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.
|Title of host publication||Advances in Neural Information Processing Systems 28 (NIPS 2015): Annual Conference on Neural Information Processing Systems, Montreal, Quebec, Canada, December 7-12, 2015|
|Editors||C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, R. Garnett|
|Publisher||Curran Associates, Inc.|
|Number of pages||9|
|Publication status||Published - 2015|
Şimşek, Ö., & Buckmann, M. (2015). Learning from small samples: An analysis of simple decision heuristics. In C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, & R. Garnett (Eds.), Advances in Neural Information Processing Systems 28 (NIPS 2015): Annual Conference on Neural Information Processing Systems, Montreal, Quebec, Canada, December 7-12, 2015 (pp. 3159-3167). Curran Associates, Inc..