A Scientific Measurement Framework for Autonomous Ecosystems: A Methodology for Deriving Operational Metrics from Scientific Characterizations

This paper introduces the Scientific Measurement Framework, a theory-neutral methodology for systematically deriving operational metrics from previously established scientific characterizations within autonomous ecosystems. While existing engineering measurement methodologies predominantly evaluate implementation-specific objectives such as performance, efficiency, reliability and safety, they generally do not provide a domain-independent scientific methodology explaining how scientifically characterized operational phenomena become quantitatively measurable. This paper addresses this methodological gap by establishing scientific measurement as an explicit methodological stage between scientific characterization and empirical validation. The proposed framework introduces a structured derivation process in which theory-neutral scientific characterizations are successively refined into measurement dimensions, measurement attributes, operational metrics and reproducible metric instances. By preserving explicit methodological traceability throughout the complete derivation process, operational metrics remain scientifically connected to the operational phenomena they quantify while remaining independent of implementation technologies, disciplinary objectives and explanatory theories. Rather than proposing application-specific engineering metrics, the Scientific Measurement Framework establishes the methodological principles governing how operational metrics should be systematically developed. The framework therefore provides a reproducible foundation for comparative investigation, empirical validation and cumulative scientific knowledge development across heterogeneous autonomous ecosystems. This work represents the second methodological foundation within a broader research program on the scientific investigation of autonomous ecosystems. Whereas previous work established a framework for the theory-neutral scientific characterization of recurring operational phenomena, the present paper introduces the quantitative methodological layer through which these scientifically characterized phenomena become reproducible scientific evidence. The framework is intended to serve as a methodological foundation for future research on operational metrics, governability metrics, runtime assurance, certification methodologies and other quantitative scientific disciplines investigating increasingly complex autonomous ecosystems. Keywords Scientific Measurement Framework; Scientific Measurement; Scientific Characterization; Operational Metrics; Autonomous Ecosystems; Autonomous Systems; Measurement Methodology; Measurement Science; Systems Science; Empirical Validation; Theory-Neutral Methodology; Robotics; Artificial Intelligence; Comparative Measurement; Scientific Methodology