Alternates, Assemble! Selecting Optimal Alternates for Citizens' Assemblies

TL;DR

This paper introduces an optimization framework for selecting alternates in citizens' assemblies to improve representation, using learning algorithms to estimate dropout probabilities and minimize misrepresentation.

Contribution

It presents a novel algorithmic approach that leverages learning theory to optimally select alternates, addressing a key gap in the management of participant dropout.

Findings

Significantly improves representation over existing methods

Requires fewer alternates to achieve better representation

Provides theoretical guarantees on sample complexity and loss bounds

Abstract

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.