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"This Is What We Don't Know": Treating Epistemic Uncertainty in Bayesian Networks for Risk Assessment.

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Communicating epistemic uncertainty is crucial in environmental risk assessment (ERA). This study proposes a Bayesian network (BN) framework to consistently address knowledge gaps and improve decision-making in ERA.

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Area of Science:

  • Environmental Science
  • Risk Assessment
  • Computational Modeling

Background:

  • Environmental risk assessment (ERA) increasingly uses Bayesian networks (BNs) for integrating diverse data and expert judgment.
  • Effective communication of epistemic uncertainty (knowledge limitations) in ERA is vital for sound decision-making.
  • Current BN applications in ERA may have gaps in fully addressing epistemic uncertainty.

Purpose of the Study:

  • To identify and address potential gaps in treating epistemic uncertainty within Bayesian networks for ERA.
  • To propose a consistent framework and methods for managing epistemic uncertainty in ERA using BNs.
  • To enhance the communication of both direct and indirect uncertainties in environmental risk assessments.

Main Methods:

  • Development of a comprehensive framework for handling epistemic uncertainty in BNs across various components (structure, parameters, data, etc.).
  • Analysis of different Bayesian network types (epistemic, aleatory, predictive) for their suitability in treating parameter uncertainty.
  • Recommendation for embedding aleatory BNs within parameter uncertainty models and utilizing external statistical models for data variability.

Main Results:

  • The proposed framework systematically addresses epistemic uncertainty in model structure, parameters, expert judgment, data, and management scenarios.
  • Distinction between aleatory and epistemic uncertainty is clarified, emphasizing the communication of both predictive uncertainty and knowledge strength.
  • Specific methods are outlined for incorporating parameter uncertainty and data variability into BN-based ERA.

Conclusions:

  • A consistent framework for treating epistemic uncertainty in BNs is essential for robust ERA.
  • Careful selection of methods is crucial for accurately communicating direct and indirect uncertainties.
  • Openness about knowledge limitations and precise uncertainty communication are paramount for effective environmental risk management.