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Related Experiment Videos

Robust reasoning with agent-based modeling.

Steven Bankes1, Robert Lempert

  • 1RAND, 1700 Main Street, Santa Monica, CA 90407, USA. bankes@rand.org

Nonlinear Dynamics, Psychology, and Life Sciences
|April 8, 2004
PubMed
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Agent-based modeling (ABM) requires robust inference methods beyond prediction. This study introduces reasoning with ensembles of alternative models to draw reliable conclusions about complex systems.

Area of Science:

  • Complex Systems Science
  • Computational Science
  • Scientific Modeling

Background:

  • Agent-based modeling (ABM) is valuable for nonlinear systems but lacks rigorous inference methods.
  • Rigor cannot rely solely on physical measurement prediction due to system nonlinearity.

Purpose of the Study:

  • To present an alternative approach for robust reasoning in agent-based modeling.
  • To enable reliable inferences from ABM by using ensembles of models.

Main Methods:

  • Developing ensembles of alternative ABMs that plausibly span system classes.
  • Employing research methodologies for searching and sampling within these ensembles.
  • Implementing a competition between problem formulations and robust conclusions.

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Main Results:

  • Demonstrated a method for drawing plausible conclusions about invariant properties of ABM ensembles.
  • Showcased how ensembles support robust reasoning about classes of systems.
  • Provided examples from research illustrating the ensemble approach.

Conclusions:

  • Ensembles of alternative models offer a rigorous framework for ABM inference.
  • This approach enhances the scientific utility of agent-based modeling for nonlinear systems.
  • Robust conclusions can be achieved by testing models against challenges.