Simulations to understand the tipping point
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Plotter.R
README.md
StandardNameGame.py
example_simulation.sh
figure.png

README.md

#CommittedMinorities

Simulator for the social dynamics of committed minorities (based on the paper from Xie et al., 2011).

Answers the question: "In a population where everyone uses norm B, what fraction of the population needs to be committed to a new norm A to sway the majority to adopt this new minority convention?"

Answer: ~10%

With 2 norms, there are 2 fixed points in the system that vary as a function of the proportion of the population that are committed agents. There is a phase transition from a regime where there is virtually no adoption when there is less than ~10% committed agents to a regime where there is universal adoption above this threshold.

The Naming Game is a model of convention formation from Baronchelli et al. It has been used to study the effect of network structure on convention formation. Some important findings are here.

The simulations here are based on research from Xie et al., 2011.

#To Run To run these simulations, place the .py files in a directory. Then, from the command line:

python StandardNameGame.py <population size> <number of simulations> <file number> <output path>

This will output a .csv file with the results.

Then, to plot the results, run:

Rscript.exe Plotter.R

Alternatively, you can run the example simulation using the shell script from the command line:

example_simulation.sh

Please change the output directory in example_simulation.sh prior to running.

This will produce the figure Results.png. It should look like the following, which shows a critical threshold at ~10%:

Simulation of Committed Minorities

Requirements

  • pandas
  • ggplot2
  • dplyr