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Task-oriented machine learning surrogates for tipping points of agent-based models.

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Summary
This summary is machine-generated.

This study introduces a machine learning framework for creating reduced order models from complex simulations. The approach effectively identifies tipping points and quantifies rare event uncertainties in financial and epidemic models.

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Area of Science:

  • Computational Science
  • Complex Systems Modeling
  • Machine Learning

Background:

  • Agent-based simulators generate complex dynamics, making analysis computationally intensive.
  • Identifying tipping points and quantifying rare event uncertainty are crucial for risk assessment in various systems.

Purpose of the Study:

  • To develop a machine learning framework for constructing effective reduced order models (ROMs).
  • To enable systematic multiscale numerical analysis of emergent dynamics, focusing on tipping point detection and rare event uncertainty quantification.

Main Methods:

  • Integration of manifold learning, neural networks, Gaussian processes, and an Equation-Free multiscale approach.
  • Application to an event-driven stochastic financial market model and a stochastic epidemic model on an Erdös-Rényi network.

Main Results:

  • The framework successfully constructs ROMs from detailed agent-based simulators.
  • Emergent dynamics near tipping points were found to be describable by a one-dimensional stochastic differential equation, revealing intrinsic dimensionality.
  • Computational cost for analysis tasks was significantly reduced.

Conclusions:

  • The proposed machine learning framework offers an efficient approach for analyzing complex system dynamics.
  • The identified intrinsic dimensionality simplifies the analysis of tipping points and rare events.
  • This method provides a powerful tool for understanding and predicting critical transitions in stochastic systems.