The behaviour of complex systems often arises from intricate interactions between multiple components, i.e. not merely pairwise but higher-order interactions. Traditional analysis tools are limited in detecting such complex dependencies. Multivariate Information Theory (MVIT) offers a robust framework to capture and quantify these interactions, providing tools such as Total Correlation, O-Information, and Partial Information Decomposition. [1, 2, 3, 4, 5, 6, 7]

This workshop brings together early-career researchers, students, and domain experts to explore MVIT, both from theoretical and practical perspectives. The program includes foundational lectures, hands-on tutorials, and collaborative project work. Thematic focus areas include applications in a range of complex systems, covering neuroscience, epidemics, and systems biology, with special attention to the overlap between MVIT and high-order network theory.

By blending instruction with collaborative exploration, the MITHIC workshop seeks to advance understanding of how to use multivariate dependencies to study collective behaviour emerging from high order interactions and to foster new interdisciplinary research directions.

This workshop is deeply connected to the EU-funded, Marie Skłodowska-Curie Doctoral Network, ‘BeyondTheEdge’. This network aims to explore how non-pairwise, higher-order interactions drive complex dynamics in networks. This emerging field, Higher-Order Network Dynamics, involves three key themes; foundations, structure, and dynamics. BeyondTheEdge seeks to unify these areas into a cohesive framework for understanding complex systems. MVIT is a promising field for bridging the gap between these areas and therefore fits perfectly into the goal of the BeyondTheEdge consortium. One of the organisers (Cyril Rommens) is an early stage researcher in this project, providing a direct connection between the workshop and the consortium.

1. 1.      Robiglio, T., Neri, M., Coppes, D., Agostinelli, C., Battiston, F., Lucas, M., & Petri, G. (2025). Synergistic signatures of group mechanisms in higher-order systems. Physical review letters, 134(13), 137401.
2. 2.      Marinazzo, D., Angelini, L., Pellicoro, M., & Stramaglia, S. (2019). Synergy as a warning sign of transitions: The case of the two-dimensional Ising model. Physical Review E, 99(4), 040101.
3. 3.      Rosas, F. E., Mediano, P. A., Gastpar, M., & Jensen, H. J. (2019). Quantifying high-order interdependencies via multivariate extensions of the mutual information. Physical Review E, 100(3), 032305.
4. 4.      Varley, T. F., Pope, M., Faskowitz, J., & Sporns, O. (2023). Multivariate information theory uncovers synergistic subsystems of the human cerebral cortex. Communications biology, 6(1), 451.
5. 5.      Baudot, P., Tapia, M., Bennequin, D., & Goaillard, J. M. (2019). Topological information data analysis. Entropy, 21(9), 869.
6. 6.      James, R. G., Ellison, C. J., & Crutchfield, J. P. (2011). Anatomy of a bit: Information in a time series observation. Chaos: An Interdisciplinary Journal of Nonlinear Science, 21(3).
7. 7.      Watanabe, S. (1960). Information theoretical analysis of multivariate correlation. IBM Journal of research and development, 4(1), 66-82.