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. 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.
Cite
Text
Simsek and Buckmann. "Learning from Small Samples: An Analysis of Simple Decision Heuristics." Neural Information Processing Systems, 2015.Markdown
[Simsek and Buckmann. "Learning from Small Samples: An Analysis of Simple Decision Heuristics." Neural Information Processing Systems, 2015.](https://mlanthology.org/neurips/2015/simsek2015neurips-learning/)BibTeX
@inproceedings{simsek2015neurips-learning,
title = {{Learning from Small Samples: An Analysis of Simple Decision Heuristics}},
author = {Simsek, Ozgur and Buckmann, Marcus},
booktitle = {Neural Information Processing Systems},
year = {2015},
pages = {3159-3167},
url = {https://mlanthology.org/neurips/2015/simsek2015neurips-learning/}
}