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This responds to an "evaluation" of the classical model for structured expert judgment by Bolger and Rowe in this issue. This response references extensive expert judgment performance data in the public domain which played no role in their evaluation.

This paper prices the risk of climate change by calculating a lower bound for the price of a virtual insurance policy against climate risks associated with the business as usual (BAU) emissions path. In analogy with ordinary insurance pricing, this price depends on the current risk to which society is exposed on the BAU emissions path and on a second emissions path reflecting risks that society is willing to take. The difference in expected damages on these two paths is the price which a risk neutral insurer would charge for the risk swap excluding transaction costs and profits, and it is also a lower bound on society's willingness to pay for this swap. The price is computed by (1) identifying a probabilistic risk constraint that society accepts, (2) computing an optimal emissions path satisfying that constraint using an abatement cost function, (3) computing the extra expected damages from the business as usual path, above those of the risk constrained path, and (4) apportioning those excess damages over the emissions per ton in the various time periods. The calculations follow the 2010 US government social cost of carbon analysis, and are done with DICE2009.

A model is constructed for the failure frequency of underground pipelines per kilometer year, as a function of pipe and environmental characteristics. The parameters in the model were quantified, with uncertainty, using historical data and structured expert judgment. Fifteen experts from institutes in The Netherlands, the United Kingdom, Italy, France, Germany, Belgium, Denmark, and Canada participated in the study.

Setting action levels or limits for health protection is complicated by uncertainty in the dose‐response relation across a range of hazards and exposures. To address this issue, we consider the classic newsboy problem. The principles used to manage uncertainty for that case are applied to two stylized exposure examples, one for high dose and high dose rate radiation and the other for ammonia. Both incorporate expert judgment on uncertainty quantification in the dose‐response relationship. The mathematical technique of probabilistic inversion also plays a key role. We propose a coupled approach, whereby scientists quantify the dose‐response uncertainty using techniques such as structured expert judgment with performance weights and probabilistic inversion, and stakeholders quantify associated loss rates.

A common problem in ethics is that people often desire an end but fail to take the means necessary to achieve it. Employers and employees may desire the safety end mandated by performance standards for pollution control, but they may fail to employ the means, specification standards, necessary to achieve this end. This article argues that current (de jure) performance standards, for lowering employee exposures to ionizing radiation, fail to promote de facto worker welfare, in part because employers and employees do not follow the necessary means (practices known as specification standards) to achieve the end (performance standards) of workplace safety. To support this conclusion, the article argues that (1) safety requires attention to specification, as well as performance, standards; (2) coal‐mine specification standards may fail to promote performance standards; (3) nuclear workplace standards may do the same; (4) choosing appropriate means to the end of safety requires attention to the ways uncertainties and variations in exposure may mask violations of standards; and (5) correcting regulatory inattention to differences between de jure and de facto is necessary for achievement of ethical goals for safety.

A general discussion of knowledge dependence in risk calculations shows that the assumption of independence underlying standard Monte Carlo simulation in uncertainty analysis is frequently violated. A model is presented for performing Monte Carlo simulation when the variabilities of the component failure probabilities are either negatively or positively coupled. The model is applied to examples in human reliability analysis and the results are compared to the results of Sandia Laboratories as published in the Peer Review Study and to recalculations using more recent methods of uncertainty analysis.