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Related Experiment Videos

Accommodating uncertainty in comparative risk.

Clinton J Andrews1, David M Hassenzahl, Branden B Johnson

  • 1Edward J. Bloustein School of Planning and Public Policy, Rutgers University, New Brunswick, NJ 08901, USA. CJA1@RCI.RUTGERS.EDU

Risk Analysis : an Official Publication of the Society for Risk Analysis
|November 26, 2004
PubMed
Summary

Comparative risk assessments struggle with uncertainty. The New Jersey project used novel methods to address this, finding that while precise rankings are impossible, sorting techniques and deliberative processes offer valuable insights for policy guidance.

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Area of Science:

  • Environmental Science
  • Risk Assessment
  • Policy Analysis

Background:

  • Comparative risk projects often lack scientific rigor for precise risk rankings.
  • Existing projects frequently overlook crucial uncertainties like stochasticity, structural issues, and ignorance.

Purpose of the Study:

  • To examine the methods and lessons learned from the New Jersey Comparative Risk Project.
  • To assess innovative approaches for incorporating uncertainties into risk comparisons.

Main Methods:

  • Utilized Monte Carlo techniques to quantify stochastic uncertainty.
  • Employed sensitivity analysis to manage structural uncertainty.
  • Incorporated a deliberative process and sorting techniques to address ignorance.

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Main Results:

  • Stochastic rankings, while calculable, demonstrated excessive imprecision rendering them impractical for policy.
  • Sorting techniques proved effective in managing risk comparisons despite inherent uncertainties.
  • A deliberative approach helped mitigate analytical overreach.

Conclusions:

  • Precise risk rankings are often unattainable due to significant uncertainties.
  • Integrating methods to manage different types of uncertainty is crucial for robust risk assessment.
  • The New Jersey project highlights the utility of sorting and deliberative processes in informing policy decisions.