Research & causality
MSc Thesis: Synergy
A causal framework to quantify synergy and redundancy in complex systems.
Overview
The thesis introduces a causality-based framework to study emergence, synergy, and redundancy in complex systems. It reformulates information-theoretic definitions into a constructive approach grounded in system history.
The results highlight how complex systems encode shared signals and provide tooling for measuring emergent dependencies in simulated and empirical settings.
Key features
- Formalized synergy metrics for multivariate causal graphs.
- Validated the framework with cellular automata and graph simulations.
- Delivered reproducible notebooks for academic replication.
- Summarized findings with visuals presented at NetSci 2025.
Technical approach
The work uses probabilistic modeling, causal discovery algorithms, and information decomposition techniques implemented in Python and Julia, with plots produced for academic presentation.
Results & impact
The framework helps researchers quantify emergent structure in complex systems and supports ongoing work in causal inference for networks.