Information Feedback and Mass Media Effects in Cultural Dynamics
Journal of Artificial Societies and Social Simulation
vol. 10, no. 3 9
<http://jasss.soc.surrey.ac.uk/10/3/9.html>
For information about citing this article, click here
Received: 11Jan2007 Accepted: 18May2007 Published: 30Jun2007
if people tend to become more alike in their beliefs, attitudes, and behavior when they interact, why do not all differences eventually disappear?
Figure 1: Diagrams representing two types of direct, endogenous mass media influences acting on the system. a) Global mass media. b) Local mass media 
(1) Select at random an agent i on the lattice (called active agent).
(2) Select the source of interaction . With probability set as an interaction with the mass media vector. Otherwise, choose agent at random among the four nearest neighbors of i on the network.
(3) Calculate the cultural overlap (number of shared features) . If , sites i and interact with probability . In case of interaction, choose randomly such that and set .
Here we use the definition of the Kronecker's delta function
(4) Update the mass media vector if required. Resume at (1).
Figure 2: Evolution of in a system subject to a global mass media message for different values of the probability , with fixed . Time is measured in number of events per site. System size . Left: ; (crosses); (squares); (diamonds); (circles). Right: ; (crosses); (squares); (circles); (diamonds). 
Figure 3: Asymptotic value of the fraction of cultural domains as a function of , for different values of the probability and for different types of mass media influences. (diamonds); (solid squares, direct global mass media); (empty squares, direct global mass media); (solid circles, direct local mass media); (empty circles, direct local mass media). 
Figure 4: Asymptotic cultural configurations for different values of the probability for a direct global mass media influence, for , , and . Top left: ; top right: ; bottom left: ; bottom right: 
Figure 5: Threshold boundaries vs. for corresponding to the global and mass media. Each line separates the region of cultural diversity (above the line, in grey) from the region of a global culture (below the line) for direct global (circles) and local (triangles) mass media influences. 
Figure 6:
Cultural configurations for different values of the probability
for different mass media influences in the multicultural region,
for , , and
. Left: no mass media ; center:
Global mass media with ; right: Local mass media with . (see Movie 1) 
(1) Select at random an agent i on the lattice (active agent).
(2) Select at random one agent among the four neighbors of i.
(3) Calculate the overlap . If , sites i and interact with probability . In case of interaction, choose randomly such that . If , then set ; otherwise with probability the state of agent i does not change and with probability set
(4) Update the global mass media vector if required. Resume at (1).
Figure 7: Diagram representing the filter model. 
Figure 8: Time evolution of the average fraction of cultural domains in the filter model for different values of the probability , with fixed . Time is measured in number of events per site. System size . Left: ; (crosses); (squares); (diamonds); (circles). Right: ; (crosses); (squares); (circles); (diamonds). 
Figure 9: Average fraction of cultural domains as a function of , for different values of the probability for the filter model. (circles); (squares); (triangles down); (diamonds); (triangles up); (stars); (plus signs). 
Figure 10: For and , there are different cultural states that can be assigned different colors as shown. 
Table 1: Key to formulae employed  
1.  Number of shared features between agent i and agent j


2.  The probability that the agent i interacts with the agent j


3.  The total probability that the agent i interacts with the mass media


4.  The overlap between agent i and the mass media messages


5.  The average fraction of cultural domains


6.  Definition of Kronecker's delta function  
BHAVNANI R, (2003), Adaptive Agents, Political Institutions and Civic Traditions in Modern Italy. Journal of Artificial Societies and Social Simulation, 6, no 4, http://jasss.soc.surrey.ac.uk/6/4/1.html.
CASTELLANO C, MARSILI M, and VESPIGNANI A, (2000), Nonequilibrium Phase Transition in a Model for Social Influence. Phys. Rev. Lett. 85, pp. 35363539.
CENTOLA D, GONZALEZAVELLA J C, EGUILUZ V M, and SAN MIGUEL M, (2006), Homophily, Cultural Drift and the CoEvolution of Cultural Groups. http://arxiv.org/abs/physics/0609213.
FLACHE A and MACY M,(2006), What sustains cultural diversity and what undermines it? Axelrod and beyond. http://arxiv.org/abs/physics/0604201.
GONZALEZAVELLA J C, COSENZA M G, and TUCCI K, (2005), Nonequilibrium transition induced by mass media in a model for social influence. Phys. Rev. E, 72, 065102(R).
GONZALEZAVELLA J C, EGUILUZ V M, COSENZA M G, KLEMM K, HERRERA J L, and SAN MIGUEL M, (2006), Local versus global interactions in nonequilibrium transitions: A model of social dynamics. Phys. Rev. E 73, 046119.
GREIG J, (2002), The End of Geography? Globalization, Communications, and Culture in the International System. J. Conflict Res. 46, pp. 225243.
KLEMM K, EGUILUZ V M, TORAL R, and SAN MIGUEL M, (2003), Global culture: A noiseinduced transition in finite systems. Phys. Rev. E. 67, 045101(R).
KLEMM K, EGUILUZ V M, TORAL R, and SAN MIGUEL M, (2003), Role of dimensionality in Axelrod's model for the dissemination of culture. Physica A, 327, pp. 15.
KLEMM K, EGUILUZ V M , TORAL R, and SAN MIGUEL M, (2003), Nonequilibrium transition in complex networks: A model of social interaction. Phys. Rev. E. 67, pp. 026120.
KLEMM K, EGUILUZ V M , TORAL R, and SAN MIGUEL M, (2005), Globalization, polarization and cultural drift. J. Econ. Dyn. Control 29, pp 321334.
LEYDESDORFF L, (2001), Technology and Culture: The Dissemination and the Potential 'Lockin' of New Technologies. Journal of Artificial Societies and Social Simulation, 6(4)5, http://jasss.soc.surrey.ac.uk/4/3/5.html.
MACY M W, KITTS J, FLACHE A and BERNARD S, (2003). Polarization in Dynamic Networks: A Hopfield Model of Emergent Structure. In Dynamic Social Network Modeling and Analysis. R. Breiger, K. Carley and P. Pattison (Eds.), National Academies Press (Washington, 2003), pp. 162173.
SAN MIGUEL M, EGUILUZ V M, TORAL R, and KLEMM K, (2005), Binary and Multivariate Stochastic Models of Consensus Formation. Computing in Science & Engineering. 7, pp. 6773.
SHIBANAI Y, YASUNO S, and ISHIGURO I, (2001), Effects of Global Information Feedback on Diversity: Extensions to Axelrod's Adaptative Culture Model. J. Conflict Res. 45, pp. 8096.
Return to Contents of this issue
© Copyright Journal of Artificial Societies and Social Simulation, [2007]