Temporal Panel Selection in Ongoing Citizens' Assemblies

TL;DR

This paper introduces a formal framework and algorithms for selecting rotating panels in ongoing citizens' assemblies, ensuring proportional representation and individual fairness over time in a dynamic, metric space population.

Contribution

It formalizes the concept of temporal panel selection, extending proportional representation to a sequential setting with algorithms guaranteeing fairness and representation.

Findings

Algorithms achieve proportional representation within panels and across sequences.

The framework ensures individual fairness over multiple rounds.

Extension of existing models to a temporal, metric space setting.

Abstract

Permanent citizens' assemblies are ongoing deliberative bodies composed of randomly selected citizens, organized into panels that rotate over time. Unlike one-off panels, which represent the population in a single snapshot, permanent assemblies enable shifting participation across multiple rounds. This structure offers a powerful framework for ensuring that different groups of individuals are represented over time across successive panels. In particular, it allows smaller groups of individuals that may not warrant representation in every individual panel to be represented across a sequence of them. We formalize this temporal sortition framework by requiring proportional representation both within each individual panel and across the sequence of panels. Building on the work of Ebadian and Micha (2025), we consider a setting in which the population lies in a metric space, and the goal is to achieve both proportional representation, ensuring that every group of citizens receives adequate representation, and individual fairness, ensuring that each individual has an equal probability of being selected. We extend the notion of representation to a temporal setting by requiring that every initial segment of the panel sequence, viewed as a cumulative whole, proportionally reflects the structure of the population. We present algorithms that provide varying guarantees of proportional representation, both within individual panels and across any sequence of panels, while also maintaining individual fairness over time.