[論文レビュー] A System-of-Systems Convergence Paradigm for Societal Challenges of the Anthropocene

Megan S. Harris, Mohammad Mahdi Naderi+12 more|arXiv (Cornell University)|Mar 2, 2026

Complex Systems and Decision Making被引用数 0

ひとこと要約

要約: The paper proposes a System-of-Systems convergence paradigm built on a meta-cognition map to integrate real-world data, systems thinking, visual modeling, mathematics, and computing, demonstrated with the Chesapeake Bay Watershed case using SysML and HFGT.

ABSTRACT

Modern societal challenges, such as climate change, urbanization, and water resource management, demand integrated, multi-discipline, multi-problem approaches to frame and address their complexity. Unfortunately, current methodologies often operate within disciplinary silos, leading to fragmented insights and missed opportunities for convergence. A critical barrier to cross-disciplinary integration lies in the disparate ontologies that shape how different fields conceptualize and communicate knowledge. To address these limitations, this paper proposes a system-of-systems (SoS) convergence paradigm grounded in a meta-cognition map, a framework that integrates five complementary domains: real-world observations, systems thinking, visual modeling, mathematics, and computing. The paradigm is based on the Systems Modeling Language (SysML), offering a standardized, domain-neutral approach for representing and analyzing complex systems. The proposed methodology is demonstrated through a case study of the Chesapeake Bay Watershed, a socio-environmental system requiring coordination across land use, hydrology, economic and policy domains. By modeling this system with SysML, the study illustrates practical strategies for navigating interdisciplinary challenges and highlights the potential of agile SoS modeling to support large-scale, multi-dimensional decision-making.

研究の動機と目的

• 研究目的1: Identify why interdisciplinary convergence fails across socio-environmental systems due to fragmented ontologies and institutional silos.
• 研究目的2: Propose a generalizable SoS convergence paradigm grounded in a meta-cognition map spanning Real-World, Systems Thinking, Visual, Mathematics, and Computing domains.
• 研究目的3: Showcase practical integration of land use, hydrology, and economic models within a SysML/HFGT ontological framework.
• 研究目的4: Outline an educational and institutional infrastructure to train Anthropocene System Integrators for scalable, cross-domain decision support.

提案手法

• 方法1: Introduce a meta-cognition map organizing knowledge generation across five domains: Real-World, Systems Thinking, Visual, Mathematics, Computing.
• 方法2: Advocate a cohesive modeling toolbelt including Network Science, Data-Driven AI, MBSE, and Hetero-functional Graph Theory (HFGT).
• 方法3: Use Systems Modeling Language (SysML) as a standardized, domain-neutral representation for SoS modeling.
• 方法4: Demonstrate the paradigm through an integrated Chesapeake Bay Watershed case study coupling land use, hydrology, and economic models within a shared ontology.
• 方法5: Discuss design criteria for a convergent SoS paradigm: strong systems foundation, common ontology, extensibility, analytical and synthetic capability, and problem independence.
• 方法6: Describe how the paradigm supports translation across five cognitive domains and enables iterative, cross-domain knowledge generation.
• 方法7: Outline training and Team Science infrastructure to develop Anthropocene System Integrators.

Figure 1 : Graphical illustration of an interconnected geophysical, biophysical, sociocultural and sociotechnical system of Anthropocene systems.

実験結果

リサーチクエスチョン

• RQ1 研究質問1: How can fragmented ontologies and siloed approaches be reconciled to support cross-domain integration in socio-environmental systems?
• RQ2 研究質問2: Can a SysML-based SoS convergence paradigm, guided by a meta-cognition map, enable integrated modeling of coupled human and natural processes?
• RQ3 研究質問3: What combination of methodologies (Network Science, AI, MBSE, HFGT) best supports multi-domain convergence and scalability?
• RQ4 研究質問4: How can the Chesapeake Bay Watershed be modeled in an integrated, domain-neutral framework to inform coordinated decision making?
• RQ5 研究質問5: What educational and institutional strategies are needed to train Anthropocene System Integrators for large-scale, cross-sector challenges?

主な発見

• 主要発見1: Five systemic pitfalls hinder convergence: regional context dependence, fragmented ontologies, bottom-up miscoupling, co-simulation limitations, and neglect of socio-psychological dynamics.
• 主要発見2: A meta-cognition map provides a scaffold for knowledge generation across Real-World, Systems Thinking, Visual, Mathematics, and Computing domains.
• 主要発見3: A convergent SoS paradigm requires strong systems foundation, common ontology, extensibility, analytical and synthetic capabilities, and problem independence.
• 主要発見4: MBSE and HFGT, alongside Network Science and Data-Driven AI, offer high convergence potential and complement each other across domains.
• 主要発見5: The Chesapeake Bay Watershed case demonstrates how an integrated, SysML/HFGT-based framework can unify land-use, hydrology, and economics for more coherent cross-domain analysis.
• 主要発見6: An educational and organizational plan is proposed to train Anthropocene System Integrators to operate across disciplines and scales.

Figure 2 : Tunnel vision when addressing Anthropocene societal challenges