Journal of Simulation

System Dynamics and Artificial Intelligence: Modelling with AI and Modelling the societal and organizational implications of AI

System Dynamics (SD) has long served as a powerful methodology for understanding complex systems, designing policies, supporting strategic decision making, and facilitating organizational learning. At the same time, advances in Artificial Intelligence (AI), particularly Machine Learning (ML), Generative AI, and Large Language Models (LLMs), are transforming the way data are analysed, decisions are made, and simulation models are developed and used.

The convergence of System Dynamics and AI presents significant opportunities for both methodological innovation and practical impact. AI techniques can enhance SD model building, calibration, validation, interpretation, and communication. Conversely, SD offers a robust framework for understanding the dynamic consequences, feedback mechanisms, unintended effects, and governance challenges associated with AI deployment. Furthermore, AI opens new possibilities for model elicitation, stakeholder engagement, automated documentation, model analytics, policy exploration, and simulation-assisted decision support. At the same time, the diffusion of AI technologies is reshaping organizations, industries, and society, creating a growing need for approaches capable of understanding the feedback mechanisms, adaptation processes, and long-term consequences associated with AI-enabled transformation.

Despite growing interest in this intersection, substantial methodological, technical, and theoretical challenges remain. Questions concerning transparency, explainability, reproducibility, human-AI collaboration, model validity, and responsible AI require systematic investigation.

This special issue aims to bring together scholars and practitioners working at the frontier of System Dynamics and AI. We welcome contributions that advance theory, methodology, software tools, applications, and critical reflections on the integration of SD with AI technologies.

Scope

The special issue seeks high-quality original research, technical notes, methodological contributions, review papers, and application studies that explore how AI can enhance System Dynamics modelling and how System Dynamics can contribute to understanding, designing, and governing AI-enabled systems.

The special issue is organized around two complementary domains of contribution. The first domain concerns the application of AI to support or augment System Dynamics modelling activities. Contributions in this domain should demonstrate how AI can contribute to create methodological advances in model development, analysis, validation, communication, or decision support. The second domain refers to the application of System Dynamics to investigate the diffusion and impacts of AI technologies in organizational and societal contexts. Contributions in this domain should advance understanding of the dynamic processes through which AI influences organizational or societal outcomes and inform managerial or policy interventions.

Both conceptual and empirical papers are welcome. Contributions may focus on methodological advances, software developments, practical implementations, educational innovations, or critical assessments of opportunities and limitations.

Guest editors expect that the papers go beyond simple demonstrations. Competitive papers should articulate what the AI component is being used for, and compared against, (e.g., a human-expert baseline, an established method, or a prior model) and should report how performance was assessed, including where the approach fails. Papers reporting negative or inconclusive results are explicitly welcome, provided the evaluation is rigorous. Application papers should make a methodological or substantive contribution beyond the domain itself.

Suggested Topics

Topics of interest include, but are not limited to:

Modelling System Dynamics with AI

• AI-assisted causal loop diagram development
• AI-assisted model conceptualization and problem structuring
• Automated extraction of feedback structures from text and documents
• AI approaches for parameter estimation and calibration
• AI-supported model validation and verification
• AI-enhanced model documentation and reporting
• Intelligent assistants for SD modelling environments
• Natural language interfaces for simulation model development
• AI-supported policy design and scenario generation
• Automated generation of stock-and-flow structures
• Hybrid approaches combining SD and AI
• AI-assisted stakeholder engagement and group model building
• Simulation model repositories enhanced by AI search and retrieval capabilities
• Conversational interfaces for simulation-based policy analysis
• Multi-agent systems involving AI agents and SD models
• Integration of SD models with generative AI workflows
• Prompt engineering for simulation modelling tasks
• Retrieval-augmented generation (RAG) for simulation knowledge management
• Synthetic data generation for SD applications
• Evaluation of AI capabilities in system modelling
• Human-in-the-loop simulation supported by AI

Modelling the societal and organizational implications of AI using System Dynamics

• The use of SD to understand the organizational and strategic consequences of AI adoption and diffusion
• SD and AI for organizational learning, adaptation, resilience, and dynamic capabilities in complex environments
• AI governance and regulatory dynamics
• The role of SD in exploring feedback effects, unintended consequences, and long-term impacts of AI-enabled organizational transformation
• Feedback effects in AI-enabled decision systems and human-AI collaboration and decision making
• Trust, transparency, and explainability in AI systems
• System Dynamics approaches to AI risk assessment
• Modelling unintended consequences of AI deployment
• Workforce transformation and skill adaptation under AI
• Societal implications of large-scale AI adoption
• AI-enabled innovation ecosystems and platform ecosystems, network effects, and AI-driven market dynamics analysed through SD models;
• Governance, ethics, and responsible AI examined through a SD perspective
• AI-enabled business model innovation and the role of SD in understanding business model evolution;
• SD and AI in healthcare, education, mobility, energy, and other socio-technical systems characterized by complex stakeholder interactions;
• Applications of System Dynamics and AI in sustainable transitions, circular economy initiatives, and ESG-related decision-making.

Reproducibility and Open Science

The Journal of Simulation strongly encourages authors to support transparency, reproducibility, and model reuse.

All submissions to this special issue should comply with the Journal of Simulation Model Reproducibility Initiative (JOS-MRI) guidelines. JOS-MRI promotes the sharing and evaluation of simulation artefacts to enhance scientific transparency and facilitate model reuse. According to the initiative, authors may voluntarily submit models, code, data, and related artefacts for reproducibility assessment and recognition. The initiative offers four badges: Open Research Object, Research Object Reviewed, Results Reproduced, and Research Object Reusable. These badges recognise increasing levels of openness, verification, reproducibility, and reusability of simulation research.

While submission of models and associated artefacts for JOS-MRI evaluation is not mandatory, authors are strongly encouraged to do so. Participation can enhance the visibility, credibility, and long-term impact of research contributions. Papers that receive reproducibility badges will have this recognition displayed alongside the published article.

Particular encouragement is given to authors who make their System Dynamics models publicly available through trusted repositories and provide sufficient documentation to facilitate replication, reuse, and extension by other researchers and practitioners.