Alternates, Assemble! Selecting Optimal Alternates for Citizens’ Assemblies
Name
3736252.3742614.pdf
Size
1.26 MB
Format
Adobe PDF
Checksum (MD5)
2b9beb7c97606130d89aa3350a8f7780
Author(s)
Assos, Angelos
Baharav, Carmel
Flanigan, Bailey
Procaccia, Ariel
Date Issued
July 2, 2025
Publisher
ACM|The 26th ACM Conference on Economics and Computation
Citation
Angelos Assos, Carmel Baharav, Bailey Flanigan, and Ariel Procaccia. 2025. Alternates, Assemble! Selecting Optimal Alternates for Citizens’ Assemblies. Proceedings of the 26th ACM Conference on Economics and Computation. Association for Computing Machinery, New York, NY, USA, 719–738.
Version
Final published version
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.
Description
EC ’25, July 7–10, 2025, Stanford, CA, USA
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3736252.3742614
