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My doctoral research is concerned with how communities form opinions, especially with regard to scientific questions. I design and explore computer models which simulate networks of scientists who generate random samples to answer particular scientific questions. In doing so, I reveal unacknowledged and unappreciated features that are inherent to science on a fundamental level, which in turn informs our understanding of more complicated situations. In addition, I critically analyze the limitations of such techniques and their ability (as well as inability) to reveal different aspects of the situations at hand.
More specifically, I represent scientists as members of a network (shown on the right) who each produce their own statistical research and share it with anyone they are connected to. I decipher the effects of different network structures as well as sharing, trusting, and updating strategies for the scientists. The result is novel, elegant explanations of multiple epistemic community issues such as polarization and group think. Unlike previous explanations, these rely solely on the nature of statistical data and its distribution, which works independently from whether or not the agents themselves are rational.
My PhD dissertation includes a full explanation of the models and their results.
Hierarchical machine learning models are constituted by layers of models. These allow for incremental categorization of hierarchical class structures. (E.g. predicting 'mammal' before 'dog'.) Doing so speeds up performance by dividing the difficulty of the problem as well as the data that each sub-model needs to process. Additionally, it provides predictions at intermediate levels thereby increasing insights and facilitating hyperparameter tuning.
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