Volume 2, Issue 2
Review Article: Finite Size Effects in the Dynamics of Opinion Formation

R. Toral & C. J. Tessone

DOI:

Commun. Comput. Phys., 2 (2007), pp. 177-195.

Published online: 2007-02

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  • Abstract

For some models of relevance in the social sciences we review some examples in which system size plays an important role in the final outcome of the dynamics. We discuss the conditions under which changes of behavior can appear only when the number of agents in the model takes a finite value. Those changes of behavior can be related to the apparent phase transitions that appear in some physical models. We show examples in the Galam's model of opinion transmission and the Axelrod's model of culture formation stressing the role that the network of interactions has on the main results of both models. Finally, we present the phenomenon of system-size stochastic resonance by which a forcing signal (identified as an advertising agent) is optimally amplified by a population of the right (intermediate) size. Our work stresses the role that the system size has in the dynamics of social systems and the inappropriateness of taking the thermodynamic limit for these systems.

  • Keywords

Dynamics of social systems, finite size effects, scaling laws.

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COPYRIGHT: © Global Science Press

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@Article{CiCP-2-177, author = {}, title = {Review Article: Finite Size Effects in the Dynamics of Opinion Formation}, journal = {Communications in Computational Physics}, year = {2007}, volume = {2}, number = {2}, pages = {177--195}, abstract = {

For some models of relevance in the social sciences we review some examples in which system size plays an important role in the final outcome of the dynamics. We discuss the conditions under which changes of behavior can appear only when the number of agents in the model takes a finite value. Those changes of behavior can be related to the apparent phase transitions that appear in some physical models. We show examples in the Galam's model of opinion transmission and the Axelrod's model of culture formation stressing the role that the network of interactions has on the main results of both models. Finally, we present the phenomenon of system-size stochastic resonance by which a forcing signal (identified as an advertising agent) is optimally amplified by a population of the right (intermediate) size. Our work stresses the role that the system size has in the dynamics of social systems and the inappropriateness of taking the thermodynamic limit for these systems.

}, issn = {1991-7120}, doi = {https://doi.org/}, url = {http://global-sci.org/intro/article_detail/cicp/7902.html} }
TY - JOUR T1 - Review Article: Finite Size Effects in the Dynamics of Opinion Formation JO - Communications in Computational Physics VL - 2 SP - 177 EP - 195 PY - 2007 DA - 2007/02 SN - 2 DO - http://dor.org/ UR - https://global-sci.org/intro/article_detail/cicp/7902.html KW - Dynamics of social systems, finite size effects, scaling laws. AB -

For some models of relevance in the social sciences we review some examples in which system size plays an important role in the final outcome of the dynamics. We discuss the conditions under which changes of behavior can appear only when the number of agents in the model takes a finite value. Those changes of behavior can be related to the apparent phase transitions that appear in some physical models. We show examples in the Galam's model of opinion transmission and the Axelrod's model of culture formation stressing the role that the network of interactions has on the main results of both models. Finally, we present the phenomenon of system-size stochastic resonance by which a forcing signal (identified as an advertising agent) is optimally amplified by a population of the right (intermediate) size. Our work stresses the role that the system size has in the dynamics of social systems and the inappropriateness of taking the thermodynamic limit for these systems.

R. Toral & C. J. Tessone. (2020). Review Article: Finite Size Effects in the Dynamics of Opinion Formation. Communications in Computational Physics. 2 (2). 177-195. doi:
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