Information Feedback and Mass Media Effects in Cultural Dynamics
Journal of Artificial Societies and Social Simulation
vol. 10, no. 3 9
<https://www.jasss.org/10/3/9.html>
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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  
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