Code for the Paper "The Impossibility of Strategyproof Rank Aggregation"

This directory contains the code for generating the SAT encoding described in the paper "The Impossibility of Strategyproof Rank Aggregation" by Manuel Eberl and Patrick Lederer (Section 4.1). Specifically, this directory contains a single file called KemenySAT.py. Upon execution, this file computes a GCNF that is satisfiable if and only if there is a strategyproof, anonymous, and unanimous SWF for m=5 alternatives and n=2 voters. This file is intended to be handed to MUSer2 (see paper), a MUS extractor. Execution After installing all dependencies (see below), the code can be executed like any python script. python3 -m venv .venvsource .venv/bin/activatepython3 KemenySAT.py Options We note that our code can be customized which, however, requires modifying the code directly. In particular, the following parameters are intended to be modified: The numbers of alternatives and voters (m, n), which are defined at the beginning of the file. In the main method (at the very end of the file), there are three more options: The parameter Min_True determines whether we encode full strategyproofness (Min_True=False) or encode a sparser variant of strategyproofness (Min_True=True) inspired by Proposition 2 in the paper "Strategy-proof preference aggregation and the anonymity-neutrality tradeoff" by S. Athanasoglou, S. Bonkoungou, and L. Ehlers (GEB 2025). The parameter Solve determines whether the satisfiability of the constructed formula directly by a python SAT solver (Solve=True) or not (Solve=False). The parameter CNFformat determines whether the SAT formula should be written in a CNF file (CNFformat=True), which is comnpatible with most external SAT solvers, or a GCNF file (CNFformat=False), which is optimized for MUSer2. The default values of these parameter is m=5, n=2, Min_True=True, Solve=True, and CNFformat=False. Dependencies Python 3.x numpy python-sat Reference to the Paper Manuel Eberl and Patrick Lederer. The Impossibility of Strategyproof Rank Aggregation. In the Proceedings of the 25th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2026. Forthcoming.