AI 中文总结
研究如何扩展液体排名声誉系统以实现多源异构声誉聚合，通过引入加权和融合机制，结合内外部声誉信号，为复杂社会技术环境下基于声誉的治理机制设计提供灵活基础。
AI 中文摘要
在本文中，我们提出了一种液体排名声誉系统的扩展，它能够将多个异构声誉源聚合和融合为一个统一的声誉分数。所提出的框架支持将外部声誉信号与内部生成的声誉相结合，使影响力能够反映跨多个上下文和子系统的参与和贡献。通过引入明确的加权和融合机制，该模型对各个声誉源的相对影响提供了细粒度控制，使其适用于涉及人类和机器代理的各种治理和协调场景。由此产生的方法扩展了现有的液体排名系统，并为在复杂的社会技术环境中设计基于声誉的治理机制提供了一个灵活的基础。
英文摘要
In this paper, we present an extension of liquid rank reputation systems that enables the aggregation and blending of multiple heterogeneous reputation sources into a unified reputation score. The proposed framework supports the incorporation of external reputational signals alongside internally generated reputation, allowing influence to reflect participation and contribution across multiple contexts and subsystems. By introducing explicit weighting and blending mechanisms, the model provides fine-grained control over the relative impact of individual reputation sources, making it adaptable to diverse governance and coordination scenarios involving both human and machine agents. The resulting approach extends existing liquid rank systems and offers a flexible foundation for designing reputation-based governance mechanisms in complex socio-technical environments.
