2026/06/29 by Bernd von Mallinckrodt · 1 voice
Computer Science · Environmental Science · Neuroscience · #Cognitive Science and Education Research #Ecosystem dynamics and resilience #Target Tracking and Data Fusion in Sensor Networks
paper · doi:10.5281/zenodo.21030010
openalex publication_date 2026/06/29 · openalex created_date 2026/06/30 · openalex updated_date 2026/07/01
Dieses Open-Access-Perspective-Paper entwickelt Adaptive Observation Science (AOS) als domänenunabhängigen konzeptionellen Forschungsrahmen durch die Integration von Measurement Architecture Dynamics (MAD) und BenchEWS. Ziel ist nicht die Einführung einer neuen mathematischen Theorie oder eines universellen Gesetzes, sondern die Synthese etablierter Konzepte aus Beobachtbarkeitstheorie, Kontrolltheorie, Active Sensing, Sensor Scheduling, Information Geometry und reproduzierbarer Early-Warning-Signal-Evaluation zu einem konsistenten Forschungsprogramm. Die Arbeit argumentiert, dass MAD erklärt, warum sich Beobachtbarkeit und Messkonfigurationen komplexer Systeme verändern, während BenchEWS quantitativ bewertet, wie zuverlässig Early-Warning-Signale unter definierten Beobachtungsbedingungen erkannt werden. Gemeinsam bilden beide Arbeiten einen geschlossenen konzeptionellen Rahmen für adaptive Beobachtung komplexer Systeme und formulieren eine Roadmap für zukünftige mathematische und experimentelle Forschung. Keywords: Adaptive Observation Science, Measurement Architecture Dynamics, BenchEWS, Adaptive Observation, Observability, Measurement Architecture, Early-Warning Signals, Complex Systems, Control Theory, Information Geometry, Active Sensing, Sensor Scheduling, Conceptual Framework, Perspective Paper, Scientific Synthesis, Resilience, Open Science. English – Description This open-access Perspective paper proposes Adaptive Observation Science (AOS) as a domain-independent conceptual research framework by integrating Measurement Architecture Dynamics (MAD) with BenchEWS. Rather than introducing a new mathematical theory or universal law, the paper synthesizes established concepts from observability theory, control theory, active sensing, sensor scheduling, information geometry, and reproducible early-warning signal evaluation into a coherent research programme. The manuscript argues that MAD explains why observability and measurement configurations evolve in complex systems, whereas BenchEWS quantitatively evaluates how reliably early-warning signals can be detected under explicitly defined observation conditions. Together, both frameworks establish a coherent conceptual foundation for adaptive observation in complex systems and outline a roadmap for future mathematical and empirical research. Keywords: Adaptive Observation Science, Measurement Architecture Dynamics, BenchEWS, Adaptive Observation, Observability, Measurement Architecture, Early-Warning Signals, Complex Systems, Control Theory, Information Geometry, Active Sensing, Sensor Scheduling, Conceptual Framework, Perspective Article, Scientific Synthesis, Resilience, Open Science.