Joshua C. Yang

Integrating voting and deliberation in democratic processes is increasingly recognized as vital, prompting calls for hybrid democratic innovations (HDIs). Notably, participatory budgeting and citizens’ assemblies—key democratic innovations over the past three decades—can significantly enhance community engagement and decision-making when combined. This emerging combination presents substantial opportunities for algorithmic decision-making, where citizens are guided by their own votes toward more constructive and meaningful collective outcomes in deliberations. This community-based participatory research examines the Kultur Komitee Winterthur 2024, which integrated these methods by employing a hybrid model of the Method of Equal Shares (MES) voting and group deliberation, clustered using PCA reduction, in its decision-making process. The committee, consisting of 30 randomly selected citizens, utilised a budget of CHF 400,000 to support cultural projects, with voting input from online approval votes and real-time MES calculation interactive display informing and guiding in-person deliberations. We found distinct outcomes between online MES voting and deliberative selections, with the committee deciding that voting should dictate only half the budget, with grouped deliberation deciding the rest of the budget. This hybrid model highlights the potential of combining voting and deliberation for informed, inclusive, and fair funding decisions in participatory budgeting.

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.

This paper investigates the voting behaviors of Large Language Models (LLMs), specifically GPT-4 and LLaMA-2, their biases, and how they align with human voting patterns. Our methodology involved using a dataset from a human voting experiment to establish a baseline for human preferences and a corresponding experiment with LLM agents. We observed that the methods used for voting input and the presentation of choices influence LLM voting behavior. We discovered that varying the persona can reduce some of these biases and enhance alignment with human choices. While the Chain-of-Thought approach did not improve prediction accuracy, it has potential for AI explainability in the voting process. We also identified a trade-off between preference diversity and alignment accuracy in LLMs, influenced by different temperature settings. Our findings indicate that LLMs may lead to less diverse collective outcomes and biased assumptions when used in voting scenarios, emphasizing the importance of cautious integration of LLMs into democratic processes.