Learning from small samples: An analysis of simple decision heuristics

Özgür Şimşek, Marcus Buckmann

Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

14 Citations (SciVal)


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. It is unknown how quickly these heuristics can be learned from experience. We show, analytically and empirically, that only a few training samples lead to substantial progress in learning. We focus on three families of heuristics: single-cue decision making, lexicographic decision making, and tallying. Our empirical analysis is the most extensive to date, employing 63 natural data sets on diverse subjects.
Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 28 (NIPS 2015): Annual Conference on Neural Information Processing Systems, Montreal, Quebec, Canada, December 7-12, 2015
EditorsC. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, R. Garnett
PublisherCurran Associates, Inc.
Number of pages9
Publication statusPublished - 1 Jan 2015


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