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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.01972 |
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| _version_ | 1866914369449754624 |
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| author | Harris, Megan S. Naderi, Mohammad Mahdi Ghorbanichemazkati, Ehsanoddin Jangjoo, Sina Lapan, Emily Hosseini, Seyed Amirreza Schipfer, Fabian Craig, Stephen Moallemi, Enayat Khayal, Inas Arpan, Laura M. Tang, Tian Little, John C. Farid, Amro M. |
| author_facet | Harris, Megan S. Naderi, Mohammad Mahdi Ghorbanichemazkati, Ehsanoddin Jangjoo, Sina Lapan, Emily Hosseini, Seyed Amirreza Schipfer, Fabian Craig, Stephen Moallemi, Enayat Khayal, Inas Arpan, Laura M. Tang, Tian Little, John C. Farid, Amro M. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_01972 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A System-of-Systems Convergence Paradigm for Societal Challenges of the Anthropocene Harris, Megan S. Naderi, Mohammad Mahdi Ghorbanichemazkati, Ehsanoddin Jangjoo, Sina Lapan, Emily Hosseini, Seyed Amirreza Schipfer, Fabian Craig, Stephen Moallemi, Enayat Khayal, Inas Arpan, Laura M. Tang, Tian Little, John C. Farid, Amro M. Systems and Control 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. |
| title | A System-of-Systems Convergence Paradigm for Societal Challenges of the Anthropocene |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2603.01972 |