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A New Heuristic Algorithm for Balanced Deliberation Groups

Jake Barrett, Philipp C Verpoort, Kobi Gal

Abstract

We here present an improved version of the Sortition Foundation's GROUPSELECT software package, which aims to repeatedly allocate participants of a deliberative process to discussion groups in a way that balances demographics in each group and maximises distinct meetings over time. Our result, DREAM, significantly outperforms the prior algorithmic approach LEGACY. We also add functionalities to the GROUPSELECT software to help the end user. The GROUPOPT algorithm utilises random shuffles and Pareto swaps to find a locally optimal solution that maximises demographic balance and minimises the number of pairwise previous meetings, with the relative importance of these two metrics defined by the user.

A New Heuristic Algorithm for Balanced Deliberation Groups

Abstract

We here present an improved version of the Sortition Foundation's GROUPSELECT software package, which aims to repeatedly allocate participants of a deliberative process to discussion groups in a way that balances demographics in each group and maximises distinct meetings over time. Our result, DREAM, significantly outperforms the prior algorithmic approach LEGACY. We also add functionalities to the GROUPSELECT software to help the end user. The GROUPOPT algorithm utilises random shuffles and Pareto swaps to find a locally optimal solution that maximises demographic balance and minimises the number of pairwise previous meetings, with the relative importance of these two metrics defined by the user.

Paper Structure

This paper contains 10 sections, 5 equations, 3 figures, 1 table, 2 algorithms.

Table of Contents

  1. Introduction
  2. Notation
  3. Methodology and Heuristic Algorithm
  4. Algorithmic Implementation
  5. Experiment Set-up
  6. Evaluating the Algorithm
  7. Results
  8. Experiment Outcomes
  9. Comparisons with Optimal
  10. Future Developments

Figures (3)

  • Figure 1: Unique meetings for the $\mid\textbf{I}\mid=100$ data set, over $k=10$ rounds. The downward-sloping line labelled "0" is a count of how many unique pairs haven't met in round $k$, while all other lines labelled "$M$" are cumulative counts of how many pairs have met at least $M$ times in round $k$
  • Figure 2: Histogram of experiments in which Dream attains better (positive values), the same (0 values) or worse (negative values) diversity scores than Legacy
  • Figure 3: $excess$ values for Dream experiments: the proportion of pairs that don't meet over the panel but theoretically could