Categorization with limited resources: A family of simple heuristics

In categorization tasks where resources such as time, information, and computation are limited, there is pressure to be accurate, and stakes are high - as when deciding if a patient is under high risk of having a disease or if a worker should undergo retraining -, it has been proposed that people use, or should use, simple adaptive heuristics. We introduce a family of deterministic, noncompensatory heuristics, called fast and frugal trees, and study them formally. We show that the heuristics require few resources and are also relatively accurate. First, we characterize fast and frugal trees mathematically as lexicographic heuristics and as noncompensatory linear models, and also show that they exploit cumulative dominance (the results are interpreted in the language of the paired comparison literature). Second, we show, by computer simulation, that the predictive accuracy of fast and frugal trees compares well with that of logistic regression (proposed as a descriptive model for categorization tasks performed by professionals) and of classification and regression trees (used, outside psychology, as prescriptive models). (C) 2008 Elsevier Inc. All rights reserved.