  Quantifying uncertainties in collective human behavior and decision making is
crucial for ensuring public health and safety, enabling effective disaster
response, informing the design of transportation and communication networks,
and guiding the development of new technologies. However, modeling and
predicting such behavior is notoriously difficult, due to the influence of a
variety of complex factors such as the availability and uncertainty of
information, the interaction and influence of social groups and networks, the
degree of risk or time pressure involved in a situation, and differences in
individual personalities and preferences. Here, we develop a stochastic model
of human decision making to describe the empirical behavior of subjects in a
controlled experiment simulating a natural disaster scenario. We compare the
observed behavior to that of statistically optimal Bayesian decision makers,
quantifying the extent to which human decisions are optimal and identifying the
conditions in which sub-optimal decisions are made. Finally, we investigate how
human evacuation strategies change when decisions are made in groups under a
variety of different rules, and whether these group strategy adjustments are
optimal or beneficial.
