Zhu, Hongzhe2026-08-242026-08-24https://hdl.handle.net/1885/733814557the author deposited 24.08.2026Computational social choice systems can support collective decisions, but their practical value depends on whether people can understand, influence, and accept the decision process. Group board-game allocation makes this problem concrete because participants need to coordinate preferences, player counts, teaching knowledge, familiarity, and possible multi-table assignments, while existing tools provide limited support for shared control and explanation. In this setting of collective decision-making, this thesis implements a human-centred framework for structuring information flow between participants and allocation algorithms. The framework is realised through PickNPlay, a participant-directed web system for collaborative game entry, tiered preference input, Z3-backed allocation, progress visibility, revision, and explanation, and extended through Boardot, an exploratory shared-station facilitation layer. Exploratory HCI sessions using observation, SUS questionnaires, interviews, follow-up material, and thematic analysis found that PickNPlay was rated as more usable over the formative Boardroom BGVS baseline, while Boardot showed comparable descriptive usability and made allocation more visible as a shared room-scale event. The findings suggest that solver-backed allocation becomes more acceptable when participants can see, shape, and revise the decision process. The thesis contributes a human-centred account of group board-game allocation as shared interaction infrastructure, showing how constrained collective decisions can be made more socially usable through transparency, recoverable coordination, and bounded facilitation.enGroup decision supportcomputational social choiceconstraint-based allocation,algorithmic transparencyboard gamescollaborative decision-makingHCIHuman centred interaction2026