Conference Agenda
Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).
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Daily Overview |
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Simulation-Based Inference for Complex Social Models
This three-hour workshop (using Python) introduces participants to simulation-based inference and its applications to complex social models, bridging the gap between agent-based simulations and experimental research. | ||
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This three-hour workshop introduces participants to simulation-based inference (SBI) and its applications to complex social models, bridging the gap between agent-based simulations and experimental research [2, 3]. The session is structured around interactive Python Jupyter notebooks and focuses on a practical, hands-on learning experience. Participants execute code and complete exercises immediately following each content section. The schedule is divided into two parts. First, we will cover the fundamental SBI workflow and best practices using the sbi Python package [1, 3], guiding attendees through toy inference problems (e.g., projectile motion). Second, we will introduce social learning models based on [5] and apply SBI to infer specific parameters (e.g., learning rate and social weight) from behavioural measures. Participants are encouraged to bring a laptop and are required to have a basic Python 3 knowledge to fully engage with the coding activities. References [1] Boelts, J., Deistler, M., Gloeckler, M., Tejero-Cantero, Á., Lueckmann, J.-M., Moss, G., Steinbach, P., Moreau, T., Muratore, F., Linhart, J., Durkan, C., Vetter, J., Miller, B. K., Herold, M., Ziaeemehr, A., Pals, M., Gruner, T., Bischoff, S., Krouglova, N., … Macke, J. H. (2025). sbi reloaded: A toolkit for simulation-based inference workflows. Journal of Open Source Software, 10(108), 7754. [2] Cranmer, K., Brehmer, J., & Louppe, G. (2020). The frontier of simulation-based inference. Proceedings of the National Academy of Sciences, 117(48), 30055–30062. [3] Deistler, M., Boelts, J., Steinbach, P., Moss, G., Moreau, T., Gloeckler, M., Rodrigues, P. L. C., Linhart, J., Lappalainen, J. K., Miller, B. K., Gonçalves, P. J., Lueckmann, J.-M., Schröder, C., & Macke, J. H. (2025). Simulation-Based Inference: A Practical Guide (No. arXiv:2508.12939). arXiv. [4] Ramalho, L. (2022). Fluent Python: Clear, concise, and effective programming (2nd ed.). O'Reilly Media. [5] Toyokawa, W., Whalen, A., & Laland, K. N. (2019). Social learning strategies regulate the wisdom and madness of interactive crowds. Nature Human Behaviour, 3(2), 183–193. |