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Algorithm selection by rational metareasoning as a model of human strategy selection

2014

Conference Paper

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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

Department(s): Rationality Enhancement
Research Project(s): Metacognitive Learning
Bibtex Type: Conference Paper (inproceedings)
Attachments:

BibTex

@inproceedings{lieder2014algorithm,
  title = {Algorithm selection by rational metareasoning as a model of human strategy selection},
  author = {Lieder, Falk and Plunkett, Dillon and Hamrick, Jessica B. and Russell, Stuart J. and Hay, Nicholas J. and Griffiths, Thomas L.},
  booktitle = {Advances in Neural Information Processing Systems 27},
  year = {2014}
}