Diverse Representation via Computational Participatory Elections -- Lessons from a Case Study

TL;DR

This paper introduces the Representation Pact, a novel computational participatory electoral process designed to enhance diverse representation in elected bodies, demonstrated through a Swiss case study and offering practical lessons for societal elections.

Contribution

The paper presents a new electoral method combining voter-defined criteria with a two-round voting process to improve diversity and fairness in representation.

Findings

The method increased diversity in the elected committee.

It demonstrated improved fairness and descriptive representation.

Practical lessons for implementing computational electoral systems.

Abstract

Elections are the central institution of democratic processes, and often the elected body -- in either public or private governance -- is a committee of individuals. To ensure the legitimacy of elected bodies, the electoral processes should guarantee that diverse groups are represented, in particular members of groups that are marginalized due to gender, ethnicity, or other socially salient attributes. To address this challenge of representation, we have designed a novel participatory electoral process coined the Representation Pact, implemented with the support of a computational system. That process explicitly enables voters to flexibly decide on representation criteria in a first round, and then lets them vote for candidates in a second round. After the two rounds, a counting method is applied, which selects the committee of candidates that maximizes the number of votes received in the second round, conditioned on satisfying the criteria provided in the first round. With the help of a detailed use case that applied this process in a primary election of 96 representatives in Switzerland, we explain how this method contributes to fairness in political elections by achieving a better "descriptive representation". Further, based on this use case, we identify lessons learnt that are applicable to participatory computational systems used in societal or political contexts. Good practices are identified and presented.