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Algorithm for model validation: theory and applications.

D Sornette1, A B Davis, K Ide

  • 1Institute of Geophysics and Planetary Physics and Departments of Earth and Space Sciences and Atmospheric and Oceanic Sciences, University of California, Los Angeles, CA 90095, USA. dsornette@ethz.ch

Proceedings of the National Academy of Sciences of the United States of America
|April 11, 2007
PubMed
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Model validation is redefined as an iterative process, moving from static claims to dynamic constructive approximation. This approach better captures the fuzzy nature of validation by considering experimental novelty and predictive accuracy.

Area of Science:

  • Computational modeling
  • Scientific validation
  • Model trustworthiness

Background:

  • Model validation is critical for decisions in industry and government.
  • Current validation methods struggle with the inherent complexity and uncertainty of real-world systems.
  • Implicit validation processes in science lack formal representation.

Purpose of the Study:

  • To propose a new framework for model validation.
  • To represent the progressive build-up of trust in a model formally.
  • To replace static validation claims with a dynamic, constructive approximation process.

Main Methods:

  • Formulating model validation as an iterative construction process.
  • Developing a formal representation of trust accumulation in models.

Related Experiment Videos

  • Factoring in experimental novelty/redundancy and model predictive accuracy.
  • Main Results:

    • A dynamic approach to validation is presented, better suited for fuzzy, coarse-grained realities.
    • The methodology is illustrated with diverse examples, including quantum mechanics, earthquake models, financial time series, solar radiation transport, and fluid dynamics.
    • A formal method for assessing model trustworthiness is established.

    Conclusions:

    • The proposed iterative validation framework offers a more realistic and adaptable approach.
    • This dynamic process enhances the understanding and application of scientific models.
    • The methodology provides a robust tool for evaluating model reliability across various scientific domains.