Learning and imitation: transitional dynamics in variants of the BAM

"We study the dynamics of self-organized systems when disturbed by shocks. For this purpose, we consider extensions of the “Bar Attendance Model” [1] (BAM), which provides a stylized setting for the analysis of the emergence of coordination in the behavior of a large collection of agents. We re...

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Autores principales: Heymann, D., Perazzo, Roberto P. J., Schuschny, A. R.
Formato: Artículos de Publicaciones Periódicas
Lenguaje:Inglés
Publicado: 2019
Materias:
Acceso en línea:http://ri.itba.edu.ar/handle/123456789/1484
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spelling I32-R138-123456789-14842022-12-07T13:06:19Z Learning and imitation: transitional dynamics in variants of the BAM Heymann, D. Perazzo, Roberto P. J. Schuschny, A. R. DINAMICA DE GRUPO APRENDIZAJE MODELOS MATEMATICOS "We study the dynamics of self-organized systems when disturbed by shocks. For this purpose, we consider extensions of the “Bar Attendance Model” [1] (BAM), which provides a stylized setting for the analysis of the emergence of coordination in the behavior of a large collection of agents. We represent the learning process of the agents through genetic algorithms, which respond to global (publicly available) information. In addition, we allow the actions of agents to be influenced by local information, as expressed in the behavior and performance of neighboring individuals. In the context of the BAM, we show that, in the event of a shock, the imitation behavior may become widespread and generate a contagion cascade which mimics a collective panic. We use this framework to represent features of the dynamics of an actual bank run." 2019-03-20T14:08:04Z 2019-03-20T14:08:04Z 2004 Artículos de Publicaciones Periódicas 0219-5259 http://ri.itba.edu.ar/handle/123456789/1484 en info:eu-repo/grantAgreementt/EC 9000XX/27/ application/pdf
institution Instituto Tecnológico de Buenos Aires (ITBA)
institution_str I-32
repository_str R-138
collection Repositorio Institucional Instituto Tecnológico de Buenos Aires (ITBA)
language Inglés
topic DINAMICA DE GRUPO
APRENDIZAJE
MODELOS MATEMATICOS
spellingShingle DINAMICA DE GRUPO
APRENDIZAJE
MODELOS MATEMATICOS
Heymann, D.
Perazzo, Roberto P. J.
Schuschny, A. R.
Learning and imitation: transitional dynamics in variants of the BAM
topic_facet DINAMICA DE GRUPO
APRENDIZAJE
MODELOS MATEMATICOS
description "We study the dynamics of self-organized systems when disturbed by shocks. For this purpose, we consider extensions of the “Bar Attendance Model” [1] (BAM), which provides a stylized setting for the analysis of the emergence of coordination in the behavior of a large collection of agents. We represent the learning process of the agents through genetic algorithms, which respond to global (publicly available) information. In addition, we allow the actions of agents to be influenced by local information, as expressed in the behavior and performance of neighboring individuals. In the context of the BAM, we show that, in the event of a shock, the imitation behavior may become widespread and generate a contagion cascade which mimics a collective panic. We use this framework to represent features of the dynamics of an actual bank run."
format Artículos de Publicaciones Periódicas
author Heymann, D.
Perazzo, Roberto P. J.
Schuschny, A. R.
author_facet Heymann, D.
Perazzo, Roberto P. J.
Schuschny, A. R.
author_sort Heymann, D.
title Learning and imitation: transitional dynamics in variants of the BAM
title_short Learning and imitation: transitional dynamics in variants of the BAM
title_full Learning and imitation: transitional dynamics in variants of the BAM
title_fullStr Learning and imitation: transitional dynamics in variants of the BAM
title_full_unstemmed Learning and imitation: transitional dynamics in variants of the BAM
title_sort learning and imitation: transitional dynamics in variants of the bam
publishDate 2019
url http://ri.itba.edu.ar/handle/123456789/1484
work_keys_str_mv AT heymannd learningandimitationtransitionaldynamicsinvariantsofthebam
AT perazzorobertopj learningandimitationtransitionaldynamicsinvariantsofthebam
AT schuschnyar learningandimitationtransitionaldynamicsinvariantsofthebam
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