Designing Digital Voting Systems for Citizens: Achieving Fairness and Legitimacy in Participatory Budgeting
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Abstract (arXiv)
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.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Oct 2023.
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Citing papers per year: 2024: 1; 2025: 7; 2026: 1.
9 papers.
- 2025Voting or Consensus? Decision-Making in Multi-Agent Debatecited by 55
- 2024Generative AI voting: fair collective choice is resilient to LLM biases and inconsistenciescited by 6
- 2025CrowdLLM: Building LLM-Based Digital Populations Augmented with Generative Modelscited by 5
- 2025Bridging Voting and Deliberation with Algorithms: Field Insights from vTaiwan and Kultur Komiteecited by 3
- 2025Upgrading Democracies with Fairer Voting Methodscited by 2
- 2025Fair Voting Methods as a Catalyst for Democratic Resilience: A Trilogy on Legitimacy, Impact and AI Safeguardingcited by 1
- 2026DAO-enabled decentralized physical AI: A new paradigm for human-machine collaborationcited by 1
- 2025Efficient Portfolio Selection through Preference Aggregation with Quicksort and the Bradley--Terry Modelcited by 0
- 2025Algorithmic Shortlisting in Participatory Budgetingcited by 0
- 2026DAO-enabled decentralized physical AI: A new paradigm for human-machine collaboration6 May 2026
- 2025Fair Voting Methods as a Catalyst for Democratic Resilience: A Trilogy on Legitimacy, Impact and AI Safeguarding19 Dec 2025
- 2025CrowdLLM: Building LLM-Based Digital Populations Augmented with Generative Models2 Dec 2025
- 2025Algorithmic Shortlisting in Participatory Budgeting7 Aug 2025
- 2025Upgrading Democracies with Fairer Voting Methods20 May 2025
- 2025Efficient Portfolio Selection through Preference Aggregation with Quicksort and the Bradley--Terry Model6 Apr 2025
- 2025Voting or Consensus? Decision-Making in Multi-Agent Debate26 Feb 2025
- 2025Bridging Voting and Deliberation with Algorithms: Field Insights from vTaiwan and Kultur Komitee7 Feb 2025
- 2024Generative AI voting: fair collective choice is resilient to LLM biases and inconsistencies31 May 2024