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Alternates, Assemble! Selecting Optimal Alternates for Citizens' Assemblies

Develops an optimization framework for selecting alternate participants for citizens’ assemblies to minimize expected demographic imbalance when dropouts occur. The method uses historical data to estimate dropout likelihood and provides theoretical guarantees regarding sample requirements and estimation accuracy. Results show the approach significantly improves representation while requiring fewer alternates compared to current practices.

Research
Creators Angelos Assos, Carmel Baharav, Bailey Flanigan and Ariel Procaccia
Year 2025