Bootstrapping of Data and Decisions

Abstract

Bootstrapping involves the substitution of a simple linear model of judgments in place of the judgments themselves. It has been found that in many decision making contexts the bootstrapped decisions are better than the judgments from which they were derived. It appears that the linear model is quite successful at capturing the policy of the judge and then making decisions without human inconsistency. Most of the work done on bootstrapping has been done in a context-such as forecasts-where the criterion or accuracy is clearly defined. This paper reviews past work done in bootstrapping and shows that it can be used to upgrade the quality of subjective judgments (data). These judgments have no ultimate criterion of accuracy but are evaluated in terms of their usefulness as input to a predictive model. Implications are explored as to the use of bootstrapping of both data and decisions in consumer behavior. Bootstrapping of decisions has generally taken the