Snowveil: A Framework for Decentralised Preference Discovery

2025-12-20

AI Summary

This paper formalizes the “Decentralized Preference Discovery (DPD)” problem—aggregating large-scale group preferences without a central authority. To address censorship resistance, partial information availability, and asynchronous communication, we propose Snowveil: a framework leveraging iterative gossip protocols for local random sampling and a Constrained Hybrid Borda (CHB) aggregation rule to balance majority support and consensus breadth. We introduce a novel analytical technique combining potential functions and supermartingales, yielding the first rigorous proof of almost-sure convergence to a unique winner within finite steps under asynchronous, partial-information settings; the analysis extends to multi-winner scenarios. Simulations confirm O(n) linear scalability and adherence to core social choice axioms—including positive responsiveness. The framework exhibits strong generality with respect to aggregation rules.

Technology Category

• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Knowledge Representation and Reasoning: Preferences
• Multiagent Systems: Mechanism Design

Application Category

• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Security and Privacy: Data transparency and provenance

Abstract

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.

Problem

• Decentralized preference discovery for large groups
• Aggregating preferences with censorship resistance and asynchrony
• Achieving consensus from diverse preferences in decentralized systems

Innovation

• Gossip-based iterative protocol for decentralized preference aggregation
• Constrained Hybrid Borda rule balancing consensus and plurality support
• Multi-level analytical method ensuring finite-time convergence to stable outcomes