Maximally Random Sortition

2026-04-03

AI Summary

This study proposes a maximum entropy–based sampling method to enhance the representativeness and fairness of citizens’ assemblies. By maximizing the distributional entropy of assembly membership under individual inclusion probability constraints, the approach introduces, for the first time, an information-theoretic optimization framework into the selection process. The resulting randomized mechanism is designed to be both manipulation-resistant and transparent. Through constrained probabilistic modeling, theoretical analysis, and empirical evaluation on real-world data, the method demonstrably improves intersectional diversity and enhances coverage across potentially unknown dimensions of representation. Practical implementation tools have been integrated into an online platform tailored for practitioners.

Abstract

Citizens' assemblies are a form of democratic innovation in which a randomly selected panel of constituents deliberates on questions of public interest. We study a novel goal for the selection of panel members: maximizing the entropy of the distribution over possible panels. We design algorithms that sample from maximum-entropy distributions, potentially subject to constraints on the individual selection probabilities. We investigate the properties of these algorithms theoretically, including in terms of their resistance to manipulation and transparency. We benchmark our algorithms on a large set of real assembly lotteries in terms of their intersectional diversity and the probability of satisfying unseen representation constraints, and we obtain favorable results on both measures. We deploy one of our algorithms on a website for citizens' assembly practitioners.

Problem

• sortition
• maximum entropy
• citizens' assemblies
• random selection
• diversity

Innovation

• maximum-entropy sampling
• citizens' assemblies
• sortition
• intersectional diversity
• algorithmic fairness