  Identifying factors that affect human decision making and quantifying their
influence remain essential and challenging tasks for the design and
implementation of social and technological communication systems. We report
results of a behavioral experiment involving decision making in the face of an
impending natural disaster. In a controlled laboratory setting, we characterize
individual and group evacuation decision making influenced by several key
factors, including the likelihood of the disaster, available shelter capacity,
group size, and group decision protocol. Our results show that success in
individual decision making is not a strong predictor of group performance. We
use an artificial neural network trained on the collective behavior of subjects
to predict individual and group outcomes. Overall model accuracy increases with
the inclusion of a subject-specific performance parameter based on laboratory
trials that captures individual differences. In parallel, we demonstrate that
the social media activity of individual subjects, specifically their Facebook
use, can be used to generate an alternative individual personality profile that
leads to comparable model accuracy. Quantitative characterization and
prediction of collective decision making is crucial for the development of
effective policies to guide the action of populations in the face of threat or
uncertainty.
