. The computational and measurement burden is thereby mitigated significantly, for example, by not requiring explicit or full probability and utility information from the decision maker. The algorithm is applicable to both linear and nonlinear utility functions. The results of behavioral and computational experimentation show that the approach is viable, efficient, and robust.

Details

Original languageEnglish
Pages (from-to)521-532
Number of pages12
JournalDecision Sciences
Volume21
Issue number3
Publication statusPublished - 1990
MoE publication typeA1 Journal article-refereed

    Research areas

  • Decision Analysis, Risk and Uncertainty, Statistical Decision Theory

ID: 9890116