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dc.contributor.author
Fernandez Slezak, Diego  
dc.contributor.author
Sigman, Mariano  
dc.contributor.author
Cecchi, Guillermo Alberto  
dc.date.available
2021-07-12T22:22:20Z  
dc.date.issued
2018-03-02  
dc.identifier.citation
Fernandez Slezak, Diego; Sigman, Mariano; Cecchi, Guillermo Alberto; An entropic barriers diffusion theory of decision-making in multiple alternative tasks; Public Library of Science; Plos Computational Biology; 14; 3; 2-3-2018; 1-14  
dc.identifier.issn
1553-734X  
dc.identifier.uri
http://hdl.handle.net/11336/135905  
dc.description.abstract
We present a theory of decision-making in the presence of multiple choices that departs from traditional approaches by explicitly incorporating entropic barriers in a stochastic search process. We analyze response time data from an on-line repository of 15 million blitz chess games, and show that our model fits not just the mean and variance, but the entire response time distribution (over several response-time orders of magnitude) at every stage of the game. We apply the model to show that (a) higher cognitive expertise corresponds to the exploration of more complex solution spaces, and (b) reaction times of users at an on-line buying website can be similarly explained. Our model can be seen as a synergy between diffusion models used to model simple two-choice decision-making and planning agents in complex problem solving.  
dc.format
application/pdf  
dc.language.iso
eng  
dc.publisher
Public Library of Science  
dc.rights
info:eu-repo/semantics/openAccess  
dc.rights.uri
https://creativecommons.org/licenses/by/2.5/ar/  
dc.subject
Decision-making  
dc.subject
Entropic barriers  
dc.subject.classification
Otras Ciencias Naturales y Exactas  
dc.subject.classification
Otras Ciencias Naturales y Exactas  
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS  
dc.title
An entropic barriers diffusion theory of decision-making in multiple alternative tasks  
dc.type
info:eu-repo/semantics/article  
dc.type
info:ar-repo/semantics/artículo  
dc.type
info:eu-repo/semantics/publishedVersion  
dc.date.updated
2021-07-08T16:43:08Z  
dc.identifier.eissn
1553-7358  
dc.journal.volume
14  
dc.journal.number
3  
dc.journal.pagination
1-14  
dc.journal.pais
Estados Unidos  
dc.journal.ciudad
San Francisco  
dc.description.fil
Fil: Fernandez Slezak, Diego. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; Argentina  
dc.description.fil
Fil: Sigman, Mariano. Universidad Torcuato Di Tella; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina  
dc.description.fil
Fil: Cecchi, Guillermo Alberto. IBM Research. Thomas J. Watson Research Center; Estados Unidos  
dc.journal.title
Plos Computational Biology  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1371/journal.pcbi.1005961  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005961