Online Selection of Diverse Committees

TL;DR

This paper investigates online algorithms for constructing diverse committees that accurately represent subpopulations, balancing contact costs and representativeness, through theoretical analysis and experimental validation.

Contribution

It introduces and compares three methods—greedy, probabilistic, and reinforcement learning—for online committee selection ensuring proportional representation.

Findings

The greedy algorithm effectively maintains proportionality.

The probabilistic method performs well with known feature distributions.

Reinforcement learning adapts to unknown distributions online.

Abstract

Citizens' assemblies need to represent subpopulations according to their proportions in the general population. These large committees are often constructed in an online fashion by contacting people, asking for the demographic features of the volunteers, and deciding to include them or not. This raises a trade-off between the number of people contacted (and the incurring cost) and the representativeness of the committee. We study three methods, theoretically and experimentally: a greedy algorithm that includes volunteers as long as proportionality is not violated; a non-adaptive method that includes a volunteer with a probability depending only on their features, assuming that the joint feature distribution in the volunteer pool is known; and a reinforcement learning based approach when this distribution is not known a priori but learnt online.