This repository is for the publication
Oswald, Malleson, Suchak (2023). An agent-based model of the 2020 international policy diffusion in response to the COVID-19 pandemic with particle filter
available as preprint at https://arxiv.org/abs/2302.11277
This work has been implemented using the Python Anaconda distribution and the agent-based model package MESA in particular.
It is provided with a .yml file specifying a conda environment which contains the required packages.
In order to set up the environment, run the following command from the
terminal/conda prompt:
conda env create -f env.yml
This will create a new conda environment titled cov-pol.
The environment can then be activated using the following command:
conda activate cov-pol
To reproduce the full body of work, take the following steps:
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Run the script file run_base_model_and_filter_with_plotting.py in the
covpoldirectory as follows:cd covpol python run_base_model_and_filter_with_plotting.pyThis reproduces a substantial amount of the above paper including Figure 2, 4 and 5. To reproduce Figure 4 exactly, the notebook has to take the parameter
no_of_iterations = 100. To reproduce Figure 5 exactly, the notebeook has to take the parameterno_of_iterations = 1000. -
To reproduce Figure 6 in full several intermediate steps are necessary (time-expensive):
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Run the script file
number_of_particles_experiment_MSE.pyto reproduce the data points where iterations = 20. -
To reproduce the datapoints where iterations = 1, run the following:
particle_filter_only.pyand collect the data.run_base_model_only_parallelized.pyand collect the data.
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Alternatively Figure 6 can be reproduced exactly in a time-cheap manner as it is from the script file
graph_mse_number_of_particles_experiment.py.
Best run from anaconda command prompt.
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To reproduce Figure 7 run the script
experiment_da_window_size.py(time-expensive). Best run from anaconda command prompt.