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Bayesian updating rules in continuous opinion dynamics models

Martins, Andre C. R. Universidade De São Paulo

Journal of Statistical Mechanics-theory and Experiment

IOP PUBLISHING LTD 2009

Acesso online

  • Título:
    Bayesian updating rules in continuous opinion dynamics models
  • Autor: Martins, Andre C. R.
  • Universidade De São Paulo
  • Assuntos: Critical Phenomena Of Socio-Economic Systems; Interacting Agent Models; Mechanics; Physics; Mathematical
  • É parte de: Journal of Statistical Mechanics-theory and Experiment
  • Descrição: Here, I investigate the use of Bayesian updating rules applied to modeling how social agents change their minds in the case of continuous opinion models. Given another agent statement about the continuous value of a variable, we will see that interesting dynamics emerge when an agent assigns a likelihood to that value that is a mixture of a Gaussian and a uniform distribution. This represents the idea that the other agent might have no idea about what is being talked about. The effect of updating only the first moments of the distribution will be studied, and we will see that this generates results similar to those of the bounded confidence models. On also updating the second moment, several different opinions always survive in the long run, as agents become more stubborn with time. However, depending on the probability of error and initial uncertainty, those opinions might be clustered around a central value.
  • DOI: 10.1088/1742-5468/2009/02/P02017
  • Títulos relacionados: Journal of Statistical Mechanics-theory and Experiment
  • Editor: IOP PUBLISHING LTD
  • Data de publicação: 2009
  • Idioma: Inglês

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