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Optimization and Control of Agent-Based Models in Biology: A Perspective.

G An1, B G Fitzpatrick2, S Christley3

  • 1Department of Surgery, University of Chicago, Chicago, IL, USA.

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Summary
This summary is machine-generated.

This study introduces a new mathematical approach for agent-based models (ABMs) in life sciences, treating them as surrogates for real systems to enable optimization and control. The method models ABMs using existing mathematical tools, facilitating complex system analysis.

Keywords:
Agent-based modelingOptimal controlOptimizationSystems theory

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Area of Science:

  • Computational Biology and Life Sciences
  • Complex Systems Modeling

Background:

  • Agent-based models (ABMs) are crucial for life sciences, especially for poorly understood systems where equation-based models fail.
  • Analyzing and controlling large-scale ABMs is challenging due to limited mathematical tools, relying primarily on simulation.
  • Existing mathematical frameworks for ABM analysis are insufficient for complex optimization and control tasks.

Purpose of the Study:

  • To propose a novel mathematical framework for optimization and control of agent-based models (ABMs).
  • To treat ABMs as surrogates of actual systems, enabling the application of established mathematical tools.
  • To outline a research program for developing and applying these new mathematical techniques to ABMs.

Main Methods:

  • Viewing ABMs as surrogates for real systems, rather than models themselves.
  • Modeling the surrogate system using data from the ABM within frameworks like differential equations or difference equations.
  • Applying optimization and control strategies to the surrogate model and lifting solutions to the actual system.

Main Results:

  • Demonstrated the approach using Sugarscape and a consumer-resource ABM, illustrating dimension reduction and approximation techniques.
  • Highlighted the significant mathematical challenges in applying this method to large, complex ABMs.
  • Showcased the potential of using established mathematical tools for ABM optimization and control.

Conclusions:

  • The proposed surrogate modeling approach offers a promising avenue for enhancing the mathematical analysis of ABMs.
  • Significant mathematical research is required to overcome the challenges for applying this to complex, large-scale ABMs.
  • This work lays the foundation for a new research program in ABM optimization and control within the life sciences.