# Learning Real-Life Approval Elections

arXiv: https://arxiv.org/abs/2601.18651 · HTML: https://mlsanity.com/paper/2601.18651/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jan 2026.

## Built on by (most cited first, 1 of 1)

- [Agreement, Diversity, and Polarization Indices for Approval Elections](https://mlsanity.com/paper/2605.14983/) (2026)

## Abstract (arXiv)

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.