Alternates, Assemble! Selecting Optimal Alternates for Citizens' Assemblies

2025-06-02

ACM Conference on Economics and Computation

AI Summary

Citizen assemblies rely on random sampling to ensure demographic representativeness, yet mid-term attrition introduces composition bias. Existing substitute mechanisms lack theoretical foundations and are typically assigned heuristically. This paper formalizes substitute selection as a theoretically grounded optimization problem for the first time. We propose a probabilistic prediction framework leveraging historical attrition data, integrating statistical learning theory, probabilistic modeling, and combinatorial optimization to dynamically select a substitute set that minimizes expected representation deviation. Theoretically, we derive an upper bound on sample complexity and establish error robustness guarantees. Empirically, our approach achieves significantly improved overall representativeness with fewer substitutes compared to current practice, demonstrating both efficacy and efficiency.

Technology Category

• Knowledge Representation and Reasoning: Preferences
• Search and Optimization: Sampling/Simulation-based Search
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

An increasingly influential form of deliberative democracy centers on citizens'assemblies, where randomly selected people discuss policy questions. The legitimacy of these panels hinges on their representation of the broader population, but panelists often drop out, leading to an unbalanced composition. Although participant attrition is mitigated in practice by alternates, their selection is not taken into account by existing methods. 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. We establish theoretical guarantees for our approach, including worst-case bounds on sample complexity (with implications for computational efficiency) and on loss when panelists'probabilities of dropping out are mis-estimated. Empirical evaluation using real-world data demonstrates that, compared to the status quo, our method significantly improves representation while requiring fewer alternates.

Problem

• Optimizing alternate selection for citizens' assemblies
• Minimizing misrepresentation due to participant dropout
• Improving representation with fewer alternates using data

Innovation

• Optimization framework for alternate selection
• Algorithm estimates dropout probabilities historically
• Improves representation with fewer alternates