In 1948, the RAND Corporation, formed to connect military planning with research and development decisions, became an independent nonprofit organization. Before then, cost-effectiveness analysis, benefit-cost analysis, and systems analysis had no established home in the federal government. In the 1950s, under the leadership of Charles Hitch, Chief, RAND Economics, undertook a program of activities they called “systems analysis,” including evaluation of the costs and effectiveness of weapon systems. In 1961, Robert McNamara appointed Hitch to be the Comptroller of the Department of Defense and invited Hitch to carry out his vision he described as “Programming and Systems Analysis.” Programming became the Planning, Programming, Budgeting System (PPBS) and the Five-Year Defense Program that linked strategies to forces to budgets. Systems Analysis assisted the Secretary to make choices of weapon systems and strategies. In 1965, Hitch returned to California and ultimately became President of the university. McNamara wanted Systems Analysis to report directly to him, and on his recommendation, President Lyndon Johnson appointed me Assistant Secretary for Systems Analysis. In 1966, the President directed that all departments in the executive branch establish offices based on the Systems Analysis model. In 1967, Henry S. Rowen became President of the RAND Corporation. He broadened RAND’s scope beyond the military to include Health Services, education, urban problems including homelessness, ethics in scientific research, and climate research. In 1970, Rowen led the establishment of the Pardee RAND Graduate School, offering a doctoral degree in Public Policy Analysis to extend widely the application of the RAND Systems Analysis approach to many fields.

In benefit-cost analysis, fatality risk reductions are usually valued based on estimates of adults’ willingness to pay for changes in their own risks, regardless of whether the risk reduction accrues to adults or children. This approach reflects the relatively large number of valuation studies that address adults; however, the literature on children is growing. We review these studies, focusing on those that estimate values for both adults and children using a consistent approach to limit the effects of between-study variability. We rely on explicit selection criteria to identify studies that measure reasonably comparable outcomes and are candidates for application to analyses of U.S. policies. The ratio of values for children to values for adults ranges from 0.6 to 2.9; however, most estimates are greater than 1.5. Although some studies suggest that the divergence between child and adult values decreases as the child ages, this finding is not universal. We conclude that analysts should test the sensitivity of their results to the use of higher values for children than adults. Additional empirical research is needed to support more precise estimates of the variation in values by age that can be featured in the primary analysis.

The value of the risk to life is a key element for benefit-cost analysis, enabling more rational public policy decisions in diverse areas as environmental, health, and crime. We value the risk to life in the context of crime using a discrete choice experiment (CE). The method has clear advantages in that it applies to the whole population and does not require vast data from labor markets, for example. Such data are not always available even in developed economies. Combining the stated preference approach with contingent valuation (CV), CE offer advantages yet to be explored in the context of crime. We demonstrate the application in a developing economy, where similar valuations are not available. The best estimate obtained for Argentina is an average of 1.5 million in 2015 US dollars per statistical life with a confidence interval ($1.1–$2.3). This result is consistent with estimates for the developed world, after appropriate transfer. We also analyze demographic factors in the risk to life, finding a positive influence of income, risk aversion, previous victimization experience and family size on the value of a statistical life, as well as a negative impact of individualism.

“Uncertain futures” refers to a set of policy problems that possess some combination of the following characteristics: (i) they potentially cause irreversible changes; (ii) they are widespread, so that policy responses may make sense only on a global scale; (iii) network effects are difficult to understand and may amplify (or moderate) consequences; (iv) time horizons are long; and (v) the likelihood of catastrophic outcomes is unknown or even unknowable. These characteristics tend to make uncertain futures intractable to market solutions because property rights are not clearly defined and essential information is unavailable. These same factors also pose challenges for benefit-cost analysis (BCA) and other traditional decision analysis tools. The diverse policy decisions confronting decision-makers today demand “dynamic BCA,” analytic frameworks that incorporate uncertainties and trade-offs across policy areas, recognizing that: perceptions of risks can be uninformed, misinformed, or inaccurate; risk characterization can suffer from ambiguity; and experts’ tendency to focus on one risk at a time may blind policymakers to important trade-offs. Dynamic BCA – which recognizes trade-offs, anticipates the need to learn from experience, and encourages learning – is essential for lowering the likelihoods and mitigating the consequences of uncertain futures while encouraging economic growth, reducing fragility, and increasing resilience.

Managing large-scale, geographically distributed, and long-term risks arising from diverse underlying causes – ranging from poverty to underinvestment in protecting against natural hazards or failures of sociotechnical, economic, and financial systems – poses formidable challenges for any theory of effective social decision-making. Participants may have different and rapidly evolving local information and goals, perceive different opportunities and urgencies for actions, and be differently aware of how their actions affect each other through side effects and externalities. Six decades ago, political economist Charles Lindblom viewed “rational-comprehensive decision-making” as utterly impracticable for such realistically complex situations. Instead, he advocated incremental learning and improvement, or “muddling through,” as both a positive and a normative theory of bureaucratic decision-making when costs and benefits are highly uncertain. But sparse, delayed, uncertain, and incomplete feedback undermines the effectiveness of collective learning while muddling through, even if all participant incentives are aligned; it is no panacea. We consider how recent insights from machine learning – especially, deep multiagent reinforcement learning – formalize aspects of muddling through and suggest principles for improving human organizational decision-making. Deep learning principles adapted for human use can not only help participants in different levels of government or control hierarchies manage some large-scale distributed risks, but also show how rational-comprehensive decision analysis and incremental learning and improvement can be reconciled and synthesized.

Although cyber security has become widely recognized as a serious threat to our modern world, there are new threats to our security that combine cyber with other modes of “attack.” This article explores the increasingly important theme in homeland and national security that future attacks will be multimodal, in particular including both a cyber and a physical component, where the cyber attack is intended to make it easier to succeed in the physical attack, and is not an end in itself. The article describes sample scenarios of combined cyber and physical attacks in two sectors where even just cyber security efforts have lagged behind: sports stadiums and the maritime transportation system. It presents an approach to comparing the risk of a combined cyber followed by physical attack and that of a “traditional” physical attack on the same target. It then analyzes the different stadium and maritime examples from the point of view of this risk assessment approach.

Nuclear war is clearly a global catastrophic risk, but it is not an existential risk as is sometimes carelessly claimed. Unfortunately, the consequence and likelihood components of the risk of nuclear war are both highly uncertain. In particular, for nuclear wars that include targeting of multiple cities, nuclear winter may result in more fatalities across the globe than the better-understood effects of blast, prompt radiation, and fallout. Electromagnetic pulse effects, which could range from minor electrical disturbances to the complete collapse of the electric grid, are similarly highly uncertain. Nuclear war likelihood assessments are largely based on intuition, and they span the spectrum from zero to certainty. Notwithstanding these profound uncertainties, we must manage the risk of nuclear war with the knowledge we have. Benefit-cost analysis and other structured analytic methods applied to evaluate risk mitigation measures must acknowledge that we often do not even know whether many proposed approaches (e.g., reducing nuclear arsenals) will have a net positive or negative effect. Multidisciplinary studies are needed to better understand the consequences and likelihood of nuclear war and the complex relationship between these two components of risk, and to predict both the direction and magnitude of risk mitigation approaches.

Uncertain future risks pose cognitive and analytical challenges to household decision makers. Risks with uncertain probabilities, coupled with potentially severe outcomes pose problems for decision-making and are prone to overreactions. Imprecision in risk estimates generates behavioral distortions such as ambiguity aversion. This article presents new empirical results indicating household overvaluations of uncertain threats posed by several drinking water risks: traces of prescription drugs in drinking water, plastic water bottles with bisphenol-A, and the weed killer atrazine in drinking water. Negative reactions reflect responses to ambiguous risks, but policies driven by these concerns may misallocate regulatory resources due to risk conservatism and “no-regrets” responses.