Premium
Preference‐Order Recursion for Finding Relevant Pure, Admissible and Optimal Statistical Decision Functions *
Author(s) -
Moskowitz Herbert,
Wallenius Jyrki
Publication year - 1990
Publication title -
decision sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.238
H-Index - 108
eISSN - 1540-5915
pISSN - 0011-7315
DOI - 10.1111/j.1540-5915.1990.tb00331.x
Subject(s) - recursion (computer science) , pruning , decision maker , mathematical optimization , preference , mathematics , optimal decision , function (biology) , order (exchange) , computer science , expected utility hypothesis , decision theory , algorithm , decision tree , mathematical economics , artificial intelligence , statistics , operations research , finance , evolutionary biology , agronomy , economics , biology
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.