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Simulation code for the paper “No Stratification Without Representation” by Gerdus Benadè, Paul Gölz, and Ariel D. Procaccia.
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README.md
data14.csv
data16.csv
experiments.ipynb
manual14.txt
manual14_tax.txt
manual16.txt
manual16_helpsick.txt
run_mskmeans.py
variance.py

README.md

Read me

This repository contains the simulation code for the paper

Gerdus Benadè, Paul Gölz, and Ariel D. Procaccia: No Stratification Without Representation. 2019.

The paper is freely available at https://paulgoelz.de/papers/sortition.pdf.

Requirements

We used the following software and libraries in the indicated versions. Newer versions will probably work, but haven’t been tested.

  • Python 3.6
  • Gurobi 8.0.1
  • Pulp 1.20 with access to Gurobi
  • Matplotlib 2.2.2
  • Numpy 1.14.5
  • Pandas 0.23.4
  • Seaborn 0.9.0

For academic use, Gurobi provides free licenses at http://www.gurobi.com/academia/for-universities.

Replication of experiments in the paper

The experiments are provided as a Jupyter/IPython notebook, experiments.ipynb. If you just want to read it, the easiest way is to go to the corresponding page on Github, e.g., https://github.com/pgoelz/sortition/blob/master/experiments.ipynb, where they can be viewed in a browser. If you want to replicate our results or experiment with different parameter settings, you need to install the dependencies mentioned above. Then, running jupyter notebook experiments.ipynb opens a browser window, in which you can see our simulation results and easily rerun them.

While we fix the random seed to 0 in all our experiments, your simulations might produce slightly different results due to different library versions being used, non-determinism in these libraries or Python implementation details such as the iteration order over dictionaries. For what it is worth, we ran our simulations on a MacBook Pro (2017) with a 3.1 GHz Intel Core Duo i5 processor, with 16 GB of RAM, and running MacOS 10.12.6.

Questions

For questions on the simulations, please contact Paul Gölz.

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