Context, Composition, Automation, and Communication -- The C2AC Roadmap for Modeling and Simulation
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Abstract (arXiv)
Simulation has become, in many application areas, a sine-qua-non. Most recently, COVID-19 has underlined the importance of simulation studies and limitations in current practices and methods. We identify four goals of methodological work for addressing these limitations. The first is to provide better support for capturing, representing, and evaluating the context of simulation studies, including research questions, assumptions, requirements, and activities contributing to a simulation study. In addition, the composition of simulation models and other simulation studies' products must be supported beyond syntactical coherence, including aspects of semantics and purpose, enabling their effective reuse. A higher degree of automating simulation studies will contribute to more systematic, standardized simulation studies and their efficiency. Finally, it is essential to invest increased effort into effectively communicating results and the processes involved in simulation studies to enable their use in research and decision-making. These goals are not pursued independently of each other, but they will benefit from and sometimes even rely on advances in other subfields. In the present paper, we explore the basis and interdependencies evident in current research and practice and delineate future research directions based on these considerations.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 9 Oct 2023.
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Citing papers per year: 2025: 2; 2026: 2.
4 papers.
- 2025Simulation Agent: A Framework for Integrating Simulation and Large Language Models for Enhanced Decision-Makingcited by 4
- 2025Using (Not-so) Large Language Models to Generate Simulation Models in a Formal DSL: A Study on Reaction Networkscited by 1
- 2026On Integrating Resilience and Human Oversight into LLM-Assisted Modeling Workflows for Digital Twinscited by 1
- 2026Simulate, Reason, Decide: Scientific Reasoning with LLMs for Simulation-Driven Decision Makingcited by 0
- 2026Simulate, Reason, Decide: Scientific Reasoning with LLMs for Simulation-Driven Decision Making3 Jun 2026
- 2026On Integrating Resilience and Human Oversight into LLM-Assisted Modeling Workflows for Digital Twins26 Mar 2026
- 2025Simulation Agent: A Framework for Integrating Simulation and Large Language Models for Enhanced Decision-Making19 May 2025
- 2025Using (Not-so) Large Language Models to Generate Simulation Models in a Formal DSL: A Study on Reaction Networks3 Mar 2025