Flash Flood Risks and Warning Decisions: A Mental Models Study of Forecasters,Public Officials,and Media Broadcasters in Boulder,Colorado

Timely warning communication and decision making are critical for reducing harm from flash flooding. To help understand and improve extreme weather risk communication and management, this study uses a mental models research approach to investigate the flash flood warning system and its risk decision context. Data were collected in the Boulder, Colorado area from mental models interviews with forecasters, public officials, and media broadcasters, who each make important interacting decisions in the warning system, and from a group modeling session with forecasters. Analysis of the data informed development of a decision‐focused model of the flash flood warning system that integrates the professionals’ perspectives. Comparative analysis of individual and group data with this model characterizes how these professionals conceptualize flash flood risks and associated uncertainty; create and disseminate flash flood warning information; and perceive how warning information is (and should be) used in their own and others’ decisions. The analysis indicates that warning system functioning would benefit from professionals developing a clearer, shared understanding of flash flood risks and the warning system, across their areas of expertise and job roles. Given the challenges in risk communication and decision making for complex, rapidly evolving hazards such as flash floods, another priority is development of improved warning content to help members of the public protect themselves when needed. Also important is professional communication with members of the public about allocation of responsibilities for managing flash flood risks, as well as improved system‐wide management of uncertainty in decisions.     

Managing the Risk of Uncertain Threshold Responses: Comparison of Robust, Optimum, and Precautionary Approaches

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.     

Robustness of Risk Maps and Survey Networks to Knowledge Gaps About a New Invasive Pest

In pest risk assessment it is frequently necessary to make management decisions regarding emerging threats under severe uncertainty. Although risk maps provide useful decision support for invasive alien species, they rarely address knowledge gaps associated with the underlying risk model or how they may change the risk estimates. Failure to recognize uncertainty leads to risk‐ignorant decisions and miscalculation of expected impacts as well as the costs required to minimize these impacts. Here we use the information gap concept to evaluate the robustness of risk maps to uncertainties in key assumptions about an invading organism. We generate risk maps with a spatial model of invasion that simulates potential entries of an invasive pest via international marine shipments, their spread through a landscape, and establishment on a susceptible host. In particular, we focus on the question of how much uncertainty in risk model assumptions can be tolerated before the risk map loses its value. We outline this approach with an example of a forest pest recently detected in North America, Sirex noctilio Fabricius. The results provide a spatial representation of the robustness of predictions of S. noctilio invasion risk to uncertainty and show major geographic hotspots where the consideration of uncertainty in model parameters may change management decisions about a new invasive pest. We then illustrate how the dependency between the extent of uncertainties and the degree of robustness of a risk map can be used to select a surveillance network design that is most robust to knowledge gaps about the pest.     

The Economic Value of Market Information for Farmers in Developing Economies

In developing countries, farmers lack information for making informed production, manufacturing/selling decisions to improve their earnings. To alleviate poverty, various non‐governmental organizations (NGOs) and for‐profit companies have developed different ways to distribute information about market price, crop advisory and farming technique to farmers. We investigate a fundamental question: will information create economic value for farmers? We construct a stylized model in which farmers face an uncertain market price (demand) and must make production decisions before the market price is realized. Each farmer has an imprecise private signal and an imprecise public signal to estimate the actual market price. By examining the equilibrium outcomes associated with a Cournot competition game, we show that private signals do create value by improving farmers' welfare. However, this value deteriorates as the public signal becomes available (or more precise). In contrast, in the presence of private signals, the public signal does not always create value for the farmers. Nevertheless, both private and public signals will reduce price variation. We also consider two separate extensions that involve non‐identical private signal precisions and farmers' risk‐aversion, and we find that the same results continue to hold. More importantly, we find that the public signal can reduce welfare inequality when farmers have non‐identical private signal precisions. Also, risk‐aversion can dampen the value created by private or public information.     

Perception of Climate Risk among Rural Farmers in Vietnam: Consistency within Households and with the Empirical Record

Rural farmers in Vietnamese communes perceive climate risk and potential impacts on livelihood within a complex context that may influence individual and household decisions. In a primary survey of 1,145 residents of the Thach Ha district of Ha Tinh province, we gathered data regarding perception about stability in climate, potential risks to livelihood, demographic characteristics, orientation toward risk, and interest in expanding economic activity. Temporal analysis of meteorological and economic indicator data forms an empirical basis for comparison with human perception. We ask the basic question: Are rural farmers’ perceptions of climate consistent with the historical record and reproducible within households? We find that respondents do perceive climate anomalies, with some anchoring on recent extreme events as revealed by climate observational data, and further that spouses disproportionately share perceptions relative to randomly simulated pairings. To put climate‐related risk perception in a larger context, we examine patterns across a range of risks to livelihood faced by farmers (livestock disease, pests, markets, health), using dimension reduction techniques. We find that our respondents distinguish among potential causes of low economic productivity, with substantial emphasis on climate‐related impacts. They do not express uniform concern across risks, but rather average patterns reveal common modes and distinguish climate concern. Still, among those expressing concern about climate‐related risks to livelihood we do not find an association with expressed intention to pursue changes in economic activity as a risk management response.     

Why Risk Analysis is Difficult,and Some Thoughts on How to Proceed

Risk analysis is challenged in three ways by uncertainty. Our understanding of the world and its uncertainties is evolving; indeterminism is an inherent part of the open universe in which we live; and learning from experience involves untestable assumptions. We discuss several concepts of robustness as tools for responding to these epistemological challenges. The use of models is justified, even though they are known to err. A concept of robustness is illustrated in choosing between a conventional technology and an innovative, promising, but more uncertain technology. We explain that nonprobabilistic robust decisions are sometimes good probabilistic bets. Info‐gap and worst‐case concepts of robustness are compared. Finally, we examine the exploitation of favorable but uncertain opportunities and its relation to robust decision making.     

Using Probability Boxes to Model Elicited Information: A Case Study

Governments are responsible for making policy decisions, often in the face of severe uncertainty about the factors involved. Expert elicitation can be used to fill information gaps where data are not available, cannot be obtained, or where there is no time for a full‐scale study and analysis. Various features of distributions for variables may be elicited, for example, the mean, standard deviation, and quantiles, but uncertainty about these values is not always recorded. Distributional and dependence assumptions often have to be made in models and although these are sometimes elicited from experts, modelers may also make assumptions for mathematical convenience (e.g., assuming independence between variables). Probability boxes (p‐boxes) provide a flexible methodology to analyze elicited quantities without having to make assumptions about the distribution shape. If information about distribution shape(s) is available, p‐boxes can provide bounds around the results given these possible input distributions. P‐boxes can also be used to combine variables without making dependence assumptions. This article aims to illustrate how p‐boxes may help to improve the representation of uncertainty for analyses based on elicited information. We focus on modeling elicited quantiles with nonparametric p‐boxes, modeling elicited quantiles with parametric p‐boxes where the elicited quantiles do not match the elicited distribution shape, and modeling elicited interval information.     

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.     

Overcoming Learning Aversion in Evaluating and Managing Uncertain Risks

Decision biases can distort cost‐benefit evaluations of uncertain risks, leading to risk management policy decisions with predictably high retrospective regret. We argue that well‐documented decision biases encourage learning aversion, or predictably suboptimal learning and premature decision making in the face of high uncertainty about the costs, risks, and benefits of proposed changes. Biases such as narrow framing, overconfidence, confirmation bias, optimism bias, ambiguity aversion, and hyperbolic discounting of the immediate costs and delayed benefits of learning, contribute to deficient individual and group learning, avoidance of information seeking, underestimation of the value of further information, and hence needlessly inaccurate risk‐cost‐benefit estimates and suboptimal risk management decisions. In practice, such biases can create predictable regret in selection of potential risk‐reducing regulations. Low‐regret learning strategies based on computational reinforcement learning models can potentially overcome some of these suboptimal decision processes by replacing aversion to uncertain probabilities with actions calculated to balance exploration (deliberate experimentation and uncertainty reduction) and exploitation (taking actions to maximize the sum of expected immediate reward, expected discounted future reward, and value of information). We discuss the proposed framework for understanding and overcoming learning aversion and for implementing low‐regret learning strategies using regulation of air pollutants with uncertain health effects as an example.     

Incentives in a Stage‐Gate Process

Many large organizations use a stage‐gate process to manage new product development projects. In a typical stage‐gate process project managers learn about potential ideas from research and exert effort in development while senior executives make intervening go/no‐go decisions. This decentralized decision making results in an agency problem because the idea quality in early stages is unknown to the executive and the project manager must exert unobservable development effort in later stages. In light of these challenges, how should the firm structure incentives to ensure that project managers reveal relevant information and invest the appropriate effort to create value? In this study, we develop a model of adverse selection in research and moral hazard in development with a go/no‐go decision at the intervening gate. Our results show that the principal's uncertainty regarding early‐stage idea quality—a term we refer to as idea risk—alters the effect of late‐stage development risk. The presence of idea risk can alter the incentives offered to the agent and may lead the principal to reject projects that otherwise seem favorable in terms of positive net present value. A simulation of early‐stage ideas, found through search on a complex landscape, shows that the firm can mitigate the negative effects of idea risk by encouraging breadth of search and high tolerance for failure.     

Climate Change Beliefs and Perceptions of Weather‐Related Changes in the United Kingdom

Public perception research in different countries has suggested that real and perceived periods of high temperature strengthen people's climate change beliefs. Such findings raise questions about the climate change beliefs of people in regions with moderate climates. Relatively little is known about whether public concerns about climate change may also be associated with perceived changes in other weather‐related events, such as precipitation or flooding. We examine the relationship between perceived changes in weather‐related events and climate change beliefs among U.K. residents at a time of below‐average winter temperatures and recent flooding. National survey data (n = 1,848) revealed that heat waves and hot summers were perceived to have become less common during respondents’ lifetimes, while flooding, periods of heavy rainfall, coastal erosions, and mild winters were perceived to have increased in frequency and cold winters were perceived to be unchanged. Although perceived changes in hot‐weather‐related events were positively associated with climate change beliefs, perceived changes in wet‐weather‐related events were found to be an even stronger predictor. Self‐reported experience of “flooding in own area” and “heat‐wave discomfort” also significantly contributed to climate change beliefs. These findings highlight the importance of salient weather‐related events and experiences in the formation of beliefs about climate change. We link our findings to research in judgment and decision making, and propose that those wishing to engage with the public on the issue of climate change should not limit their focus to heat.     

Context‐Specific,Scenario‐Based Risk Scales

Reacting to an emergency requires quick decisions under stressful and dynamic conditions. To react effectively, responders need to know the right actions to take given the risks posed by the emergency. While existing research on risk scales focuses primarily on decision making in static environments with known risks, these scales may be inappropriate for conditions where the decision maker's time and mental resources are limited and may be infeasible if the actual risk probabilities are unknown. In this article, we propose a method to develop context‐specific, scenario‐based risk scales designed for emergency response training. Emergency scenarios are used as scale points, reducing our dependence on known probabilities; these are drawn from the targeted emergency context, reducing the mental resources required to interpret the scale. The scale is developed by asking trainers/trainees to rank order a range of risk scenarios and then aggregating these orderings using a Kemeny ranking. We propose measures to assess this aggregated scale's internal consistency, reliability, and validity, and we discuss how to use the scale effectively. We demonstrate our process by developing a risk scale for subsurface coal mine emergencies and test the reliability of the scale by repeating the process, with some methodological variations, several months later.     

Can Carbon Nanomaterials Improve CZTS Photovoltaic Devices? Evaluation of Performance and Impacts Using Integrated Life‐Cycle Assessment and Decision Analysis

In emergent photovoltaics, nanoscale materials hold promise for optimizing device characteristics; however, the related impacts remain uncertain, resulting in challenges to decisions on strategic investment in technology innovation. We integrate multi‐criteria decision analysis (MCDA) and life‐cycle assessment (LCA) results (LCA‐MCDA) as a method of incorporating values of a hypothetical federal acquisition manager into the assessment of risks and benefits of emerging photovoltaic materials. Specifically, we compare adoption of copper zinc tin sulfide (CZTS) devices with molybdenum back contacts to alternative devices employing graphite or graphene instead of molybdenum. LCA impact results are interpreted alongside benefits of substitution including cost reductions and performance improvements through application of multi‐attribute utility theory. To assess the role of uncertainty we apply Monte Carlo simulation and sensitivity analysis. We find that graphene or graphite back contacts outperform molybdenum under most scenarios and assumptions. The use of decision analysis clarifies potential advantages of adopting graphite as a back contact while emphasizing the importance of mitigating conventional impacts of graphene production processes if graphene is used in emerging CZTS devices. Our research further demonstrates that a combination of LCA and MCDA increases the usability of LCA in assessing product sustainability. In particular, this approach identifies the most influential assumptions and data gaps in the analysis and the areas in which either engineering controls or further data collection may be necessary.     

Are We Adapting to Floods? Evidence from Global Flooding Fatalities

There has been a growing interest in understanding whether and how people adapt to extreme weather events in a changing climate. This article presents one of the first empirical analyses of adaptation to flooding on a global scale. Using a sample of 97 countries between 1985 and 2010, we investigate the extent and pattern of flood adaptation by estimating the effects of a country's climatological risk, recent flood experiences, and socioeconomic characteristics on its flood‐related fatalities. Our results provide mixed evidence on adaptation: countries facing greater long‐term climatological flooding risks do not necessarily adapt better and suffer fewer fatalities; however, after controlling for the cross‐country heterogeneity, we find that more recent flooding shocks have a significant and negative effect on fatalities from subsequent floods. These findings may suggest the short‐term learning dynamics of adaptation and potential inefficacy of earlier flood control measures, particularly those that promote increased exposure in floodplains. Our findings provide important implications for climate adaptation policy making and climate modeling.     

New Product Development Decision‐Making Effectiveness: Comparing Individuals,Face‐To‐Face Teams,and Virtual Teams*

A total of 411 subjects participated in two decision‐making experiments in order to examine the effectiveness of new product development project continuation decisions. Using escalation of commitment theory, in Study 1, individual versus face‐to‐face team decision‐making effectiveness was compared. Study 2, an extension of Study 1, compared the new product development decision‐making effectiveness of individuals, face‐to‐face teams, and virtual teams. A virtual team is a geographically and temporally dispersed and electronically communicating work group. In Study 2, the virtual teams communicated asynchronously via groupware technology. Our findings suggest that teams make more effective decisions than individuals, and virtual teams make the most effective decisions.     

The Implications of Utilizing Market Information and Adopting Agricultural Advice for Farmers in Developing Economies

To alleviate poverty in developing countries, governments and non‐governmental organizations disseminate two types of information: (i) agricultural advice to enable farmers to improve their operations (cost reduction, quality improvement, and process yield increase); and (ii) market information about future price/demand to enable farmers to make better production planning decisions. This information is usually disseminated free of charge. While farmers can use the market information to improve their production plans without incurring any (significant) cost, adopting agricultural advice to improve operations requires upfront investment, for example, equipment, fertilizers, pesticides, and higher quality seeds. In this study, we examine whether farmers should use market information to improve their production plans (or adopt agricultural advice to improve their operations) when they engage in Cournot competition under both uncertain market demand and uncertain process yield. Our analysis indicates that both farmers will use the market information to improve their profits in equilibrium. Hence, relative to the base case in which market information is not available, the provision of market information can improve the farmers' total welfare (i.e., total profit for both farmers). Moreover, when the underlying process yield is highly uncertain or when the products are highly heterogeneous, the provision of market information is welfare‐maximizing in the sense that the maximum total welfare of farmers is attained when both farmers utilize market information in equilibrium. Furthermore, in equilibrium, whether a farmer adopts the agricultural advice depends on the size of the requisite upfront investment. More importantly, we show that agricultural advice is not always welfare improving unless the upfront investment is sufficiently low. This result implies that to improve farmers' welfare, governments should consider offering farmer subsidies.     

Reconciling Uncertain Costs and Benefits in Bayes Nets for Invasive Species Management

Bayes nets are used increasingly to characterize environmental systems and formalize probabilistic reasoning to support decision making. These networks treat probabilities as exact quantities. Sensitivity analysis can be used to evaluate the importance of assumptions and parameter estimates. Here, we outline an application of info‐gap theory to Bayes nets that evaluates the sensitivity of decisions to possibly large errors in the underlying probability estimates and utilities. We apply it to an example of management and eradication of Red Imported Fire Ants in Southern Queensland, Australia and show how changes in management decisions can be justified when uncertainty is considered.     

不利天气影响下“公司+农户”型订单契约设计

不利天气影响农业生产并使订单农业中各成员在履约过程中遭遇不可控风险,针对这一问题,构建了由风险厌恶的农户与风险中性的公司组成的两级农产品供应链随机利润模型,在条件风险价值(CVaR)准则下,建立了具有风险厌恶特性农户的CVaR决策模型,比较分析了不利天气对集中式与分散式决策模式下最优决策的影响。研究发现不利天气降低了供应链绩效而农户的风险厌恶特性则加剧了双边际效应,对此,设计了一种与天气指数和农户风险厌恶度相关的改进收益共享契约。研究表明:公司可依据不利天气影响的结果来设计订单契约以激励双方成为"收益共享、风险共担"的统一体,此外,在实施该订单契约后可实现农产品供应链的完美共赢协调,这弥补了分散式决策下的效率损失并增强了供应链系统的稳健性。     

Mitigating Supply Uncertainty: The Interplay Between Diversification and Pricing

We consider a firm's sourcing problem from one reliable supplier and one unreliable supplier in two price‐setting scenarios. In the committed pricing scenario, the firm makes the pricing decision before the supply uncertainty is resolved. In the responsive pricing scenario, the firm's pricing decision is made after the supply uncertainty is resolved. For the committed pricing scenario, we develop a condition on supply uncertainty that guarantees the unimodality of the firm's objective function. By comparing the firm's optimal diversification decisions in the two pricing scenarios, we examine the interplay of supply diversification strategy and responsive pricing strategy in mitigating supply uncertainty. While both strategies are effective in mitigating supply uncertainty, we show that they are not necessarily substitutes. The relationship between these two strategies depends on two adverse effects caused by supply uncertainty: the lost‐revenue effect and the lost‐goodwill effect. More specifically, when the lost‐revenue effect dominates the lost‐goodwill effect, these two strategies are complements; otherwise, they are substitutes. Furthermore, we examine the impact of market size, price sensitivity, supplier reliability, and failure rebate on the interplay between these two strategies, and discuss the implications of our results. Finally, we extend our analysis to the case of two unreliable suppliers and show that the insights regarding the interplay between diversification and pricing continue to hold.     

Climate Change Risk Perception and Communication: Addressing a Critical Moment?

Climate change is an increasingly salient issue for societies and policy‐makers worldwide. It now raises fundamental interdisciplinary issues of risk and uncertainty analysis and communication. The growing scientific consensus over the anthropogenic causes of climate change appears to sit at odds with the increasing use of risk discourses in policy: for example, to aid in climate adaptation decision making. All of this points to a need for a fundamental revision of our conceptualization of what it is to do climate risk communication. This Special Collection comprises seven papers stimulated by a workshop on “Climate Risk Perceptions and Communication” held at Cumberland Lodge Windsor in 2010. Topics addressed include climate uncertainties, images and the media, communication and public engagement, uncertainty transfer in climate communication, the role of emotions, localization of hazard impacts, and longitudinal analyses of climate perceptions. Climate change risk perceptions and communication work is critical for future climate policy and decisions.