Guide to Numerical Experiments on Elections in Computational Social Choice
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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.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Feb 2024.
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Citing papers per year: 2024: 2; 2025: 3; 2026: 2.
7 papers.
- 2024Multiwinner Temporal Voting with Aversion to Changecited by 10
- 2025Drawing a Map of Electionscited by 4
- 2024DeepVoting: Learning and Fine-Tuning Voting Rules with Canonical Embeddingscited by 2
- 2025Matching Markets Meet LLMs: Algorithmic Reasoning with Ranked Preferencescited by 2
- 2026Outer Diversity of Structured Domainscited by 2
- 2026Large Language Models for Designing Participatory Budgeting Rulescited by 1
- 2025Efficient Algorithms for Electing Successive Committeescited by 0
- 2026Outer Diversity of Structured Domains17 Feb 2026
- 2026Large Language Models for Designing Participatory Budgeting Rules10 Feb 2026
- 2025Matching Markets Meet LLMs: Algorithmic Reasoning with Ranked Preferences4 Jun 2025
- 2025Efficient Algorithms for Electing Successive Committees23 May 2025
- 2025Drawing a Map of Elections4 Apr 2025
- 2024DeepVoting: Learning and Fine-Tuning Voting Rules with Canonical Embeddings24 Aug 2024
- 2024Multiwinner Temporal Voting with Aversion to Change20 Aug 2024