
arXiv: 2310.03501
handle: 20.500.11850/675837
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.
FOS: Computer and information sciences, General Economics (econ.GN), 330, J.4, Computer Science - Human-Computer Interaction, legitimacy, K.4.3, Trust, 91B14, explainable AI, Human-Computer Interaction (cs.HC), Méthodes informatiques spéciales, FOS: Economics and business, Digital democracy, Computer Science - Computers and Society, H.5.3, Computer Science - Computer Science and Game Theory, Computers and Society (cs.CY), Computer Science - Multiagent Systems, Economics - General Economics, Collective decision-making, I.2.11, H.5.3; J.4; K.4.3; I.2.11; F.2.0, 006, trust, collective decision-making, digital democracy, Explainable AI, Participatory budgeting, Participatory budgeting; digital democracy; collective decision-making; explainable AI; trust; legitimacy, F.2.0, Computer Science and Game Theory (cs.GT), Multiagent Systems (cs.MA)
FOS: Computer and information sciences, General Economics (econ.GN), 330, J.4, Computer Science - Human-Computer Interaction, legitimacy, K.4.3, Trust, 91B14, explainable AI, Human-Computer Interaction (cs.HC), Méthodes informatiques spéciales, FOS: Economics and business, Digital democracy, Computer Science - Computers and Society, H.5.3, Computer Science - Computer Science and Game Theory, Computers and Society (cs.CY), Computer Science - Multiagent Systems, Economics - General Economics, Collective decision-making, I.2.11, H.5.3; J.4; K.4.3; I.2.11; F.2.0, 006, trust, collective decision-making, digital democracy, Explainable AI, Participatory budgeting, Participatory budgeting; digital democracy; collective decision-making; explainable AI; trust; legitimacy, F.2.0, Computer Science and Game Theory (cs.GT), Multiagent Systems (cs.MA)
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 11 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
