Accession Number : ADA138506

Title :   Equal Weights, Flat Maxima, and Trivial Decisions.

Descriptive Note : Research rept.,

Corporate Author : UNIVERSITY OF SOUTHERN CALIFORNIA LOS ANGELES SOCIAL SCIENCE RESEARCH INST

Personal Author(s) : John,R S ; Edwards,W ; Von Winterfeldt,D ; Barron,F H

PDF Url : ADA138506

Report Date : Jun 1980

Pagination or Media Count : 28

Abstract : Most predictions are intended as a basis for decision making. The point of this paper is that prediction and decision require different methods. Equal weights, while often useful for prediction, are less useful for decision making. The action options available in any decision problem fall into three classes: sure winners, sure losers, and contenders. Sure winners and sure losers are defined by dominance, accepting sure winners and rejecting sure losers is trivial. Good decision rules should discriminate well among contenders. In the familiar pick-1 decision problem, options on the Pareto frontier (i.e. undominated options) almost always show negative correlations among attributes. Such negative correlations make equal weights inappropriate. This paper extends that result to the case in which a decision maker must pick k options out of n. In this case, the set of sure winners is usually not empty. It develops general procedures for identifying the set of contenders, given the options, k, and n. This set is a generalized Pareto frontier, of which the traditional kind is a special case. Simulations show that attribute intercorrelations among contenders are substantially depressed and typically negative, even if the intercorrelations in the whole set are positive. Such negative correlations among contenders strongly question the usefulness of equal weights for decision making.

Descriptors :   *Decision theory, *Mathematical prediction, Decision making, Cueing, Social sciences, Research management, Game theory, Monte Carlo method

Subject Categories : Psychology
      Numerical Mathematics

Distribution Statement : APPROVED FOR PUBLIC RELEASE