Alternates, Assemble! Selecting Optimal Alternates for Citizens' Assemblies
This addresses a specific problem in deliberative democracy for policymakers and organizers, but it is incremental as it builds on existing methods for alternate selection.
The paper tackles the problem of participant dropout in citizens' assemblies, which undermines representation, by introducing an optimization framework that selects alternates to minimize expected misrepresentation, resulting in significant improvements in representation while requiring fewer alternates.
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.