Bayesian nonparametric models characterize instantaneous strategies in a competitive dynamic game

Game theory typically models strategic human behavior using scenarios with decision constraints that poorly represent real-world social interactions. Here, the authors show it is possible to model dynamic, real-world strategic interactions using Bayesian and reinforcement learning principles.

Guardado en:

Detalles Bibliográficos
Autores principales:	Kelsey R. McDonald, William F. Broderick, Scott A. Huettel, John M. Pearson
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2019
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/7a267ee763994fdba6cd11b8b5c6012e
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:7a267ee763994fdba6cd11b8b5c6012e
record_format	dspace
spelling	oai:doaj.org-article:7a267ee763994fdba6cd11b8b5c6012e2021-12-02T14:38:44ZBayesian nonparametric models characterize instantaneous strategies in a competitive dynamic game10.1038/s41467-019-09789-42041-1723https://doaj.org/article/7a267ee763994fdba6cd11b8b5c6012e2019-04-01T00:00:00Zhttps://doi.org/10.1038/s41467-019-09789-4https://doaj.org/toc/2041-1723Game theory typically models strategic human behavior using scenarios with decision constraints that poorly represent real-world social interactions. Here, the authors show it is possible to model dynamic, real-world strategic interactions using Bayesian and reinforcement learning principles.Kelsey R. McDonaldWilliam F. BroderickScott A. HuettelJohn M. PearsonNature PortfolioarticleScienceQENNature Communications, Vol 10, Iss 1, Pp 1-12 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Kelsey R. McDonald William F. Broderick Scott A. Huettel John M. Pearson Bayesian nonparametric models characterize instantaneous strategies in a competitive dynamic game
description	Game theory typically models strategic human behavior using scenarios with decision constraints that poorly represent real-world social interactions. Here, the authors show it is possible to model dynamic, real-world strategic interactions using Bayesian and reinforcement learning principles.
format	article
author	Kelsey R. McDonald William F. Broderick Scott A. Huettel John M. Pearson
author_facet	Kelsey R. McDonald William F. Broderick Scott A. Huettel John M. Pearson
author_sort	Kelsey R. McDonald
title	Bayesian nonparametric models characterize instantaneous strategies in a competitive dynamic game
title_short	Bayesian nonparametric models characterize instantaneous strategies in a competitive dynamic game
title_full	Bayesian nonparametric models characterize instantaneous strategies in a competitive dynamic game
title_fullStr	Bayesian nonparametric models characterize instantaneous strategies in a competitive dynamic game
title_full_unstemmed	Bayesian nonparametric models characterize instantaneous strategies in a competitive dynamic game
title_sort	bayesian nonparametric models characterize instantaneous strategies in a competitive dynamic game
publisher	Nature Portfolio
publishDate	2019
url	https://doaj.org/article/7a267ee763994fdba6cd11b8b5c6012e
work_keys_str_mv	AT kelseyrmcdonald bayesiannonparametricmodelscharacterizeinstantaneousstrategiesinacompetitivedynamicgame AT williamfbroderick bayesiannonparametricmodelscharacterizeinstantaneousstrategiesinacompetitivedynamicgame AT scottahuettel bayesiannonparametricmodelscharacterizeinstantaneousstrategiesinacompetitivedynamicgame AT johnmpearson bayesiannonparametricmodelscharacterizeinstantaneousstrategiesinacompetitivedynamicgame
_version_	1718390903027007488

Bayesian nonparametric models characterize instantaneous strategies in a competitive dynamic game

Ejemplares similares