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Designing Digital Voting Systems for Citizens: Achieving Fairness and Legitimacy in Participatory Budgeting

Joshua C. Yang, Carina I. Hausladen, Dominik Peters, Evangelos Pournaras, Regula Hänggli Fricker, Dirk Helbing

TL;DR

This work identified approaches to designing PB voting that minimise cognitive load and enhance the perceived fairness and legitimacy of the digital process from the citizens’ perspective and offers actionable insights for digital governance.

Abstract

Participatory Budgeting (PB) has evolved into a key democratic instrument for resource allocation in cities. Enabled by digital platforms, cities now have the opportunity to let citizens directly propose and vote on urban projects, using different voting input and aggregation rules. However, the choices cities make in terms of the rules of their PB have often not been informed by academic studies on voter behaviour and preferences. Therefore, this work presents the results of behavioural experiments where participants were asked to vote in a fictional PB setting. We identified approaches to designing PB voting that minimise cognitive load and enhance the perceived fairness and legitimacy of the digital process from the citizens' perspective. In our study, participants preferred voting input formats that are more expressive (like rankings and distributing points) over simpler formats (like approval voting). Participants also indicated a desire for the budget to be fairly distributed across city districts and project categories. Participants found the Method of Equal Shares voting rule to be fairer than the conventional Greedy voting rule. These findings offer actionable insights for digital governance, contributing to the development of fairer and more transparent digital systems and collective decision-making processes for citizens.

Designing Digital Voting Systems for Citizens: Achieving Fairness and Legitimacy in Participatory Budgeting

TL;DR

This work identified approaches to designing PB voting that minimise cognitive load and enhance the perceived fairness and legitimacy of the digital process from the citizens’ perspective and offers actionable insights for digital governance.

Abstract

Participatory Budgeting (PB) has evolved into a key democratic instrument for resource allocation in cities. Enabled by digital platforms, cities now have the opportunity to let citizens directly propose and vote on urban projects, using different voting input and aggregation rules. However, the choices cities make in terms of the rules of their PB have often not been informed by academic studies on voter behaviour and preferences. Therefore, this work presents the results of behavioural experiments where participants were asked to vote in a fictional PB setting. We identified approaches to designing PB voting that minimise cognitive load and enhance the perceived fairness and legitimacy of the digital process from the citizens' perspective. In our study, participants preferred voting input formats that are more expressive (like rankings and distributing points) over simpler formats (like approval voting). Participants also indicated a desire for the budget to be fairly distributed across city districts and project categories. Participants found the Method of Equal Shares voting rule to be fairer than the conventional Greedy voting rule. These findings offer actionable insights for digital governance, contributing to the development of fairer and more transparent digital systems and collective decision-making processes for citizens.
Paper Structure (32 sections, 2 equations, 15 figures, 15 tables)

This paper contains 32 sections, 2 equations, 15 figures, 15 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Evolution and Challenges of Digital Participatory Budgeting
  4. Trust, Fairness, and Legitimacy in the Digital Age
  5. Computational Social Choice Literature on PB
  6. Prior Experimental Work
  7. Formal Model
  8. Greedy Rule
  9. Method of Equal Shares (MES)
  10. Experimental Design
  11. Research Question 1: What are voters' preferred voting input formats, and how does the choice of voting input formats impact the collective outcome?
  12. Research Question 2: How do voter perceptions of fairness and trustworthiness vary across aggregation methods such as Greedy and MES, and what effect do different explanations have on these perceptions?
  13. Results: Voting Input Formats
  14. Individual Voting Patterns
  15. Perception of Voting Input Formats
  16. ...and 17 more sections

Figures (15)

  • Figure 1: Overview of voter perceptions of different voting formats. The top row shows how voters ranked the formats (Recommendation), quantified via the rank assigned to each format (Borda's Method) with higher values indicating greater recommendation. The middle and bottom rows display the Expressiveness and Easiness, respectively, derived from 5-point Likert scale responses. Box plots indicate the inter-quartile ranges, with medians highlighted in pink and means represented by orange diamonds.
  • Figure 2: Box plots illustrating the percent of votes that participants assigned to their self-identified preferred district and project category. The medians are highlighted in red.
  • Figure 3: Voting distribution across projects based on six distinct input formats (SN, S5, D5, D10, S5R, S5D10). The projects are ordered by their mean popularity across the 6 inputs.
  • Figure 4: Heat map of Jensen-Shannon divergence values comparing the distribution of votes between different voting input formats. The higher the value, the larger the difference between the distributions.
  • Figure 5: Visualisation of funded projects based on varying voting input formats, aggregation methods, and total budgets. Each row displays which of the 24 projects were selected by a specific combination of input format and aggregation method. The different shades of green or blue represent the projects that would be funded if the total budget were 10,000, 20,000, 60,000, or 90,000 CHF, from dark to light respectively. Projects with the lightest shade were not funded in all 4 budget scenarios. Projects with a cost of 5,000 CHF are on the left, and those with 10,000 CHF on the right. The heatmap display follows Fairstein2023ParticipatoryWorld.
  • ...and 10 more figures