John Hotchkiss is a lead data scientist with experience in generating data science solutions to support policy decision makers in many domains, including education and epidemiology.
Hotchkiss is both a methodologist and technician who seeks problems that will require solutions that draw on the intersection of those skills. Currently, Hotchkiss applies his training building agent-based models that simulate the spread of SARS-CoV-2 in educational contexts under varied transmission mitigation strategies to inform safe reopening strategies. Current clients include the University of California San Diego and the Rockefeller Foundation. He holds an M.S. in data analytics from Georgetown University.
This slide deck presents results from Mathematica’s agent-based simulation model, grounded in real-world voter data, to compare ranked-choice voting rules on their ability to elect moderate candidates who represent the electorate and their robustness to voter polarization and spoiler candidates.
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