Voter Satisfaction Efficiency

Voter Satisfaction Efficiency

These are some methods for running VSE (Voter Satisfaction Efficiency) simulations for various voting systems.

See Voter Satisfaction Efficiency FAQ for an explanation of the methods and results. To reproduce the figures in the paper:

Installing the code

Requirements: python3, scipy, pydoc

Testing uses pydoc, which should make most things pretty self-documenting.

E.g.:

python3 -m doctest methods.py
python3 -m doctest voterModels.py
python3 -m doctest dataClasses.py
python3 vse.py

Running simulations

Try

$ python3
>>> from vse import CsvBatch, baseRuns, Mav, medianRuns, Score
>>> from voterModels import PolyaModel
>>> csvs = CsvBatch(PolyaModel(), [[Score(), baseRuns], [Mav(), medianRuns]], nvot=5, ncand=4, niter=3)
>>> csvs.saveFile()

and look for the results in SimResults1.csv

• Install python dependencies (eg, scipy) and run "run sims for paper.ipynb"
• Install R dependencies (eg, data.table) and run "create graphs for paper.R". Working directory must be here.
• Install shell dependency (Inkscape for Mac — or, if other platform, edit script) and run "convert_pngs_for_paper.sh"

Recreating figures for STAR voting paper

To reproduce the figures in the paper:

• Install python dependencies (eg, scipy) and run "run sims for paper.ipynb"
• Install R dependencies (eg, data.table) and run "create graphs for paper.R". Working directory must be here.
• Install shell dependency (Inkscape for Mac — or, if other platform, edit script) and run "convert_pngs_for_paper.sh"