Maximally Random Sortition

TL;DR

This paper introduces algorithms for selecting citizens' assembly panels that maximize diversity and representation by optimizing the entropy of the selection distribution, with theoretical analysis and practical benchmarking.

Contribution

It presents novel algorithms for maximum-entropy panel selection, analyzing their properties and demonstrating their effectiveness in real-world scenarios.

Findings

Algorithms achieve high intersectional diversity.

Algorithms effectively satisfy unseen representation constraints.

Deployment on a public website demonstrates practical usability.

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.