Learning Game-theoretic Models from Aggregate Behavioral Data with Applications to Vaccination Rates in Public Health

摘要

In this paper, we undertake the challenging task of uncovering independencies of public-health behavioral data on populations' vaccination rates collected by government officials in the United States. We use computational game theory to model such data as the result of distributed decision-making at the reported granularity level (e.g., nations and states). To achieve our task, we posit the view of aggregated behavioral data as jointly randomized, or mixed, strategies of multiple agents. We propose a novel general machine-learning approach to learn game-theoretic models within a given hypothesis class of games from any potentially noisy dataset of mixed strategies. We illustrate our framework using publicly available data on vaccination rates in the continental USA.