Integrating System Dynamics with AI and Other Analytical Methods: Advancements and Applications for Decision Making with/within Complex Systems

Dear Colleagues,

As the complexity of our society’s problems increases, ranging from climate change and environmental degradation to social inequality, armed conflicts, and economic instability, the need for innovative and integrative approaches to understand and address these challenges has never been more critical. The complexity of modern socio-technical and environmental systems demands such approaches and solutions that can navigate and help make sense of their interdependencies [1,2]. In such a context, this Special Issue focuses on how various systemic approaches - with particular reference to system dynamics but also to other modeling and simulation techniques - can synergize with analytical methods [3,4,5,6,7] such as artificial intelligence (AI), machine learning, and generative AI, when it comes to decision making in the context of complex systems [1,2,8,9,10].

This issue explores the innovative intersection of systemic and analytical methods. In an era where complex challenges are becoming the norm, integrating these methodologies offers new avenues for understanding, modeling, and solving multifaceted problems.

For example, this SI seeks to discuss and promote the potential of combining the holistic, feedback-oriented perspective of system dynamics with the data-driven, predictive power of analytical techniques.

System dynamics offers a powerful framework for conceptualizing complex systems through the lens of mental models, feedback loops, stock and flow structures, and time delays [11,12,13]. It enables scenario simulation and explores policy impacts [11,14]. Meanwhile, AI and machine learning can sift through vast amounts of data to uncover patterns, predict outcomes, and optimize solutions [15,7]. By melding these approaches, researchers and practitioners can create more nuanced, adaptive, and effective models of complex systems.

We aim to promote studies that develop and apply integrated approaches leveraging the strengths of systemic thinking and analytical methodologies to understand, model, and intervene in complex systems.

Scope

We invite theoretical and empirical research papers that explore integrating systemic and analytical methods. This Special Issue seeks to highlight research that not only addresses the methodological aspects of combining these approaches but also demonstrates their practical application across various domains, such as healthcare, environmental management, urban planning, social systems, and business.

Topics of Interest

Submissions are encouraged from a broad range of topics, including, but not limited to:

• Theoretical frameworks for integrating system dynamics with AI and machine learning.
• Methodological innovations and best practices for integrating systemic and analytical methods.
• Theoretical papers discussing the implications of combining system dynamics with AI and machine learning for systems theory and practice.
• Empirical studies that employ combined methodologies to solve complex problems in various domains.
• Case studies on applying integrated approaches in policy analysis, strategic planning, and decision support.
• Methodological innovations and challenges in merging qualitative systemic methods with quantitative analytical techniques.
• Critical reflections on the implications of using AI and machine learning within system dynamics for understanding complex systems.
• Critical evaluations of the effectiveness and limitations of using system dynamics and analytical methods in tandem.
• Methodological innovations for integrating ecological data with systemic models for better environmental policy formulation and prediction.

References

1. Armenia, S.; Barile, S.; Iandolo, F.; Pompei, A.; Sicca, L.M. Organisational ambidexterity and knowledge management: A systems perspective towards Smart Model-based Governance. Res. Behav. Sci. 2023, 41, 114. https://doi.org/10.1002/sres.2985
2. Armenia, S.; Franco, E.F.; Mecella, M.; Onori, R. Smart Model-based Governance: from Big-Data to future Policy Making. In Model-based Governance for Smart Organizational Future: BSLab-SYDIC International Workshop, Rome, January, 2017, 23-24. Available online: https://www.researchgate.net/profile/Eduardo-Franco/publication/313479171_Smart_Model-based_Governance_from_Big-Data_to_future_Policy_Making/links/589c63e6a6fdcc75417867a7/Smart-Model-based-Governance-from-Big-Data-to-future-Policy-Making.pdf (accessed on 28 May 2024)
3. Chen, H.; Chiang, R.H.L.; Storey, V.C. Business intelligence and analytics: from big data to big impact. MIS Q. 2012, 36, 1165–1188.
4. Chen, P.; Zhang, C.-Y. Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Inf. Sci. 2014, 275, 314–347. https://doi.org/10.1016/j.ins.2014.01.015
5. Elragal, A.; Klischewski, R. Theory-driven or process-driven prediction? Epistemological challenges of big data analytics. J. Big Data, 2017, 4, 19. https://doi.org/10.1186/s40537-017-0079-2
6. LaValle, S.; Hopkins, M.S.; Lesser, E.; Shockley, R.; Kruschwitz, N. Analytics: The new path to value. MIT Sloan Manage. Rev. 2014, 52, 1–25.
7. Vassakis, K.; Petrakis, E.; Kopanakis, I. Big Data Analytics: Applications, Prospects and Challenges; Springer, Cham: Cham. Switzerland, 2018; 3–20. https://link.springer.com/chapter/10.1007/978-3-319-67925-9_1
8. Clarke, A.; Margetts, H. Governments and Citizens Getting to Know Each Other? Open, Closed, and Big Data in Public Management Reform. Policy Internet, 2014, 6, 393–417. https://doi.org/10.1002/1944-2866.POI377
9. Dunleavy, P. New Public Management Is Dead--Long Live Digital-Era Governance. Publ. Administration Res. and Theor., 2005, 16, 467–494. https://doi.org/10.1093/jopart/mui057
10. Hufty, M. Governance: Exploring Four Approaches and Their Relevance to Research. In Research for Sustainable Development: Foundations, Experiences, and Perspectives; Wiesmann, ; Hurni, H. Eds.; NCCR North-South, Centre for Development and Environment (CDE) and Instiute of Geography, University of Bern: Bern, Switzerland, 2011; pp. 165–183.
11. Forrester, J.W. Policies, decisions and information sources for modeling. Eur. J. Operational Res. 1992, 59, 42–63. https://doi.org/10.1016/0377-2217(92)90006-U
12. Simon, H.A. A Behavioral Model of Rational Choice. The Q. J. Econ. 1955, 69, 99–118.
13. Simon, H.A. Designing organizations for an information rich world. In Computers, communications, and the public interest; Greenberger Ed.; The Johns Hopkins Press: Baltimore, MD, USA, 1971; pp. 37–72.
14. Sterman, J.; Oliva, R.; Linderman, K.; Bendoly, E. System dynamics perspectives and modeling opportunities for research in operations management. J. Oper. Manage. 2015, 39–40, 1–5. https://doi.org/10.1016/j.jom.2015.07.001
15. Simsek, Z.; Vaara, E.; Paruchuri, S.; Nadkarni, S.; Shaw, J.D. New Ways of Seeing Big Data. Acad. Manage. J. 2019, 62, 971–978. https://doi.org/10.5465/amj.2019.4004

Dr. Stefano Armenia
Dr. Eduardo Ferreira Franco
Dr. Pietro Vito
Guest Editors

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• system dynamics
• systems thinking
• agent-based modeling
• modeling & simulation
• artificial intelligence
• machine learning
• predictive analytics
• data analytics
• complex systems
• decision making
• decision support systems
• strategic planning
• sustainability and resilience
• scenario analysis
• policy design & evaluation