Snowveil: A Framework for Decentralised Preference Discovery

Aggregating subjective preferences of a large group is a fundamental challenge in computational social choice, traditionally reliant on central authorities. To address the limitations of this model, this paper introduces Decentralised Preference Discovery (DPD), the problem of determining the collective will of an electorate under constraints of censorship resistance, partial information, and asynchronous communication. We propose Snowveil, a novel framework for this task. Snowveil uses an iterative, gossip-based protocol where voters repeatedly sample the preferences of a small, random subset of the electorate to progressively converge on a collective outcome. We demonstrate the framework's modularity by designing the Constrained Hybrid Borda (CHB), a novel aggregation rule engineered to balance broad consensus with strong plurality support, and provide a rigorous axiomatic analysis of its properties. By applying a potential function and submartingale theory, we develop a multi-level analytical method to show that the system almost surely converges to a stable, single-winner in finite time, a process that can then be iterated to construct a set of winning candidates for multi-winner scenarios. This technique is largely agnostic to the specific aggregation rule, requiring only that it satisfies core social choice axioms like Positive Responsiveness, thus offering a formal toolkit for a wider class of DPD protocols. Furthermore, we present a comprehensive empirical analysis through extensive simulation, validating Snowveil's $O(n)$ scalability. Overall, this work advances the understanding of how a stable consensus can emerge from subjective, complex, and diverse preferences in decentralised systems for large electorates.