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Risk Anal. 2012 Oct;32(10):1607-29. doi: 10.1111/j.1539-6924.2012.01792.x. Epub 2012 Apr 10.

Confronting deep uncertainties in risk analysis.

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Associates and University of Colorado, 503 Franklin St., Denver, CO 80218, USA.


How can risk analysts help to improve policy and decision making when the correct probabilistic relation between alternative acts and their probable consequences is unknown? This practical challenge of risk management with model uncertainty arises in problems from preparing for climate change to managing emerging diseases to operating complex and hazardous facilities safely. We review constructive methods for robust and adaptive risk analysis under deep uncertainty. These methods are not yet as familiar to many risk analysts as older statistical and model-based methods, such as the paradigm of identifying a single "best-fitting" model and performing sensitivity analyses for its conclusions. They provide genuine breakthroughs for improving predictions and decisions when the correct model is highly uncertain. We demonstrate their potential by summarizing a variety of practical risk management applications.

[Indexed for MEDLINE]

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