SEARCH

SEARCH BY CITATION

Keywords:

  • AdaBoost;
  • deep uncertainty;
  • low-regret online decisions;
  • Markov decision process;
  • model ensemble methods;
  • POMDP;
  • reinforcement learning;
  • robust decision making;
  • robust optimization;
  • robust risk analysis;
  • SARSA

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.