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This paper extends liquid rank reputation systems by introducing a framework that aggregates multiple heterogeneous reputation sources into a unified score, allowing for the integration of both external and internal reputational signals. The model incorporates explicit weighting and blending mechanisms, enabling fine-grained control over the influence of individual sources based on context and participation. The findings demonstrate that this approach enhances adaptability in governance and coordination scenarios involving diverse agents, thereby improving the effectiveness of reputation-based systems in complex socio-technical environments.
A unified reputation score that seamlessly integrates multiple sources could revolutionize governance in complex socio-technical systems.
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.