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Sensitivity analysis of agent-based simulation utilizing massively parallel computation and interactive data

Atsushi Niida1, Takanori Hasegawa2, Satoru Miyano1,3

  • 1Division of Health Medical Computational Science, Health Intelligence Center, Institute of Medical Science, University of Tokyo, Tokyo, Japan.

Plos One
|March 6, 2019
PubMed
Summary
This summary is machine-generated.

We developed MASSIVE (Massively parallel Agent-based Simulations and Subsequent Interactive Visualization-based Exploration), a novel method for agent-based simulation sensitivity analysis. This approach combines parallel computing and visualization for intuitive parameter space exploration.

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Area of Science:

  • Computational Biology
  • Complex Systems Modeling
  • Data Visualization

Background:

  • Sensitivity analysis is crucial for agent-based simulations but existing methods lack exhaustivity and interpretability.
  • Agent-based models (ABMs) are widely used to simulate complex systems, requiring robust analysis techniques.

Purpose of the Study:

  • To introduce a novel methodology, MASSIVE (Massively parallel Agent-based Simulations and Subsequent Interactive Visualization-based Exploration), for enhanced sensitivity analysis of agent-based simulations.
  • To overcome limitations of current sensitivity analysis approaches in terms of exhaustivity and interpretability.

Main Methods:

  • MASSIVE integrates massively parallel computation with interactive data visualization.
  • This unique paradigm allows for intuitive exploration of a broad parameter space in agent-based simulations.

Main Results:

  • The utility of MASSIVE was demonstrated through its application to cancer evolution simulations.
  • The analysis successfully identified specific conditions that lead to the generation of heterogeneous tumors.

Conclusions:

  • MASSIVE offers a powerful and intuitive approach for sensitivity analysis in agent-based simulations.
  • This methodology has the potential to become a standard for analyzing complex simulations, especially with advancing computational power.