  Identifying and quantifying factors influencing human decision making remains
an outstanding challenge, impacting the performance and predictability of
social and technological systems. In many cases, system failures are traced to
human factors including congestion, overload, miscommunication, and delays.
Here we report results of a behavioral network science experiment, targeting
decision making in a natural disaster. In each scenario, individuals are faced
with a forced "go" versus "no go" evacuation decision, based on information
available on competing broadcast and peer-to-peer sources. In this controlled
setting, all actions and observations are recorded prior to the decision,
enabling development of a quantitative decision making model that accounts for
the disaster likelihood, severity, and temporal urgency, as well as competition
between networked individuals for limited emergency resources. Individual
differences in behavior within this social setting are correlated with
individual differences in inherent risk attitudes, as measured by standard
psychological assessments. Identification of robust methods for quantifying
human decisions in the face of risk has implications for policy in disasters
and other threat scenarios.
