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An Active Learning Method for the Comparison of Agent-based Models.

Swapna Thorve1, Zhihao Hu2, Kiran Lakkaraju3

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Summary
This summary is machine-generated.

We present a new method to compare agent-based models, even with different data or structures. This approach identifies distinct behavioral regions by mapping model responses, aiding in comparative analysis.

Keywords:
active learningagent-based modelingmodel comparisonresponse surface methods

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Area of Science:

  • Computational Social Science
  • Complex Systems Modeling

Background:

  • Agent-based models (ABMs) are powerful tools for simulating complex systems.
  • Comparing ABMs with differing structures or datasets presents significant challenges.
  • Identifying distinct model behaviors is crucial for robust analysis.

Purpose of the Study:

  • To develop a novel methodology for comparing multiple agent-based models within a common domain.
  • To enable comparison of ABMs despite variations in data application and internal structure.
  • To facilitate the identification and comparison of qualitatively different model behaviors.

Main Methods:

  • Learning a response surface within the common parameter space of the agent-based models.
  • Utilizing an active learning algorithm to identify phase transition boundaries.
  • Applying the methodology to compare two agent-based models of rooftop solar panel adoption.

Main Results:

  • The developed methodology effectively maps the response surface across common parameters.
  • Phase transition boundaries indicative of distinct behaviors were successfully identified.
  • Comparative analysis of rooftop solar panel adoption models demonstrated the approach's utility.

Conclusions:

  • The proposed methodology offers a robust framework for comparing diverse agent-based models.
  • Mapping response surfaces and identifying behavioral regions enhances model interpretability.
  • This approach advances the field of agent-based model comparison and validation.