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Measuring and modeling behavioral decision dynamics in collective evacuation.

Jean M Carlson1, David L Alderson2, Sean P Stromberg1

  • 1Department of Physics, University of California Santa Barbara, Santa Barbara, California, United States of America.

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|February 13, 2014
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Summary
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This study quantifies factors influencing human evacuation decisions during disasters. A new model captures collective behavior, aiding disaster response strategies.

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Area of Science:

  • Behavioral Network Science
  • Decision Making
  • Disaster Response

Background:

  • Human decision-making factors are critical for system performance but remain challenging to quantify.
  • System failures are often linked to human factors like congestion, overload, miscommunication, and delays.
  • Understanding individual and collective responses in crisis situations is vital for effective disaster management.

Purpose of the Study:

  • To quantify key factors influencing individual evacuation decision-making in a natural disaster scenario.
  • To develop a quantitative model of human decision-making based on empirical evacuation data.
  • To assess the impact of information sources (broadcast vs. peer-to-peer), temporal urgency, and shelter capacity on evacuation behavior.

Main Methods:

  • Conducted a behavioral network science experiment in a controlled laboratory setting.
  • Measured the cumulative rate of evacuations as a function of instantaneous disaster likelihood.
  • Developed and validated a quantitative decision-making model against observed collective behavior.

Main Results:

  • Quantified key factors affecting individual evacuation decisions, including information source, urgency, and capacity constraints.
  • Developed a predictive model that accurately captures main features of collective evacuation behavior across scenarios.
  • The model demonstrates sensitivity to external pressures and identifies variability in collective responses.

Conclusions:

  • Robust methods for quantifying human decisions under risk are essential for disaster policy.
  • Findings support the development and testing of evacuation strategies that integrate human behavior and network topology.
  • This research provides a quantitative basis for improving disaster preparedness and response.