Learning Real-Life Approval Elections
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866908788942962688 |
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| author | Faliszewski, Piotr Janeczko, Łukasz Kaczmarczyk, Andrzej Kurdziel, Marcin Pierczyński, Grzegorz Szufa, Stanisław |
| author_facet | Faliszewski, Piotr Janeczko, Łukasz Kaczmarczyk, Andrzej Kurdziel, Marcin Pierczyński, Grzegorz Szufa, Stanisław |
| contents | We study the independent approval model (IAM) for approval elections, where each candidate has its own approval probability and is approved independently of the other ones. This model generalizes, e.g., the impartial culture, the Hamming noise model, and the resampling model. We propose algorithms for learning IAMs and their mixtures from data, using either maximum likelihood estimation or Bayesian learning. We then apply these algorithms to a large set of elections from the Pabulib database. In particular, we find that single-component models are rarely sufficient to capture the complexity of real-life data, whereas their mixtures perform well. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_18651 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Learning Real-Life Approval Elections Faliszewski, Piotr Janeczko, Łukasz Kaczmarczyk, Andrzej Kurdziel, Marcin Pierczyński, Grzegorz Szufa, Stanisław Computer Science and Game Theory 60B20 F.2.2; I.2.6 We study the independent approval model (IAM) for approval elections, where each candidate has its own approval probability and is approved independently of the other ones. This model generalizes, e.g., the impartial culture, the Hamming noise model, and the resampling model. We propose algorithms for learning IAMs and their mixtures from data, using either maximum likelihood estimation or Bayesian learning. We then apply these algorithms to a large set of elections from the Pabulib database. In particular, we find that single-component models are rarely sufficient to capture the complexity of real-life data, whereas their mixtures perform well. |
| title | Learning Real-Life Approval Elections |
| topic | Computer Science and Game Theory 60B20 F.2.2; I.2.6 |
| url | https://arxiv.org/abs/2601.18651 |