Bridget is a Rhodes Scholar and DPhil candidate in Mathematics at the University of Oxford, supervised by Professor Renaud Lambiotte and Professor J. Doyne Farmer. She is affiliated with the Mathematical Institute and the Institute for New Economic Thinking at the Oxford Martin School.
Her work is motivated by the social dynamics that emerge from interactions between people, technologies, institutions, and their environments. These range from how information spreads and shapes beliefs on social media to how individual decisions produce collective outcomes in prediction markets. She develops mathematical methods for understanding the mechanisms behind these complex dynamics, with the goal of building models that are both mathematically meaningful and useful in practice.
Predicting sequences of movements, interactions or events means accounting for both memory (how the past shapes what happens next) and structure (who or what can interact). Higher-order Markov modelscapture memory, but their state space grows exponentially and they quickly become hard to interpret. This project develops the Separable Markov model, a compact and interpretable alternative for settings with long-range memory and limited data.
Human interactions are bursty and follow daily rhythms. This makes it extremely difficult to tell whether one event tends to trigger another, or whether they simply happen at similar times. This work adapts the cross-correlogram, a method from neuroscience, to distinguish lagged dependencies from correlations driven by bursty or rhythmic activity. Applied to 3.1 million posts on X, our method recovers known television broadcast schedules.
Prediction markets are sometimes interpreted as predictions of how likely a future event is to occur. Using a simple model of a prediction market, this work shows that a single trader with sufficient capital can manipulate prices. The key result is that biased traders can temporarily shift market prices, with the magnitude and duration of this distortion increasing if other traders herd toward the market price or are slow to update their internal valuations.
Before Oxford, Bridget completed an MPhil in Applied Mathematics and Statistics at the University of Adelaide as a Westpac Future Leaders Scholar. Her thesis on measuring and modelling information flows in real-world networks received the Dean’s Commendation for Thesis Excellence.
She enjoys teaching probability, statistics, graph theory and information theory at Oxford and is a Non-Stipendiary Lecturer at New College. Bridget is committed to creating a diverse, welcoming and engaged STEM community.
She enjoys sharing research beyond academia. She contributed to the Youth National Security Strategy and has spoken about mis- and disinformation on this podcast.