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The article discusses the creation of a new, individualized hazard warning system by the town of Sitka, Alaska in tandem with scientists after the occurrence of the fatal 2015 landslides as of October 2021. Topics covered include a timeline in the development of a warning system from 2015 to its implementation in September 2021, and various stakeholders' views on the evolution of a solution to the challenge. Also noted is the revelation of complexities in the process of coproducing knowledge.

Real-world experience underscores the complexity of interactions among multiple drivers of climate change risk and of how multiple risks compound or cascade. However, a holistic framework for assessing such complex climate change risks has not yet been achieved. Clarity is needed regarding the interactions that generate risk, including the role of adaptation and mitigation responses. In this perspective, we present a framework for three categories of increasingly complex climate change risk that focus on interactions among the multiple drivers of risk, as well as among multiple risks. A significant innovation is recognizing that risks can arise both from potential impacts due to climate change and from responses to climate change. This approach encourages thinking that traverses sectoral and regional boundaries and links physical and socio-economic drivers of risk. Advancing climate change risk assessment in these ways is essential for more informed decision making that reduces negative climate change impacts.

AbstractThe Belt and Road Initiative (BRI), a global infrastructure and connectivity plan initiated by China, has undergone significant changes in its policy implementation environment since it was first proposed in 2013. Faced with escalating tensions between the US and China and a sweeping pandemic, many assumptions upon which the original BRI policy framework was formulated no longer stand. As with other types of mega-scale infrastructure programme, BRI outcomes depend on future changes in the system that are hard to predict as well as opposing preferences of stakeholders that are difficult to balance. This situation may be conceptualized as ‘deep uncertainty’ in long-term decision making. Based on our initial analysis of BRI literature, we suggest incorporating deep uncertainty in BRI long-term decision making. At the Initiative level, to better cope with deep uncertainties, this implies that that decision makers need systematic, risk-based, and quantitative policy analysis tools to inform any adjustments in BRI development strategies and efforts to make BRI more resilient to potential future shocks. At the project level, it means that participating countries and a wide array of stakeholders may need such instruments to assess the feasibility and risks of specific projects. Decision Making Under Deep Uncertainty (DMDU) methods offer a set of useful and effective policy analysis tools to support tough and costly decisions that involve long-term planning, as exemplified by BRI megaproject investment. In this paper, we describe the deep uncertainties affecting BRI decision making, and from a preliminary analysis identify drawbacks of existing BRI policy analysis in light of its future challenges, use examples to illustrate how DMDU methods may potentially shape BRI decisions differently compared with current approaches, and tentatively discuss how scientists and policymakers can apply DMDU methods to inform and support BRI-related decision making that help enhance Initiative and project efficacy and efficiency.

The article discusses how to deal with the world's long-term problems, such as climate change. In 2004 a panel of experts known as the Copenhagen Consensus ranked the world's most pressing environmental, health and social problems in a prioritized list. Usually each of these problems is treated in isolation, as though humanity had the luxury of dealing with its problems one by one. The nation's and the world's long-term threats often get ignored altogether or are even made worse by shortsighted decisions. We have constructed rigorous, systematic methods for dealing with deep uncertainty. Striking a balance between the economy and the environment is one leading example of the difficulty in using science to inform long-term decisions. Analysts have turned to techniques such as scenario planning that involve exploring different possible futures rather than gambling on a single prediction. The European Union often favors the "precautionary principle"--in essence, basing policy on the most hazardous plausible scenarios. INSET: Overview/Dealing with Uncertainty.

Robust decision making (RDM) is a framework for making decisions with a large number of highly imperfect forecasts of the future. Rather than relying on improved point forecasts or probabilistic [...]

Climate change generates multifaceted and difficult-to-measure risks to human and natural systems. Now, research offers a composite indicator of global climate risk that may help track progress in addressing climate change.

AbstractBenefit-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.

We report results of a series of experiments on decision-making in the presence of irreducibly imprecise probabilities of negative and undesirable outcomes. Subjects faced decisions among actions where the payoffs depend on the probability of drawing balls from an urn whose composition was not fully known. Consistent with the vagueness avoidance hypothesis, Decision Makers (DMs) displayed systematic preferences for safe actions even at a high premium. This tendency grew with increased vagueness, characterized by the width of the interval of plausible probabilities. We compared two decision aids that portray these imprecise probabilities in different ways: one aid calculates the expected value of alternative actions contingent on probability distributions provided by the DMs, and the other displays graphically the distribution of the conditional expected value of the actions over the entire range of plausible probabilities. Access to either decision aid reduced vagueness avoidance and the type of aid used systematically influenced the way DMs approached the problem. We compared the DMs’ choices with predictions of decision models for decision under ignorance and under risk. We found support for the conservative maxi–min criterion, but a subjective expected value model with probabilities inferred from the partial information available also performed well, especially for low levels of vagueness and in the presence of decision aids. These findings suggest some initial implications for the debate over how to best characterize imprecise probabilistic information for policy-makers when decisions involve irreducible uncertainties, such as climate change.

In this paper, we review the need for, use of, and demands on climate modeling to support so‐called ‘robust’ decision frameworks, in the context of improving the contribution of climate information to effective decision making. Such frameworks seek to identify policy vulnerabilities under deep uncertainty about the future and propose strategies for minimizing regret in the event of broken assumptions. We argue that currently there is a severe underutilization of climate models as tools for supporting decision making, and that this is slowing progress in developing informed adaptation and mitigation responses to climate change. This underutilization stems from two root causes, about which there is a growing body of literature: one, a widespread, but limiting, conception that the usefulness of climate models in planning begins and ends with regional‐scale predictions of multidecadal climate change; two, the general failure so far to incorporate learning from the decision and social sciences into climate‐related decision support in key sectors. We further argue that addressing these root causes will require expanding the conception of climate models; not simply as prediction machines within ‘predict‐then‐act’ decision frameworks, but as scenario generators, sources of insight into complex system behavior, and aids to critical thinking within robust decision frameworks. Such a shift, however, would have implications for how users perceive and use information from climate models and, ultimately, the types of information they will demand from these models—and thus for the types of simulations and numerical experiments that will have the most value for informing decision making. WIREs Clim Change2013, 4:39–60. doi: 10.1002/wcc.202

Comments on the article by Alexander E. MacDonald about climate change predictions which appeared in the Summer 2001 issue of 'Issues in Science and Technology.' Suggestion on the use of paleoclimate data to understand trends in climate changes; Role of the ocean in climatic change; Need for the creation of a national climatic change policy.

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.

An essay is presented on the challenges related with adapting to climate change under pervasive uncertain conditions, highlighting water resources planning, management, and development. It states that climate change adaptation is crucial for water resources management and that the research community is plagued with conducting interdisciplinary studies that tackles adaptation in complicated systems. It notes that decision makers rely on the research community for obtaining technical information.