Temporal Panel Selection in Ongoing Citizens' Assemblies

2026-02-18

AI Summary

This study addresses temporal sampling mechanisms in permanent citizen assemblies, aiming to achieve fair representation across multiple sequential panels. It seeks to ensure that any consecutive sequence of panels collectively reflects the population’s demographic composition—including minority groups—in proportion, while simultaneously guaranteeing that each individual has an equal long-term probability of selection. To this end, the work extends the notion of proportional representation to a temporal cumulative setting, modeling population structure within a metric space and integrating temporal ordering with fairness constraints. The authors propose a novel panel selection algorithm that provides rigorous guarantees for both proportional representation over any single panel or arbitrary-length sequence of panels and long-term individual fairness.

Technology Category

Knowledge Representation and Reasoning: PreferencesMultiagent Systems: Mechanism DesignGame Theory and Economic Paradigms: Fair Division

Application Category

User Modeling, Personalization and Recommendation: Fairness-aware retrieval and rankingEconomics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systemsSecurity and Privacy: Data transparency and provenance

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.

Problem

• temporal sortition
• proportional representation
• individual fairness
• citizens' assemblies
• metric space

Innovation

• temporal sortition
• proportional representation
• individual fairness
• citizens' assemblies
• metric space