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An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
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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
1Department of Safety, Economics and Planning, University of Stavanger, Stavanger, Norway.
Summary
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.
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.
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