Learning From Small Samples: An Analysis of Simple Decision Heuristics

Abstract

Simple decision heuristics are models of human and animal behavior that use few pieces of information-perhaps only a single piece of information-and integrate the pieces in simple ways, for example, by considering them sequentially, one at a time, or by giving them equal weight.We focus on three families of heuristics: single-cue decision making, lexicographic decision making, and tallying.It is unknown how quickly these heuristics can be learned from experience.We show, analytically and empirically, that substantial progress in learning can be made with just a few training samples.When training samples are very few, tallying performs substantially better than the alternative methods tested.Our empirical analysis is the most extensive to date, employing 63 natural data sets on diverse subjects.

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