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A linear threshold model for optimal stopping behavior

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Baumann, Christiane, Singmann, Henrik, Gershman, Samuel J. and von Helversend, Bettina (2020) A linear threshold model for optimal stopping behavior. Proceedings of the National Academy of Sciences of the United States of America, 117 (23). pp. 12750-12755. doi:10.1073/pnas.2002312117

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Official URL: https://doi.org/10.1073/pnas.2002312117

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Abstract

In many real-life decisions, options are distributed in space and time, making it necessary to search sequentially through them, often without a chance to return to a rejected option. The optimal strategy in these tasks is to choose the first option that is above a threshold that depends on the current position in the sequence. The implicit decision-making strategies by humans vary but largely diverge from this optimal strategy. The reasons for this divergence remain unknown. We present a model of human stopping decisions in sequential decision-making tasks based on a linear threshold heuristic. The first two studies demonstrate that the linear threshold model accounts better for sequential decision making than existing models. Moreover, we show that the model accurately predicts participants’ search behavior in different environments. In the third study, we confirm that the model generalizes to a real-world problem, thus providing an important step toward understanding human sequential decision making.

Item Type: Journal Article
Subjects: B Philosophy. Psychology. Religion > BF Psychology
H Social Sciences > HD Industries. Land use. Labor
Divisions: Faculty of Science > Psychology
Library of Congress Subject Headings (LCSH): Decision making, Optimal stopping (Mathematical statistics), Adaptability (psychology), Cognition
Journal or Publication Title: Proceedings of the National Academy of Sciences of the United States of America
Publisher: National Academy of Sciences
ISSN: 0027-8424
Official Date: 9 June 2020
Dates:
DateEvent
9 June 2020Published
27 May 2020Available
27 April 2020Accepted
Volume: 117
Number: 23
Page Range: pp. 12750-12755
DOI: 10.1073/pnas.2002312117
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
168889[SNSF] Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschunghttp://dx.doi.org/10.13039/501100001711
100014_179121[SNSF] Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschunghttp://dx.doi.org/10.13039/501100001711
157432[SNSF] Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschunghttp://dx.doi.org/10.13039/501100001711
N00014-17-1-2984Office of Naval Researchhttp://dx.doi.org/10.13039/100000006
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