Snowveil: A Framework for Decentralised Preference Discovery 文章

arXiv:2512.18444v2 Announce Type: replace-cross Abstract: Aggregating subjective preferences in social choice traditionally assumes a trusted central authority. In contrast, this paper formalises Decentralised Preference Discovery (DPD): the reliable identification of a social choice parameter (e.g. the canonical outcome of an aggregation rule applied to the global preference profile) under conditions of partial information, asynchronous interaction, censorship resistance, and no central coordinator. To address DPD, we propose Snowveil, a gossip-based framework where agents repeatedly sample random peer rankings and update local beliefs to converge on the canonical outcome. Using a potential function, submartingale theory, and concentration bounds, we prove the system reaches this stable state with tunable high probability, in finite expected time. This single-winner process can then be iterated to construct a set of winning candidates for multi-winner scenarios.