# Guide to Numerical Experiments on Elections in Computational Social Choice

arXiv: https://arxiv.org/abs/2402.11765 · HTML: https://mlsanity.com/paper/2402.11765/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Feb 2024.

## Built on by (most cited first, 7 of 7)

- [Multiwinner Temporal Voting with Aversion to Change](https://mlsanity.com/paper/2408.11017/) (2024)
- [Drawing a Map of Elections](https://mlsanity.com/paper/2504.03809/) (2025)
- [DeepVoting: Learning and Fine-Tuning Voting Rules with Canonical Embeddings](https://mlsanity.com/paper/2408.13630/) (2024)
- [Matching Markets Meet LLMs: Algorithmic Reasoning with Ranked Preferences](https://mlsanity.com/paper/2506.04478/) (2025)
- [Outer Diversity of Structured Domains](https://mlsanity.com/paper/2602.15708/) (2026)
- [Large Language Models for Designing Participatory Budgeting Rules](https://mlsanity.com/paper/2602.09349/) (2026)
- [Efficient Algorithms for Electing Successive Committees](https://mlsanity.com/paper/2505.18287/) (2025)

## Abstract (arXiv)

We analyze how numerical experiments regarding elections were conducted within the computational social choice literature (focusing on papers published in the IJCAI, AAAI, and AAMAS conferences). We analyze the sizes of the studied elections and the methods used for generating preference data, thereby making previously hidden standards and practices explicit. In particular, we survey a number of statistical cultures for generating elections and their commonly used parameters.