Machine Learning for Collective Behavior

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This research will develop and apply new machine learning methods to model individual behavior in the context of collective problem-solving. The experimental task in earlier empirical research with human subjects was to solve challenging distributed computational problems on networks, such as graph coloring, from only local information. The goal of the new work is to develop machine learning methods whose outputs can accurately reconstruct and predict collective behavior from individual models, and can shed light on related questions such as the empirical diversity of strategies within a human population, and its importance for effective collective behavior.This project contributes in novel ways to several distinct research communities, including machine learning, sociology, economics, and related fields. It integrates research and education in two ways: by giving both graduate students and undergraduates the experience of participating in a cutting-edge research study, and by providing new curriculum for a course entitled "Networked Life."