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Sensitivity-driven simulation development: a case study in forced migration.

D Suleimenova1, H Arabnejad1, W N Edeling2

  • 1Department of Computer Science, Brunel University London, London, UK.

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|March 29, 2021
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Summary
This summary is machine-generated.

Sensitivity analysis (SA) guides simulation development by identifying key assumptions. This sensitivity-driven simulation development (SDSD) refines models, improving predictions for forcibly displaced populations.

Keywords:
agent-based modellingforced migration predictionsensitivity analysissimulation development approachuncertainty quantification

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Area of Science:

  • Computational modeling
  • Simulation science
  • Computational social science

Background:

  • Millions are forcibly displaced globally, necessitating accurate migration prediction for humanitarian aid.
  • Agent-based models (ABMs) are crucial for simulating forced migration but require robust validation.
  • Direct calibration to validation data can be challenging and may not improve model reliability.

Purpose of the Study:

  • To introduce and demonstrate a novel approach, sensitivity-driven simulation development (SDSD), for refining computational models.
  • To guide simulation development using sensitivity analysis (SA) without direct calibration to validation data.
  • To enhance the reliability and reproducibility of agent-based models for simulating forced displacement.

Main Methods:

  • Implementing sensitivity analysis (SA) to identify pivotal assumptions and parameters in agent-based models (ABMs).
  • Utilizing SA results to focus model ruleset refinement on critical assumptions.
  • Iteratively applying SA to assess and balance parameter sensitivity for improved model robustness.

Main Results:

  • The sensitivity-driven simulation development (SDSD) approach successfully guided the refinement of agent-based models.
  • Initial SA identified several pivotal parameters influencing model validation outcomes.
  • Subsequent SA iterations demonstrated an average reduction of 54% in the relative sensitivity of these parameters.

Conclusions:

  • SDSD offers an effective alternative to direct calibration for improving simulation model reliability.
  • By focusing refinement on sensitive parameters, SDSD enhances the trustworthiness of computational models for forced migration.
  • This approach contributes to verification, validation, and uncertainty quantification in computational science.