Computational modelling with uncertainty of frequent users of e-commerce in Spain using an age-group dynamic nonlinear model with varying size population

[EN] Electronic commerce (EC) has numerous advantages. It allows saving time when we purchase an item, offers the possibility of review without depending on the schedules of traditional stores, access to a wider variety and quantity of articles, in many cases, with lower prices, etc. Based upon math...

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Detalhes bibliográficos
Autores: Burgos-Simon, Clara|||0000-0001-6385-4263, Cortés, J.-C.|||0000-0002-6528-2155, Villanueva Micó, Rafael Jacinto|||0000-0002-0131-0532, Martínez-Rodríguez, David
Formato: artículo
Fecha de publicación:2019
País:España
Recursos:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/139655
Acesso em linha:https://riunet.upv.es/handle/10251/139655
Access Level:acceso abierto
Palavra-chave:Electronic Commerce
Real-world mathematical model
Nonlinear system of difference equations
Uncertainty quantification
MATEMATICA APLICADA
Descrição
Resumo:[EN] Electronic commerce (EC) has numerous advantages. It allows saving time when we purchase an item, offers the possibility of review without depending on the schedules of traditional stores, access to a wider variety and quantity of articles, in many cases, with lower prices, etc. Based upon mathematical epidemiology tenets strongly related to social behavior able to describe the influence of peers, in this paper we propose an age-group dynamic model with population varying size based on a system of difference equations to study the evolution of the frequent users of EC over time in Spain. Using data from surveys retrieved from the Spanish National Statistics Institute, we use and design computational algorithms to perform a probabilistic estimation of the model parameters that allow the model output to capture the data uncertainty. Then, we will be able to perform a precise prediction with uncertainty.