Optimal prediction of decisions and model selection in social dilemmas using block models

Abstract Advancing our understanding of human behavior hinges on the ability of theories to unveil the mechanisms underlying such behaviors. Measuring the ability of theories and models to predict unobserved behaviors provides a principled method to evaluate their merit and, thus, to help establish which mechanisms are most plausible. Here, we propose models and develop rigorous inference approaches to predict strategic decisions in dyadic social dilemmas. In particular, we use bipartite stochastic block models that incorporate information about the dilemmas faced by individuals. We show, combining these models with empirical data on strategic decisions in dyadic social dilemmas, that individual strategic decisions are to a large extent predictable, despite not being “rational.” The analysis of these models also allows us to conclude that: (i) individuals do not perceive games according their game-theoretical structure; (ii) individuals make decisions using combinations of multiple simple strategies, which our approach reveals naturally.

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PID https://www.doi.org/10.6084/m9.figshare.c.4302464
PID https://www.doi.org/10.6084/m9.figshare.c.4302464.v1
URL http://dx.doi.org/10.6084/m9.figshare.c.4302464
URL http://hdl.handle.net/20.500.11797/imarina5133293
URL http://dx.doi.org/10.6084/m9.figshare.c.4302464.v1
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Access Right Open Access
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Author Cobo-López, Sergio
Author Godoy-Lorite, Antonia
Author Duch, Jordi
Author Sales-Pardo, Marta
Author Guimerà, Roger
Contributor Enginyeria Química
Contributor Enginyeria Informàtica i Matemàtiques
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Collected From Datacite; Repositori Institucional URV
Hosted By figshare; Repositori Institucional URV
Publication Date 2018-01-01
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Country Spain
Language Undetermined
Resource Type Collection; UNKNOWN
keyword Computational Mathematics,Computer Science Applications,Mathematics, Interdisciplinary Applications,Modeling and Simulation,Social Sciences, Mathematical Methods
keyword Social Sciences, Mathematical Methods
keyword FOS: Mathematics
keyword CIÊNCIA DA COMPUTAÇÃO
keyword Mathematics, Interdisciplinary Applications
keyword @uroweb
keyword FOS: Biological sciences
keyword FOS: Computer and information sciences
keyword Etiqueta «#»
keyword @residentesaeu
keyword FOS: Sociology
keyword @infoAeu
system:type other
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Source https://science-innovation-policy.openaire.eu/search/other?orpId=dedup_wf_001::8071db3921cf4c30bc03f817986a54f3
Author jsonws_user
Last Updated 18 December 2020, 18:36 (CET)
Created 18 December 2020, 18:36 (CET)