# Alternates, Assemble! Selecting Optimal Alternates for Citizens' Assemblies

arXiv: https://arxiv.org/abs/2506.15716 · HTML: https://mlsanity.com/paper/2506.15716/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 24 references that are in the map.
- First posted on arXiv 2 Jun 2025.

## Builds on (most cited first, 3 of 3)

- [Neutralizing Self-Selection Bias in Sampling for Sortition](https://mlsanity.com/paper/2006.10498/) (2020)
- [Boosting Sortition via Proportional Representation](https://mlsanity.com/paper/2406.00913/) (2024)
- [Fair, Manipulation-Robust, and Transparent Sortition](https://mlsanity.com/paper/2406.15009/) (2024)

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

- [SNAP: A Self-Consistent Agreement Principle with Application to Robust Computation](https://mlsanity.com/paper/2602.02013/) (2026)

## Abstract (arXiv)

Citizens' assemblies are an increasingly influential form of deliberative democracy, where randomly selected people discuss policy questions. The legitimacy of these assemblies hinges on their representation of the broader population, but participant dropout often leads to an unbalanced composition. In practice, dropouts are replaced by preselected alternates, but existing methods do not address how to choose these alternates. To address this gap, we introduce an optimization framework for alternate selection. Our algorithmic approach, which leverages learning-theoretic machinery, estimates dropout probabilities using historical data and selects alternates to minimize expected misrepresentation. Our theoretical bounds provide guarantees on sample complexity (with implications for computational efficiency) and on loss due to dropout probability mis-estimation. Empirical evaluation using real-world data demonstrates that, compared to the status quo, our method significantly improves representation while requiring fewer alternates.