Collective Decision Dynamics in Group Evacuation: Behavioral Experiment and Machine Learning Models
Chantal Nguyen, Fangqiu Han, Kimberly J. Schlesinger, Izzeddin G\"ur,, Jean M. Carlson

TL;DR
This study combines behavioral experiments and machine learning to analyze and predict individual and group evacuation decisions during natural disasters, highlighting factors influencing decision-making and the potential of social media data.
Contribution
It introduces a neural network model trained on experimental data and social media profiles to predict evacuation outcomes, advancing understanding of collective decision dynamics.
Findings
Model accuracy improves with subject-specific performance parameters.
Social media activity can effectively substitute laboratory performance data.
Group decision success is not strongly linked to individual decision quality.
Abstract
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…
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Taxonomy
TopicsOpinion Dynamics and Social Influence · Evacuation and Crowd Dynamics · Complex Network Analysis Techniques
