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"Today's defense resource planners face unprecedented uncertainty. The planning processes currently used to determine what forces and capabilities will be needed to address future threats to our national security and interests may be vulnerable to predictive failure. To manage these risks, a new approach to planning is needed to identify strategies that perform well over a whide range of threat and funding futures and thus are better able to manage surprise. This report describes how robust decision making (RDM) may help address this need. RDM, a quantitative decision support methodology for informing decisions under conditions of deep uncertainty and complexity, has been applied to many policy areas in the last decade. This document provides a proof of concept application of RDM to defense planning, focusing on the air-launched munitions mix challenge. The study embeds a fast-running "weapons on targets" allocation model within a "scenario generator" that explores many thousands of plausible, future 20-year series of military campaigns. The RDM analysis uses these simulation models to stress-test alternative munitions mix strategies against many plausible futures. The analysis then identifies a robust munitions mix strategy, which interestingly depends not only on the desired portfolio of alternative weapons types but also on the rules used to replenish depleted weapons stocks after each campaign. The study also suggests how RDM might best be integrated into current DoD planning processes and some of the challenges that might be involved." --Back cover

Abstract:Benefit-cost analysis (BCA) aims to help people make better decisions. But BCA does not always serve this role as well as intended. In particular, BCA's aim of aggregating all attributes of concern to decision makers into a single, best-estimate metric can conflict with the differing world views and values that may be an inherent characteristic of many climate-related decisions. This paper argues that new approaches exist that can help reduce the tension between the benefits of providing useful, scientifically based information to decision makers and the costs of aggregating uncertainty and differing values into single best estimates. Enabled by new information technology, these approaches can summarize decision-relevant information in new ways. Viewed in this light, many limitations of BCA lie not in the approach itself, but with the way it is used. In particular, this paper will argue that the problem lies in a process that begins by first assigning agreed-upon values to all the relevant inputs and then using BCA to rank the desirability of alternative decision options. In contrast, BCA can be used as part of a process that begins by acknowledging a wide range of ethical and epistemological views, examines which combinations of views are most important in affecting the ranking among proposed decision options, and uses this information to identify and seek consensus on actions that are robust over a wide range of such views.

AbstractMany of today's most pressing policy challenges are usefully characterized as wicked problems. With contested framings parties to a decision disagree not only on potential solutions, but on the nature of the problem they are trying to solve. The quantitative tools of risk and policy analysis, commonly designed to develop and compare choices within a single decision framing, are poorly designed to bring quantitative information into debates with contested framings. This study aims to build on recent advances in decision making under deep uncertainty (DMDU) to demonstrate methods and tools that may help resolve the tension between quantitative decision support and multiworldview approaches for addressing wicked problems. The study employs robust decision making (RDM), one common DMDU method, and a new version of the lake model, a simple and widely used model of a coupled human and natural system, to conduct a stylized analysis that reflects three different worldviews. The RDM analysis solves the decision challenge independently for each worldview and then compares each set of solutions from the vantage of the other worldviews. The resulting utopia–dystopia matrix informs problem reframing that seeks robust, adaptive strategies independently consistent with each worldview and thus provides a locus for agreement. The study describes how stakeholder engagements might use such analytic tools and their information products to provide overlapping but alternative entry points for groups with fundamentally different worldviews to engage with each other in deliberative processes appropriate for wicked problems.

Many governments use technology incentives as an important component of their greenhouse gas abatement strategies. These "carrots" are intended to encourage the initial diffusion of new, greenhouse-gas-emissions-reducing technologies, in contrast to carbon taxes and emissions trading which provide a "stick" designed to reduce emissions by increasing the price of high-emitting technologies for all users. Technology incentives appear attractive, but their record in practice is mixed and economic theory suggests that in the absence of market failures, they are inefficient compared to taxes and trading. This study uses an agent-based model of technology diffusion and exploratory modeling, a new technique for decision-making under conditions of extreme uncertainty, to examine the conditions under which technology incentives should be a key building block of robust climate change policies. We find that a combined strategy of carbon taxes and technology incentives, as opposed to carbon taxes alone, is the best approach to greenhouse gas emissions reductions if the social benefits of early adoption sufficiently exceed the private benefits. Such social benefits can occur when economic actors have a wide variety of cost/performance preferences for new technologies and either new technologies have increasing returns to scale or potential adopters can reduce their uncertainty about the performance of new technologies by querying the experience of other adopters. We find that if decision-makers hold even modest expectations that such social benefits are significant or that the impacts of climate change will turn out to be serious then technology incentive programs may be a promising hedge against the threat of climate change.

Many commentators have suggested the need for new decision analysis approaches to better manage systems with deeply uncertain, poorly characterized risks. Most notably, policy challenges such as abrupt climate change involve potential nonlinear or threshold responses where both the triggering level and subsequent system response are poorly understood. This study uses a simple computer simulation model to compare several alternative frameworks for decision making under uncertainty—optimal expected utility, the precautionary principle, and three different approaches to robust decision making—for addressing the challenge of adding pollution to a lake without triggering unwanted and potentially irreversible eutrophication. The three robust decision approaches—trading some optimal performance for less sensitivity to assumptions, satisficing over a wide range of futures, and keeping options open—are found to identify similar strategies as the most robust choice. This study also suggests that these robust decision approaches offer a quantitative, decision analytic framework that captures the spirit of the precautionary principle while addressing some of its shortcomings. Finally, this study finds that robust strategies may be preferable to optimum strategies when the uncertainty is sufficiently deep and the set of alternative policy options is sufficiently rich.

The checkered history of predicting the future-e.g., "Man will never fly"-has dissuaded policymakers from considering the long-term effects of decisions. New analytic methods, enabled by modern computers, transform our ability to reason about the future. The authors here demonstrate a quantitative approach to long-term policy analysis (LTPA). Robust methods enable decisionmakers to examine a vast range of futures and design adaptive strategies to be robust across them. Using sustainable development as an example, the authors discuss how these methods apply to LTPA and a wide range of decis

This study compares two widely used approaches for robustness analysis of decision problems: the info‐gap method originally developed by Ben‐Haim and the robust decision making (RDM) approach originally developed by Lempert, Popper, and Bankes. The study uses each approach to evaluate alternative paths for climate‐altering greenhouse gas emissions given the potential for nonlinear threshold responses in the climate system, significant uncertainty about such a threshold response and a variety of other key parameters, as well as the ability to learn about any threshold responses over time. Info‐gap and RDM share many similarities. Both represent uncertainty as sets of multiple plausible futures, and both seek to identify robust strategies whose performance is insensitive to uncertainties. Yet they also exhibit important differences, as they arrange their analyses in different orders, treat losses and gains in different ways, and take different approaches to imprecise probabilistic information. The study finds that the two approaches reach similar but not identical policy recommendations and that their differing attributes raise important questions about their appropriate roles in decision support applications. The comparison not only improves understanding of these specific methods, it also suggests some broader insights into robustness approaches and a framework for comparing them.

AbstractIndividuals use values to frame their beliefs and simplify their understanding when confronted with complex and uncertain situations. The high complexity and deep uncertainty involved in climate risk management (CRM) lead to individuals' values likely being coupled to and contributing to their understanding of specific climate risk factors and management strategies. Most mental model approaches, however, which are commonly used to inform our understanding of people's beliefs, ignore values. In response, we developed a "Values‐informed Mental Model" research approach, or ViMM, to elicit individuals' values alongside their beliefs and determine which values people use to understand and assess specific climate risk factors and CRM strategies. Our results show that participants consistently used one of three values to frame their understanding of risk factors and CRM strategies in New Orleans: (1) fostering a healthy economy, wealth, and job creation, (2) protecting and promoting healthy ecosystems and biodiversity, and (3) preserving New Orleans' unique culture, traditions, and historically significant neighborhoods. While the first value frame is common in analyses of CRM strategies, the latter two are often ignored, despite their mirroring commonly accepted pillars of sustainability. Other values like distributive justice and fairness were prioritized differently depending on the risk factor or strategy being discussed. These results suggest that the ViMM method could be a critical first step in CRM decision‐support processes and may encourage adoption of CRM strategies more in line with stakeholders' values.

Concerned that the unavailability of terrorism insurance would impede economic recovery and hinder growth after the 9/11 attacks, Congress passed the Terrorism Risk Insurance Act of 2002 (TRIA). TRIA will sunset at the end of 2007 unless Congress takes further action. This book examines the implications of allowing TRIA to expire and of enhancements aimed at improving the availability and affordability of insurance for nuclear, biological, chemical, and radiological (NBCR) attacks. The analysis takes a systematic approach to addressing the deep uncertainties that underlie the market for terror

Not too long ago industry almost monolithically viewed environmental considerations as an expense--sometimes as a nuisance. That is beginning to change. Industry leaders now see the advantage of going beyond complying with environmental regulations, as had been their wont, and incorporating the search for environmentally sound business practices into their research and development. This report features case studies of four industry leaders--DuPont, Intel, Monsanto, and Xerox--and discusses how these companies view environmental technology investments. The information is based on a series of interviews with senior R & D managers and environment, health, and safety personnel along with the relevant literature. At the request of the Office of Science and Technology Policy in the Executive Office of the President, the authors have provided the views of a subset of one of the key stakeholders in environmental policy, industry, which have heretofore not received a lot of emphasis. The case studies and the findings are summarized here