Individual Decision Biases and Behavioral Correction Strategies in Crowd Evacuation

Individual Decision Biases and Behavioral Correction Strategies in Crowd Evacuation

Problem Description
During emergency evacuations, individual decisions are often influenced by cognitive biases, emotional states, and incomplete environmental information, leading to irrational behaviors (such as blind following, path dependency, ignoring instructions, etc.). These biases can exacerbate congestion, prolong evacuation time, and even trigger secondary incidents. This problem requires a systematic analysis of common types of individual decision biases, exploration of their behavioral manifestations and formation mechanisms, and the design of corresponding behavioral correction strategies to improve overall evacuation efficiency.

Solution Process

1. Identify Key Types of Decision Biases

  • Anchoring Effect: Individuals rely excessively on initial information (e.g., familiar exits), even when new information indicates other paths are more optimal.
  • Herd Behavior: Blindly following the flow of the crowd, neglecting personal independent judgment of the environment.
  • Optimistic Bias: Underestimating personal risk (e.g., believing a fire won't spread to one's own area), leading to delayed action.
  • Information Overload: Inability to effectively process multiple sources of information (e.g., multiple signs, audible alarms) in complex environments, resulting in decision paralysis.

2. Analyze Behavioral Manifestations and Impacts of Biases

  • Anchoring Effect→Congestion at a few familiar exits, underutilization of other exits.
  • Herd Behavior→Formation of a "herd effect," causing localized surges in crowd density and increasing the risk of stampedes.
  • Optimistic Bias→Delayed initiation of evacuation, intensifying competition for paths later.
  • Information Overload→Individuals stagnate or move randomly, reducing overall flow efficiency.

3. Design Behavioral Correction Strategies

  • Dynamic Information Reinforcement:
    • Use dynamic message signs (e.g., electronic displays) to update optimal paths in real-time, countering the anchoring effect.
    • Example: Displaying "Congestion ahead, suggest turning right to Exit B (similar distance)" at a congested exit.
  • Hierarchical Guidance Signals:
    • Differentiate primary guidance signals (e.g., high-frequency flashing lights) from auxiliary information to reduce information overload.
    • Primary signals indicate the general direction, while auxiliary signals provide details (e.g., distance, capacity).
  • Social Proof Intervention:
    • Utilize authority figures (e.g., security personnel) or virtual guidance (e.g., announcements stating "Most people have chosen the east exit") to correct herd behavior.
  • Risk Perception Activation:
    • Use changes in the local environment (e.g., simulated smoke diffusion) to visually signal danger, mitigating optimistic bias.

4. Strategy Validation and Iteration

  • Use Agent-Based Modeling (ABM) to simulate populations with different bias proportions, comparing evacuation times and number of congestion points before and after implementing correction strategies.
  • Parameter Adjustment: For example, optimizing information update frequency (to avoid confusion from frequent changes) or the intensity of guidance signals (to avoid conflict with panic).
  • Limitations: Must consider cultural differences (e.g., trust in authority) and physical environmental constraints (e.g., signal coverage).