Bridging Voting and Deliberation with Algorithms: Field Insights from vTaiwan and Kultur Komitee

Democratic processes increasingly integrate large-scale voting with face-to-face deliberation to reconcile individual preferences with collective decision-making. This work introduces algorithmic methods to bridge online voting with face-to-face deliberation, tested in two real-world scenarios: Kultur Komitee 2024 (KK24) and vTaiwan. We present three key contributions: (1) Preference-based Clustering for Deliberation (PCD), enabling both focused and broad discussions by computing balanced homogeneous and heterogeneous groups; (2) Human-in-the-loop MES, enhancing the Method of Equal Shares algorithm with real-time feedback, giving participants control over algorithmic decision-making; and (3) the ReadTheRoom method, using opinion mapping to identify agreement and divergence while tracking opinion shifts during deliberation. These actionable frameworks extend in-person deliberation with scalable digital methods that address the complexities of modern participatory decision-making.