Toward a Methodology for the Scientific Characterization of Recurring Operational Observations in Autonomous Ecosystems

This paper introduces a theory-neutral methodology for the scientific characterization of recurring operational observations in autonomous ecosystems. While recurring operational phenomena are extensively investigated across robotics, artificial intelligence, systems engineering, distributed systems and related disciplines, the methodological transition from recurring empirical observation to systematic scientific characterization is rarely made explicit. The paper proposes a structured analytical framework that separates observation, qualification, scientific characterization and theoretical interpretation into distinct methodological stages. By introducing explicit qualification criteria and preserving the separation between characterization and explanation, the framework establishes a common observational foundation upon which multiple scientific disciplines can independently develop, compare and evaluate alternative explanatory theories. Rather than proposing a new explanatory theory or replacing existing disciplinary approaches, the framework complements established scientific perspectives by strengthening methodological transparency, reproducibility and interdisciplinary comparability. The paper demonstrates the framework through an illustrative characterization of recurring operational observations while emphasizing that the proposed observational classes are methodological examples rather than definitive scientific taxonomies. The principal contribution of this work is the formalization of scientific characterization as an explicit methodological stage between empirical observation and disciplinary interpretation. If validated through future empirical research, the proposed methodology may provide a common analytical foundation for the development of domain-independent metrics, assurance methodologies, certification approaches and future theories of autonomous ecosystems.