Robust Climate Policies Under Uncertainty: A Comparison of Robust Decision Making and Info‐Gap Methods

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.     

Scenario Discovery with Multiple Criteria: An Evaluation of the Robust Decision‐Making Framework for Climate Change Adaptation

There is increasing concern over deep uncertainty in the risk analysis field as probabilistic models of uncertainty cannot always be confidently determined or agreed upon for many of our most pressing contemporary risk challenges. This is particularly true in the climate change adaptation field, and has prompted the development of a number of frameworks aiming to characterize system vulnerabilities and identify robust alternatives. One such methodology is robust decision making (RDM), which uses simulation models to assess how strategies perform over many plausible conditions and then identifies and characterizes those where the strategy fails in a process termed scenario discovery. While many of the problems to which RDM has been applied are characterized by multiple objectives, research to date has provided little insight into how treatment of multiple criteria impacts the failure scenarios identified. In this research, we compare different methods for incorporating multiple objectives into the scenario discovery process to evaluate how they impact the resulting failure scenarios. We use the Lake Tana basin in Ethiopia as a case study, where climatic and environmental uncertainties could impact multiple planned water infrastructure projects, and find that failure scenarios may vary depending on the method used to aggregate multiple criteria. Common methods used to convert multiple attributes into a single utility score can obscure connections between failure scenarios and system performance, limiting the information provided to support decision making. Applying scenario discovery over each performance metric separately provides more nuanced information regarding the relative sensitivity of the objectives to different uncertain parameters, leading to clearer insights on measures that could be taken to improve system robustness and areas where additional research might prove useful.     

Impact of Uncertainty Parameter Distribution on Robust Decision Making Outcomes for Climate Change Adaptation under Deep Uncertainty

Deep uncertainty in future climatic and economic conditions complicates developing infrastructure designed to last several generations, such as water reservoirs. In response, analysts have developed multiple robust decision frameworks to help identify investments and policies that can withstand a wide range of future states. Although these frameworks are adept at supporting decisions where uncertainty cannot be represented probabilistically, analysts necessarily choose probabilistic bounds and distributions for uncertain variables to support exploratory modeling. The implications of these assumptions on the analytical outcomes of robust decision frameworks are rarely evaluated, and little guidance exists in terms of how to select uncertain variable distributions. Here, we evaluate the impact of these choices by following the robust decision-making procedure, using four different assumptions about the probabilistic distribution of exogenous uncertainties in future climatic and economic states. We take a water reservoir system in Ethiopia as our case study, and sample climatic parameters from uniform, normal, extended uniform, and extended normal distributions; we similarly sample two economic parameters. We compute regret and satisficing robustness decision criteria for two performance measures, agricultural water demand coverage and net present value, and perform scenario discovery on the most robust reservoir alternative. We find lower robustness scores resulting from extended parameter distributions and demonstrate that parameter distributions can impact vulnerabilities identified through scenario discovery. Our results suggest that exploratory modeling within robust decision frameworks should sample from extended, uniform parameters distributions.     

Adaptive Governance,Uncertainty, and Risk: Policy Framing and Responses to Climate Change,Drought, and Flood

As climate change impacts result in more extreme events (such as droughts and floods), the need to understand which policies facilitate effective climate change adaptation becomes crucial. Hence, this article answers the question: How do governments and policymakers frame policy in relation to climate change, droughts, and floods and what governance structures facilitate adaptation? This research interrogates and analyzes through content analysis, supplemented by semi‐structured qualitative interviews, the policy response to climate change, drought, and flood in relation to agricultural producers in four case studies in river basins in Chile, Argentina, and Canada. First, an epistemological explanation of risk and uncertainty underscores a brief literature review of adaptive governance, followed by policy framing in relation to risk and uncertainty, and an analytical model is developed. Pertinent findings of the four cases are recounted, followed by a comparative analysis. In conclusion, recommendations are made to improve policies and expand adaptive governance to better account for uncertainty and risk. This article is innovative in that it proposes an expanded model of adaptive governance in relation to “risk” that can help bridge the barrier of uncertainty in science and policy.     

Confronting Deep Uncertainties in Risk Analysis

How can risk analysts help to improve policy and decision making when the correct probabilistic relation between alternative acts and their probable consequences is unknown? This practical challenge of risk management with model uncertainty arises in problems from preparing for climate change to managing emerging diseases to operating complex and hazardous facilities safely. We review constructive methods for robust and adaptive risk analysis under deep uncertainty. These methods are not yet as familiar to many risk analysts as older statistical and model‐based methods, such as the paradigm of identifying a single “best‐fitting” model and performing sensitivity analyses for its conclusions. They provide genuine breakthroughs for improving predictions and decisions when the correct model is highly uncertain. We demonstrate their potential by summarizing a variety of practical risk management applications.     

Electric-Utility Resource Planning and Decision-Making: The Importance of Uncertainty

Uncertainty is a critical factor that pervades all aspects of electric-utility planning. Uncertainties about future load growth, about the continued performance of existing supply and demand resources, and about the costs, construction times, and operations of new resources greatly complicate utility resource acquisition. This paper discusses the factors that lead to uncertainty, reviews the methods that utilities use in planning and in acquiring resources, and suggests future research to help deal with these uncertainties. This review is based on assessments of the long-term resource plans prepared by 10 utilities and one Public Service Commission, telephone interviews with staff at these 11 organizations and with staff at three consulting firms, and reviews of many other related publications.     

The Challenge of Degraded Environments: How Common Biases Impair Effective Policy

Economic activity can damage natural systems and reduce the flow of ecosystem services. The harms can be substantial, as our case studies vividly illustrate. Most degraded landscapes have at least some potential to be reclaimed. However, uncertainty plagues decision making regarding degradation and reclamation, in relation to the extent of the damage, the success of reclamation, and how exposure will change in the future. We examine how a range of observed decision biases can lead to far‐from‐optimal policies regarding how much degradation to allow and when, as well as how and how much, to reclaim degraded sites. Despite our focus on degraded landscapes, we believe these are generic biases present in a wide range of risk situations. Our three case studies show these biases at work. The first two studies are of mining operations in the United States and Canada, and the third is of climate change.     

Farmers’ Risk‐Based Decision Making Under Pervasive Uncertainty: Cognitive Thresholds and Hazy Hedging

Researchers in judgment and decision making have long debunked the idea that we are economically rational optimizers. However, problematic assumptions of rationality remain common in studies of agricultural economics and climate change adaptation, especially those that involve quantitative models. Recent movement toward more complex agent‐based modeling provides an opportunity to reconsider the empirical basis for farmer decision making. Here, we reconceptualize farmer decision making from the ground up, using an in situ mental models approach to analyze weather and climate risk management. We assess how large‐scale commercial grain farmers in South Africa (n = 90) coordinate decisions about weather, climate variability, and climate change with those around other environmental, agronomic, economic, political, and personal risks that they manage every day. Contrary to common simplifying assumptions, we show that these farmers tend to satisfice rather than optimize as they face intractable and multifaceted uncertainty; they make imperfect use of limited information; they are differently averse to different risks; they make decisions on multiple time horizons; they are cautious in responding to changing conditions; and their diverse risk perceptions contribute to important differences in individual behaviors. We find that they use two important nonoptimizing strategies, which we call cognitive thresholds and hazy hedging, to make practical decisions under pervasive uncertainty. These strategies, evident in farmers' simultaneous use of conservation agriculture and livestock to manage weather risks, are the messy in situ performance of naturalistic decision‐making techniques. These results may inform continued research on such behavioral tendencies in narrower lab‐ and modeling‐based studies.     

Uncertain Risk Assessment and Management: Case Studies of the Application of the Precautionary Principle in Portugal

This study intends to clarify how the precautionary principle (PP) has been interpreted and applied by the courts in Portugal in the analysis of conflicts associated with uncertain and serious potential risks to human health and the environment. It also aims to contribute to the debate of when and how to apply precautionary measures. To this end, recent court cases in the areas of waste incineration, high-voltage power lines, as well as dam and wind farm construction were considered. The degree of consistency in the courts’ decisions and their reasons in the different judicial bodies was analyzed with the support of a theoretical framework based on three attributes: the level of seriousness of potential hazards, level of evidence required, and the severity of precautionary actions taken. Different positions among courts were observed, with contradictory arguments in the same case or in similar cases. A greater propensity for favorable decisions in the acceptance of restraining orders was verified in the courts of lower instances, where human health could be threatened. However, the decisions of the Supreme Administrative Court, which were always unfavorable to the restraining orders, seem to reflect the priority given to national economic and political interests over local or regional environmental interests. They may also reflect the Supreme Court's reluctancy to apply the PP in the absence of a firm legally binding PP in national legislation. To address this situation, more explicit legal requirements and criteria for the analysis of uncertain risks and the weighting of interests by area of activity are needed.     

Decision Making for Risk Management: A Comparison of Graphical Methods for Presenting Quantitative Uncertainty

Previous research has shown that people err when making decisions aided by probability information. Surprisingly, there has been little exploration into the accuracy of decisions made based on many commonly used probabilistic display methods. Two experiments examined the ability of a comprehensive set of such methods to effectively communicate critical information to a decision maker and influence confidence in decision making. The second experiment investigated the performance of these methods under time pressure, a situational factor known to exacerbate judgmental errors. Ten commonly used graphical display methods were randomly assigned to participants. Across eight scenarios in which a probabilistic outcome was described, participants were asked questions regarding graph interpretation (e.g., mean) and made behavioral choices (i.e., act; do not act) based on the provided information indicated that decision‐maker accuracy differed by graphical method; error bars and boxplots led to greatest mean estimation and behavioral choice accuracy whereas complementary cumulative probability distribution functions were associated with the highest probability estimation accuracy. Under time pressure, participant performance decreased when making behavioral choices.     

Risk Analysis, Life Cycle Assessment—The Common Challenge of Dealing with the Precautionary Frame (Based on the Toxicity Controversy in Sweden and the Netherlands)

Life cycle impact assessment (LCIA) and comparative risk assessment (RA) use the same building blocks for analyzing fate and potential effects of toxic substances. It is tacitly assumed that emission-effect calculations can give uniform and decisive answers in debates on toxicity problems. For several decades, mainstream policy sciences have taken a different starting point when analyzing decision making on complex, controversial societal issues. Such controversies in essence are thought to be caused by the fact that different actor coalitions adhere to a different, but in scientific terms equally reasonable, conceptualization or "framing" of the problem. A historical, argumentative analysis of the Dutch chlorine debate and the Swedish PVC debate shows that this is also true in the discussions on toxic substances. Three frames have been identified, which were coined the "risk assessment frame," "the strict control frame," and the "precautionary frame." These frames tacitly disagree about the extent of knowledge/ignorance about the impacts of substances, the robustness/fallibility of emission-reduction schemes, and the robustness/vulnerability of nature. The latter frame, adhered to by environmentalists, seeks to judge substances mainly on their inherent safety. Under the current institutional arrangements and practices, RA and LCIA are executed mainly in line with the philosophy expressed by the risk assessment frame. This article gives various suggestions for dealing with framing in debates on toxic substances. One of the options is elaborated in somewhat more detail, i.e., the development of multiple indicators and calculation schemes for RA and LCIA that reflect the different frames. An outline is given for a possible indicator system reflecting the precautionary principle.     

Deep Uncertainties in Sea-Level Rise and Storm Surge Projections: Implications for Coastal Flood Risk Management

Sea levels are rising in many areas around the world, posing risks to coastal communities and infrastructures. Strategies for managing these flood risks present decision challenges that require a combination of geophysical, economic, and infrastructure models. Previous studies have broken important new ground on the considerable tensions between the costs of upgrading infrastructure and the damages that could result from extreme flood events. However, many risk-based adaptation strategies remain silent on certain potentially important uncertainties, as well as the tradeoffs between competing objectives. Here, we implement and improve on a classic decision-analytical model (Van Dantzig 1956) to: (i) capture tradeoffs across conflicting stakeholder objectives, (ii) demonstrate the consequences of structural uncertainties in the sea-level rise and storm surge models, and (iii) identify the parametric uncertainties that most strongly influence each objective using global sensitivity analysis. We find that the flood adaptation model produces potentially myopic solutions when formulated using traditional mean-centric decision theory. Moving from a single-objective problem formulation to one with multiobjective tradeoffs dramatically expands the decision space, and highlights the need for compromise solutions to address stakeholder preferences. We find deep structural uncertainties that have large effects on the model outcome, with the storm surge parameters accounting for the greatest impacts. Global sensitivity analysis effectively identifies important parameter interactions that local methods overlook, and that could have critical implications for flood adaptation strategies.     

Measuring Uncertainty Importance: Investigation and Comparison of Alternative Approaches

Uncertainty importance measures are quantitative tools aiming at identifying the contribution of uncertain inputs to output uncertainty. Their application ranges from food safety (Frey & Patil (2002)) to hurricane losses (Iman et al. (2005a, 2005b)). Results and indications an analyst derives depend on the method selected for the study. In this work, we investigate the assumptions at the basis of various indicator families to discuss the information they convey to the analyst/decisionmaker. We start with nonparametric techniques, and then present variance-based methods. By means of an example we show that output variance does not always reflect a decisionmaker state of knowledge of the inputs. We then examine the use of moment-independent approaches to global sensitivity analysis, i.e., techniques that look at the entire output distribution without a specific reference to its moments. Numerical results demonstrate that both moment-independent and variance-based indicators agree in identifying noninfluential parameters. However, differences in the ranking of the most relevant factors show that inputs that influence variance the most are not necessarily the ones that influence the output uncertainty distribution the most.     

Concerns,Challenges, and Directions of Development for the Issue of Representing Uncertainty in Risk Assessment

In the analysis of the risk associated to rare events that may lead to catastrophic consequences with large uncertainty, it is questionable that the knowledge and information available for the analysis can be reflected properly by probabilities. Approaches other than purely probabilistic have been suggested, for example, using interval probabilities, possibilistic measures, or qualitative methods. In this article, we look into the problem and identify a number of issues that are foundational for its treatment. The foundational issues addressed reflect on the position that “probability is perfect” and take into open consideration the need for an extended framework for risk assessment that reflects the separation that practically exists between analyst and decisionmaker.     

A Survey of Approaches for Assessing and Managing the Risk of Extremes

In this paper, we review methods for assessing and managing the risk of extreme events, where extreme events are defined to be rare, severe, and outside the normal range of experience of the system in question. First, we discuss several systematic approaches for identifying possible extreme events. We then discuss some issues related to risk assessment of extreme events, including what type of output is needed (e.g., a single probability vs. a probability distribution), and alternatives to the probabilistic approach. Next, we present a number of probabilistic methods. These include: guidelines for eliciting informative probability distributions from experts; maximum entropy distributions; extreme value theory; other approaches for constructing prior distributions (such as reference or noninformative priors); the use of modeling and decomposition to estimate the probability (or distribution) of interest; and bounding methods. Finally, we briefly discuss several approaches for managing the risk of extreme events, and conclude with recommendations and directions for future research.     

Confluence and Contours: Reflexive Management of Environmental Risk

Government institutions have responsibilities to distribute risk management funds meaningfully and to be accountable for their choices. We took a macro‐level sociological approach to understanding the role of government in managing environmental risks, and insights from micro‐level psychology to examine individual‐level risk‐related perceptions and beliefs. Survey data from 2,068 U.K. citizens showed that lay people's funding preferences were associated positively with beliefs about responsibility and trust, yet associations with perception varied depending on risk type. Moreover, there were risk‐specific differences in the funding preferences of the lay sample and 29 policymakers. A laboratory‐based study of 109 participants examined funding allocation in more detail through iterative presentation of expert information. Quantitative and qualitative data revealed a meso‐level framework comprising three types of decisionmakers who varied in their willingness to change funding allocation preferences following expert information: adaptors, responders, and resistors. This research highlights the relevance of integrated theoretical approaches to understanding the policy process, and the benefits of reflexive dialogue to managing environmental risks.     

Measuring feelings about choices and risks: The Berlin Emotional Responses to Risk Instrument (BERRI)

We introduce a brief instrument specifically validated for measuring positive and negative feelings about risks—the Berlin Emotional Responses to Risk Instrument (BERRI). Based on seven studies involving diverse adults from three countries (n = 2120), the BERRI was found to robustly estimate anticipatory affective reactions derived from subjective evaluations of positive (i.e., assured, hopeful, and relieved) and negative emotions (i.e., anxious, afraid, and worried). The brief BERRI outperformed a 14-item assessment, uniquely tracking costs/benefits associated with cancer screening among men and women (Studies 1 and 2). Predictive validity was further documented in paradigmatic risky choice studies wherein options varied over probabilities and severities across six contexts (health, social, financial, technological, ethical, and environmental; Study 3). Studies 4–6, conducted during the Ebola epidemic and COVID-19 pandemic, indicated BERRI responses were sensitive to subtle effects caused by emotion-related framing manipulations presented in different cultures and languages (the United States, Spain, and Poland). Study 7 indicated BERRI responses remained stable for 2 weeks. Although the BERRI can provide an estimate of overall affect, choices were generally better explained by the unique influences of positive and negative affect. Overall, results suggest the novel, brief instrument can be an efficient tool for high-stakes research on decision making and risk communication.     

Science for Policy: A Case Study of Scientific Polarization,Values, and the Framing of Risk and Uncertainty

It is well documented that more research can lead to hardened positions, particularly when dealing with complex, controversial, and value‐laden issues. This study is an attempt to unveil underlying values in a contemporary debate, where both sides use scientific evidence to support their argument. We analyze the problem framing, vocabulary, interpretation of evidence, and policy recommendations, with particular attention to the framing of nature and technology. We find clear differences between the two arguments. One side stress that there is no evidence that the present approach is causing harm to humans or the environment, does not ruminate on uncertainties to that end, references nature's ability to handle the problem, and indicates distrust in technological solutions. In contrast, the other side focuses on uncertainties, particularly the lack of knowledge about potential environmental effects and signals trust in technological development and human intervention as the solution. Our study suggests that the two sides’ diverging interpretations are tied to their perception of nature: vulnerable to human activities versus robust and able to handle human impacts. The two sides also seem to hold diverging views of technology, but there are indications that this might be rooted in their perception of governance and economy rather than about technology per se. We conclude that there is a need to further investigate how scientific arguments are related to worldviews, to see how (if at all) worldview typologies can help us to understand how value‐based judgments are embedded in science advice, and the impact these have on policy preferences.     

An Evaluation of the Treatment of Risk and Uncertainties in the IPCC Reports on Climate Change

Few global threats rival global climate change in scale and potential consequence. The principal international authority assessing climate risk is the Intergovernmental Panel on Climate Change (IPCC). Through repeated assessments the IPCC has devoted considerable effort and interdisciplinary competence to articulating a common characterization of climate risk and uncertainties. We have reviewed the assessment and its foundation for the Fifth Assessment Reports published in 2013 and 2014, in particular the guidance note for lead authors of the fifth IPCC assessment report on consistent treatment of uncertainties. Our analysis shows that the work carried out by the ICPP is short of providing a theoretically and conceptually convincing foundation on the treatment of risk and uncertainties. The main reasons for our assessment are: (i) the concept of risk is given a too narrow definition (a function of consequences and probability/likelihood); and (ii) the reports lack precision in delineating their concepts and methods. The goal of this article is to contribute to improving the handling of uncertainty and risk in future IPCC studies, thereby obtaining a more theoretically substantiated characterization as well as enhanced scientific quality for risk analysis in this area. Several suggestions for how to improve the risk and uncertainty treatment are provided.     

How Much Uncertainty is Too Much and How Do We Know? A Case Example of the Assessment of Ozone Monitor Network Options

Limited time and resources usually characterize environmental decision making at policy organizations such as the U.S. Environmental Protection Agency. In these climates, addressing uncertainty, usually considered a flaw in scientific analyses, is often avoided. However, ignoring uncertainties can result in unpleasant policy surprises. Furthermore, it is important for decisionmakers to know how defensible a chosen policy option is over other options when the uncertainties of the data are considered. The purpose of this article is to suggest an approach that is unique from other approaches in that it considers uncertainty in two specific ways-the uncertainty of stakeholder values within a particular decision context and data uncertainty in the light of the decision-contextual data-values relationship. It is the premise of this article that the interaction between data and stakeholder values is critical to how the decision options are viewed and determines the effect of data uncertainty on the relative acceptability of the decision options, making the understanding of this interaction important to decisionmakers and other stakeholders. This approach utilizes the recently developed decision analysis framework and process, multi-criteria integrated resource assessment (MIRA). This article will specifically address how MIRA can be used to help decisionmakers better understand the importance of uncertainty on the specific (i.e., decision contextual) environmental policy options that they are deliberating.