Improving Individual Risk Management Decisions: Learning from Experience and Coping with Model Uncertainty

Louis Anthony Cox
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Louis Anthony Cox: Cox Associates

Chapter Chapter 2 in Improving Risk Analysis, 2012, pp 59-95 from Springer

Abstract: Abstract Chapter 1 argued that causal modeling allows risk managers to predict the probable consequences of alternative actions, thereby supporting rational (consequence-driven) deliberation and decision-making. This is practical when enough knowledge and data are available to create and validate causal models, using technical methods such as influence diagrams or simulation models, or more black-box statistical methods such as Granger causality testing and intervention analysis. But what should a decision-maker do when not enough is known to construct a reliable causal model? How can risk analysts help to improve policy and decision-making when the correct probabilistic causal relation between alternative acts and their probable consequences is unknown? This is the challenge of risk management with model uncertainty. It drives technical debates and policy clashes in problems from preparing for climate change, to managing emerging diseases, to operating complex and hazardous facilities safely.

Keywords: Reinforcement Learning; Markov Decision Process; Robust Optimization; Bayesian Model Average; Ambiguity Aversion (search for similar items in EconPapers)
Date: 2012
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Persistent link: https://EconPapers.repec.org/RePEc:spr:isochp:978-1-4614-6058-9_2

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DOI: 10.1007/978-1-4614-6058-9_2

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