Algorithm selection by rational metareasoning as a model of human strategy selection
Selecting the right algorithm is an important problem in computer science, because the algorithm often has to exploit the structure of the input to be efficient. The human mind faces the same challenge. Therefore, solutions to the algorithm selection problem can inspire models of human strategy selection and vice versa. Here, we view the algorithm selection problem as a special case of metareasoning and derive a solution that outperforms existing methods in sorting algorithm selection. We apply our theory to model how people choose between cognitive strategies and test its prediction in a behavioral experiment. We find that people quickly learn to adaptively choose between cognitive strategies. People's choices in our experiment are consistent with our model but inconsistent with previous theories of human strategy selection. Rational metareasoning appears to be a promising framework for reverse-engineering how people choose among cognitive strategies and translating the results into better solutions to the algorithm selection problem.
| Author(s): | Falk Lieder and Dillon Plunkett and Jessica B. Hamrick and Stuart J. Russell and Nicholas J. Hay and Thomas L. Griffiths |
| Book Title: | Advances in Neural Information Processing Systems 27 |
| Year: | 2014 |
| Project(s): | |
| Bibtex Type: | Conference Paper (inproceedings) |
| Electronic Archiving: | grant_archive |
| Attachments: | |
BibTex
@inproceedings{lieder2014algorithm,
title = {Algorithm selection by rational metareasoning as a model of human strategy selection},
booktitle = {Advances in Neural Information Processing Systems 27},
abstract = {Selecting the right algorithm is an important problem in computer science, because the algorithm often has to exploit the structure of the input to be efficient. The human mind faces the same challenge. Therefore, solutions to the algorithm selection problem can inspire models of human strategy selection and vice versa. Here, we view the algorithm selection problem as a special case of metareasoning and derive a solution that outperforms existing methods in sorting algorithm selection. We apply our theory to model how people choose between cognitive strategies and test its prediction in a behavioral experiment. We find that people quickly learn to adaptively choose between cognitive strategies. People's choices in our experiment are consistent with our model but inconsistent with previous theories of human strategy selection. Rational metareasoning appears to be a promising framework for reverse-engineering how people choose among cognitive strategies and translating the results into better solutions to the algorithm selection problem.},
year = {2014},
slug = {lieder2014algorithm},
author = {Lieder, Falk and Plunkett, Dillon and Hamrick, Jessica B. and Russell, Stuart J. and Hay, Nicholas J. and Griffiths, Thomas L.}
}