Collective Decision Dynamics in Group Evacuation: Behavioral Experiment and Machine Learning Models
This work addresses the problem of predicting collective human behavior in emergencies for policymakers and system designers, but it is incremental as it applies existing machine learning methods to new experimental data.
The study investigated factors influencing group evacuation decisions through a behavioral experiment, finding that individual decision-making success does not strongly predict group performance, and used neural networks to predict outcomes with up to comparable accuracy using social media data.
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.