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Background
The rapid advancement of computational resources, spanning both hardware and software, has transformed research across many complex systems domains. Computational approaches now represent a fundamental pillar of modern scientific inquiry, enabling more efficient investigation of complex phenomena and fostering collaboration between experimental, theoretical, and computational researchers.
Advances in modelling, simulation, and artificial intelligence have substantially increased the realism, scale, and predictive capabilities of computational methods. Complex systems, ranging from biological and ecological systems to social and technological networks, can increasingly be studied through multi-scale computational frameworks informed by large and heterogeneous datasets.
At the same time, there is growing recognition that computational methods should not only provide predictive power, but also support interpretability and mechanistic understanding. Explainable computational approaches, including mechanistic modelling, interpretable AI, and hybrid data-driven methods, are therefore becoming increasingly important.
This workshop aims to bring together researchers working on computational and AI-assisted approaches to complex systems in order to present ongoing work, exchange ideas, discuss methodological challenges, and foster interdisciplinary collaboration.
Scope
The goal of this research topic is to present, discuss, and exchange ideas on computational approaches for the study of complex systems that go beyond purely predictive performance. Given the increasing importance of explainability, interpretability, and mechanistic understanding, we are particularly interested in approaches that address current limitations of black-box AI techniques and support more transparent and trustworthy scientific modelling.
The meeting aims to encourage open discussion of current methodological challenges, existing gaps, and opportunities for interdisciplinary collaboration across areas such as biological, ecological, social, technological, and economic complex systems.
A particular focus will be on platforms, frameworks, and software tools that facilitate explainable model generation, simulation, comparison, and validation. Ideally, these approaches should support reproducibility, extensibility, and collaborative development through open-source practices and shared computational infrastructures.
Ultimately, we envision this meeting as a stepping stone toward broader international collaboration, community building, and future joint funding initiatives in explainable and computational complex systems research.
Agenda (TBD)
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