Guided rewiring of social networks reduces polarization and accelerates collective action

by Lilli Frei, Jordan Everall, Andrew K. Ringsmuth

This paper has been submitted for publication in Nature Scientific Reports.

In this project, we use an agent based model where agents are embedded in a social network to analyse the effect of different rewiring strategies on the speed and magnitude of cooperative consensus formation, or depolariazion in social groups. We are interested in these dynamics because socio-political polarization is a major barrier to collective action problems such as climate change, which must urgently be addressed. We investigate rewiring algorithms representing random meetings, introduction by mutual acquaintances, and bridging between socially distant communities. We find that building lasting links between polarized individuals and communities can accelerate consensus formation when the sociopolitical environment is favourable. This strengthens the evidence that promoting connection between polarized communities could accelerate collective action on urgent global challenges.  

Comparing network evolutions for all rewiring algorithms

Rewiring leads to accelerated cooperative opinion formation unless homophily is enforced when establishing links (i.e. (similar) rewiring). Bridge(opposite) and local(opposite) rewiring achieve the highest convergence rates. Local(similar) leads to the lowest convergence rate.

Software implementation

All source code used to generate the results and figures in the paper are in the Analysis folder. The model itself is containted in models_checks.py, run.py runs the model. All sensitivity analyses are run by scripts ending in "..parameter_sweep". Results generated by the code are saved in Output, and figures in Figs.

Structure

├── README.md
├── requirements_no_win.txt
├── Analysis
│   ├── convergence_plots_paper_v3.py
│   ├── general_parameter_sweep.py
│   ├── heatmap_parameter_sweep.py
│   ├── heatmap_plots.py
│   ├── heatmap_plots_lilli.py
│   ├── models_checks.py
│   ├── network_analysis.py
│   ├── plots.py
│   ├── plots_paper.py
│   ├── rewiring_parameter_sweep.py
│   └── run.py
├── Figs
│   └── Various figures in PDF and PNG formats
├── Output
│   └── Various output files in CSV and PNG formats
└── Output_backup
    └── Backup of output files

Getting the code

You can download a copy of all the files in this repository by cloning the git repository:

git clone https://github.com/lifrei/It-s_how_we_talk_that_matters.git

or download a zip archive.

Dependencies

You'll need a working Python environment to run the code. The recommended way to set up your environment is through the Anaconda Python distribution which provides the conda package manager. Anaconda can be installed in your user directory and does not interfere with the system Python installation. The required dependencies are specified in the file environment.yml, as well as requirements_no_win.txt .

We use conda virtual environments to manage the project dependencies in isolation. Thus, you can install our dependencies without causing conflicts with your setup (even with different Python versions).

Run the following command in the repository folder (where environment.yml is located) to create a separate environment and install all required dependencies in it:

conda env create

Reproducing the results

Before running any code you must activate the conda environment:

source activate ENVIRONMENT_NAME

or, if you're on Windows:

activate ENVIRONMENT_NAME

This will enable the environment for your current terminal session. Any subsequent commands will use software that is installed in the environment.

License

All source code is made available under a BSD 3-clause license. You can freely use and modify the code, without warranty, so long as you provide attribution to the authors. See LICENSE.md for the full license text.

The manuscript text is not open source. The authors reserve the rights to the article content, which is currently submitted for publication in Nature Human Behaviour.