W.A. Brock
Estimating a Path through a Map of Decision Making
Brock, W.A.; Bentley, R.A.; O'Brien, M.J.; Caiado, C.C.S.
Authors
R.A. Bentley
M.J. O'Brien
Professor Camila Caiado c.c.d.s.caiado@durham.ac.uk
Deputy Executive Dean (Impact and Research Engagement)
Abstract
Studies of the evolution of collective behavior consider the payoffs of individual versus social learning. We have previously proposed that the relative magnitude of social versus individual learning could be compared against the transparency of payoff, also known as the “transparency” of the decision, through a heuristic, two-dimensional map. Moving from west to east, the estimated strength of social influence increases. As the decision maker proceeds from south to north, transparency of choice increases, and it becomes easier to identify the best choice itself and/or the best social role model from whom to learn (depending on position on east–west axis). Here we show how to parameterize the functions that underlie the map, how to estimate these functions, and thus how to describe estimated paths through the map. We develop estimation methods on artificial data sets and discuss real-world applications such as modeling changes in health decisions.
Citation
Brock, W., Bentley, R., O'Brien, M., & Caiado, C. (2014). Estimating a Path through a Map of Decision Making. PLoS ONE, 9(11), Article e111022. https://doi.org/10.1371/journal.pone.0111022
Journal Article Type | Article |
---|---|
Online Publication Date | Nov 4, 2014 |
Publication Date | Nov 1, 2014 |
Deposit Date | Sep 29, 2015 |
Publicly Available Date | Oct 9, 2015 |
Journal | PLoS ONE |
Electronic ISSN | 1932-6203 |
Publisher | Public Library of Science |
Peer Reviewed | Peer Reviewed |
Volume | 9 |
Issue | 11 |
Article Number | e111022 |
DOI | https://doi.org/10.1371/journal.pone.0111022 |
Public URL | https://durham-repository.worktribe.com/output/1401837 |
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http://creativecommons.org/licenses/by/4.0/
Copyright Statement
Copyright: © 2014 Brock et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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