🤖 AI Summary
This study addresses the challenge of integrating heterogeneous reputation signals from multiple sources in complex socio-technical systems. To this end, it extends the Liquid Rank reputation system by introducing a multi-source reputation fusion mechanism that supports explicit weighting and modular inputs. The proposed approach enables, for the first time, fine-grained control and dynamic integration of both internal and external reputation sources, facilitating contribution assessment across diverse contexts and subsystems. The resulting configurable and extensible aggregation architecture substantially enhances Liquid Rank’s adaptability and expressiveness in human-AI collaborative governance scenarios, offering a general-purpose foundational framework for reputation-driven governance.
📝 Abstract
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.