Addressing Wicked Problems and Deep Uncertainties in Risk Analysis

Louis Anthony Cox
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Louis Anthony Cox: Cox Associates and University of Colorado

Chapter Chapter 7 in AI-ML for Decision and Risk Analysis, 2023, pp 215-249 from Springer

Abstract: Abstract How can decision analysts and 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. This chapter discusses 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.

Keywords: Model uncertainty; Deep uncertainties; Wicked problems; Expected utility theory; Multiple priors decision models; Robust control; Robust decision optimization; Bayesian Model Averaging (BMA); Resampling; model ensembles; Low-regret online decisions; Reinforcement Learning (RL); Markov decision processes (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:isochp:978-3-031-32013-2_7

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DOI: 10.1007/978-3-031-32013-2_7

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