Information · Meaning · Adaptation
Information dynamics
How does information evolve as it moves through complex communication networks?
Overview
Information rarely remains unchanged as it passes from one person (or a system) to another. Meanings are interpreted, compressed, transformed, and occasionally distorted through continual interaction. As generative AI becomes increasingly embedded in how we create, communicate, and evaluate knowledge, understanding these dynamics has become both a scientific and societal challenge.
This research theme examines how information evolves when humans and AI systems communicate and interact. I am interested in generative AI not merely as a technological artefact, but as part of a broader information ecosystem that shapes how knowledge is produced, circulated, interpreted, and transformed.
My current work investigates semantic dynamics in recursive human–AI communication using tools from topology, geometry, and network science. By analysing trajectories of meaning across multimodal AI systems and large-scale knowledge networks, I seek to develop quantitative diagnostics for semantic drift, information flow, and knowledge integrity. Ultimately, this research aims to identify structural signatures that distinguish resilient knowledge ecosystems from those vulnerable to fragmentation, misinformation, or epistemic degradation, providing foundations for more trustworthy and responsible human–AI collaboration.
