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The CSSSA defines computational social science broadly as the theory-driven application of computational methods to analyze, model, and simulate social systems. This includes analyzing large-scale social data, exploring mechanisms underlying social dynamics through simulation,
and connecting insights to social science theory.
The 12th Artificial Intelligence Symposium (AIS) is an international annual conference on artificial intelligence, large language models, machine/deep learning, foundation models, and generative arti…
Held in conjunction with AAMAS 2026 (https://cyprusconferences.org/aamas2026/)
In today’s hyper-connected world, traffic and transportation systems rank among the most complex socio-technical syste…
As we look toward the future of computing at a moment of profound transformation, you are invited to contribute your research, ideas, and
vision to the *14th Computing Conference 2026 <https://saiconf…
As technology advances in different sub-domains of computing, data-driven models are becoming increasingly important. The data-dependent world now faces many challenges in terms of data accuracy and d…
The Future Technologies Conference (FTC) 2025 invites original research contributions for presentation at the upcoming event, scheduled to take place on 6 & 7 November in Munich, Germany. This confere…
AsiaSim (Asia Simulation Conference), 24th in the series, is an annual international conference organized by the Federation of Asia Simulation Societies (ASIASIM) whose current member societies are Ch…
The abundance of data, accessible computing power, and storage has revolutionized science and ushered an era of data-driven scientific discoveries. This talk will cover the challenges involved in capturing and managing computational provenance and examine the evolution of methods and tools that have been proposed to facilitate transparency and reproducibility.
This issue explores integrating systemic and analytical methods to address complex challenges. It promotes studies leveraging system dynamics, AI, and analytical approaches for modeling and decision-making. Topics include theoretical frameworks, methodological innovations, empirical studies, and case studies in policy analysis, strategic planning, and decision support. Submissions focusing on hybrid methodologies for understanding and solving complex problems are encouraged.
Spatial and temporal thinking is important not just because everything happens at some places and at some time, but because knowing where and when things are happening is key to understanding how and why they happened or will happen. Spatial data science is concerned with the representation, modeling, and simulation of spatial processes, as well as with the publication, retrieval, reuse, integration, and analysis of such space- and place-centric data.
Displaying 10 of 52 results machine learning clear search