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.
The Challenge: Governance in Large-Scale Decentralised Systems. The rise of large-scale decentralised systems marks a shift from centrally governed platforms to distributed, trust-minimised architectures. Decentralised Autonomous Organisations (DAOs) manage substantial treasuries, peer-to-peer networks coordinate global computational resources, and online communities function as digital polities with millions of participants [26,29,43]. While these systems excel in scalability, censorship-resistance, and resilience, their social and political infrastructure lags behind. This creates a pressing governance challenge: How can a large, heterogeneous ArXiv Preprint, 20th December 2025, London, UK. 2025. collective, operating without a central authority, aggregate individual preferences into legitimate and effective decisions? Addressing this question requires reconciling two opposing forces. On one hand, decentralisation demands mechanisms that are scalable, asynchronous, and trust-minimised. On the other, computational social choice emphasises fairness, expressiveness, and robustness in preference aggregation [3,10]. Classical models of social choice almost universally assume a trusted central authority to collect ballots and compute the outcome -an assumption incompatible with decentralised architectures. This paper proposes a framework that combines the scalability of gossip-based communication with axiomatic guarantees inspired by social choice theory, aiming to bridge this foundational gap.
The Gap: From Objective Consensus to Subjective Discovery. While classical Byzantine Fault Tolerant (BFT) protocols establish the foundations for fault-tolerant consensus, their communication complexity limits scalability. Modern advances overcome this; for instance, leader-based protocols such as HotStuff achieve rapid finality with linear communication [44], while the Snow family, which underpins Avalanche [38], provides highly scalable probabilistic agreement through repeated sub-sampling. Despite their different architectures, these mechanisms are designed to reach consensus on an objective, verifiable state (e.g., transaction validity). Their goal is to produce a single, correct outcome -a fundamentally different task from the subjective aggregation required for social choice.
Collective governance poses a qualitatively different problem. The task is not to discover a pre-existing truth but to construct a collective preference from subjective, non-verifiable inputs. Instead of validating transactions, the system must determine a group’s ranking over candidates -a setting with no objective ground truth. The desired output is an expressive social outcome (e.g., a winner or a full ranking), ideally accompanied by fairness properties, such as responsiveness and monotonicity. We term this challenge Decentralised Preference Discovery (DPD): enabling a network of autonomous agents, each with private preferences, to converge on a collective outcome without a central coordinator. Existing consensus protocols, while powerful for state replication, are ill-suited for DPD as they do not address fairness, expressiveness, or strategic robustness in preference aggregation.
Our Approach: Snowveil -Bridging Consensus and Social Choice. To address the DPD problem, we introduce Snowveil, a framework that reimagines the scalable, gossip-based sampling engine of Snow protocols for the domain of social choice. The key insight is to generalise the protocol’s payload from objective facts to subjective preferences: instead of querying peers for a binary decision on a transaction, agents in Snowveil sample the complete preference rankings of their peers. Each agent treats this sample as a noisy local signal of the emergent global will and processes it through a purpose-built, axiomatically-justified aggregation rule to update its own local belief. Through this iterative process of local sampling and aggregation, the network is proven to converge efficiently under any positively responsive rule, yielding a single winning alternative. The full Snowveil protocol leverages this single-winner engine, iterating the process to construct a complete social ranking. By coupling the scalability of gossip-based consensus with the axiomatic guarantees of social choice, Snowveil is, to our knowledge, the first framework to formally bridge these two domains. For a structured comparison of convergence objectives, proof techniques, and state complexity between Snow-family protocols and Snowveil, see Table 1 in Appendix.
This paper’s primary contributions are as follows.
(𝑖) A framework for Decentralised Preference Discovery (DPD): A formal problem definition for preference aggregation in decentralised, asynchronous, and partially informed environments, together with Snowveil, a modular framework that upgrades binary/𝑘-ary consensus payloads to expressive social outcomes (single winner and ranking) from subjective preferences.
(𝑖𝑖) The Constrained Hybri
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