Maximally Random Sortition

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.

Figures (18)

• Figure 1: Strip plots for the selection probabilities, each point represents a pool member and its position represents the selection probability. Vertical lines mark the equalized selection probabilities $(\frac{k}{n}, \dots, \frac{k}{n})$.
• Figure 2: Generalization probabilities. Points are colored according to the probability that a uniform set of $k$ pool members satisfies the held-out feature. Points with probabilities/empirical means less than $5 \cdot 10^{-6}$ are moved into side boxes, see footnote.
• Figure 3: Planned interface of Panelot for choosing the selection algorithm (left) and visualization of the state of MaxEntropy (right).
• Figure 4: Generalization probabilities colored by organization.
• Figure :

Theorems & Definitions (17)

• Proposition 1
• Proposition 2
• Theorem 3.1
• Proposition 3
• Theorem 4.1
• Proposition 4
• definition 1
• Theorem 5.1
• Theorem 5.2: Resistance to Manipulation, Informal statement
• Proposition 5