Measuring and Modeling Behavioral Decision Dynamics in Collective Evacuation

TL;DR

This study investigates how individuals make evacuation decisions during disasters by analyzing behavioral data and modeling factors like risk perception, information sources, and social influence to improve prediction and policy design.

Contribution

It introduces a quantitative decision-making model incorporating disaster factors, social network effects, and individual risk attitudes, advancing understanding of human behavior in emergency scenarios.

Findings

Behavioral differences correlate with risk attitudes.

A decision model predicts evacuation choices based on disaster and social factors.

Quantitative methods for assessing human decision-making under risk are developed.

Abstract

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.