The study of mathematical models can provide insight into the structure and function of complex systems. However, even simple models can often be quite difficult to analyze, and meaningfully connecting models to real-world data is more challenging still. Omidyar Postdoctoral Fellow Harrison Hartle’s research expertise is in mathematical and computational modeling. He is interested in advancing the study of generative models for complex systems with the goal of constructing practically applicable and meaningfully interpretable models for real-world data.
The models that Hartle plans to work on range from the very simple to the relatively complex, including both null and mechanistic models. Null models can be used to detect nontrivial patterns in data. Simple mechanistic models may exhibit qualitatively realistic behavior but serve primarily as abstractions. More intricate and data-driven models can produce quantitative predictions pertaining to specific real-world systems. Hartle wants to create models that can be applied to areas such as origins-of-life research, immunology, criminal legal systems, and international relations. By working to advance both theory and application, he intends to contribute toward strengthening the theoretical foundations of complexity science and help bridge the divide between the understanding of models and of real-world phenomena.
Hartle studied physics at the University of Alaska, Fairbanks, and holds a Ph.D. in network science from Northeastern University. He has worked on fluid dynamics, nonlinear oscillator systems, and probabilistic network modeling. He joins SFI in October.