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This study introduces an agile social computing approach for effective emergency management policy selection and adjustment. The PZE framework enables rapid adaptation and cost-effective policy development through iterative evaluation.

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Area of Science:

  • Social Computing
  • Emergency Management
  • Artificial Intelligence

Background:

  • Effective emergency management requires timely policy selection, evaluation, and adjustment.
  • Existing methods may lack agility, generality, or cost-effectiveness.

Purpose of the Study:

  • To propose an agile and lightweight social computing approach for emergency management policy.
  • To facilitate quantitative and qualitative policy selection, evaluation, and adjustment.

Main Methods:

  • The PZE framework: Policy selection (P), Zombie-city model for artificial societies (Z), and Policy evaluation (E).
  • Formal specification and reasoning using the zombie-city model.
  • Iterative adjustment via a feedback loop.

Main Results:

  • The approach was verified using an infectious disease transmission case study.
  • Quantitative evaluations and qualitative reasoning demonstrated policy effectiveness.
  • Iterative adjustments led to improved emergency management policies.

Conclusions:

  • The proposed PZE approach enables the iterative development of effective emergency management policies.
  • Benefits include simplicity, generality, rapid adaptability, and low cost compared to existing research.