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Data-driven predictive modeling in risk assessment: Challenges and directions for proper uncertainty representation.

Kaia Stødle1, Roger Flage1, Seth D Guikema2

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Data-driven predictive models in risk assessments often fail to capture epistemic uncertainty. A risk science foundation reveals these models need to address underlying assumption uncertainties for complete risk descriptions.

Keywords:
assumptionsdata-driven predictive modelingrisk assessmentrisk descriptionuncertainty representation

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Area of Science:

  • Risk assessment
  • Predictive modeling
  • Uncertainty quantification

Background:

  • Data-driven predictive modeling is prevalent in risk assessments.
  • These models offer improved consequence predictions and probability estimates.
  • A key challenge is their inability to measure and represent epistemic uncertainty (uncertainty due to lack of knowledge).

Purpose of the Study:

  • To conceptually link data-driven predictive models with risk science principles.
  • To evaluate these models against recommendations for a complete risk description.
  • To identify the need for assessing uncertainties related to model assumptions.

Main Methods:

  • Conceptual linkage of data-driven predictive modeling elements to general risk description elements.
  • Evaluation of modeling outputs against risk science completeness criteria.
  • Discussion of an approach for assessing assumptions in data-driven models.

Main Results:

  • Data-driven predictive models, when placed on a risk science foundation, highlight limitations in representing epistemic uncertainty.
  • A complete risk description requires addressing uncertainties associated with the underlying assumptions of these models.

Conclusions:

  • Risk assessments using data-driven predictive modeling must incorporate assessments of uncertainty and risk related to model assumptions.
  • An approach for assessing these assumptions is discussed to enhance risk description completeness.