This is a Python package implementing various tools for researchers working in Computational Social Choice [1], and specifically voting theory. Its aim is to provide a framework for projects requiring access to SAT solvers to reason about axioms and voting rules [2].
The package also provides a library for the automated justification of voting outcomes [3,4,5], as well as the code of a web application that allows users to search for axiomatic justifications for a given outcome in a small-scale voting scenario.
For a more in-depth introduction to the code, consult the sample Python notebook Example.ipynb.
[1] Felix Brandt, Vincent Conitzer, Ulle Endriss, Jérôme Lang, and Ariel D. Procaccia, editors. Handbook of Computational Social Choice. Cambridge University Press, 2016.
[2] Christian Geist and Dominik Peters. Computer-Aided Methods for Social Choice Theory. In: Ulle Endriss, editor, Trends in Computational Social Choice. AI Access, 2017.
[3] Arthur Boixel and Ulle Endriss. Automated Justification of Collective Decisions via Constraint Solving. In Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems (AAMAS-2020), IFAAMAS, 2020.
[4] Arthur Boixel, Ulle Endriss, and Ronald de Haan. A Calculus for Computing Structured Justifications for Election Outcomes. In Proceedings of the 36th AAAI Conference on Artificial Intelligence (AAAI-2022), AAAI Press, 2022.
[5] Oliviero Nardi, Arthur Boixel, and Ulle Endriss. A Graph-Based Algorithm for the Automated Justification of Collective Decisions. In Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems (AAMAS-2022), IFAAMAS, 2022.