Improved Transferability of Data‐Driven Damage Models Through Sample Selection Bias Correction

Damage models for natural hazards are used for decision making on reducing and transferring risk. The damage estimates from these models depend on many variables and their complex sometimes nonlinear relationships with the damage. In recent years, data‐driven modeling techniques have been used to capture those relationships. The available data to build such models are often limited. Therefore, in practice it is usually necessary to transfer models to a different context. In this article, we show that this implies the samples used to build the model are often not fully representative for the situation where they need to be applied on, which leads to a “sample selection bias.” In this article, we enhance data‐driven damage models by applying methods, not previously applied to damage modeling, to correct for this bias before the machine learning (ML) models are trained. We demonstrate this with case studies on flooding in Europe, and typhoon wind damage in the Philippines. Two sample selection bias correction methods from the ML literature are applied and one of these methods is also adjusted to our problem. These three methods are combined with stochastic generation of synthetic damage data. We demonstrate that for both case studies, the sample selection bias correction techniques reduce model errors, especially for the mean bias error this reduction can be larger than 30%. The novel combination with stochastic data generation seems to enhance these techniques. This shows that sample selection bias correction methods are beneficial for damage model transfer.     

Efficacy Foundations for Risk Communication: How People Think About Reducing the Risks of Climate Change

Believing action to reduce the risks of climate change is both possible (self‐efficacy) and effective (response efficacy) is essential to motivate and sustain risk mitigation efforts, according to current risk communication theory. Although the public recognizes the dangers of climate change, and is deluged with lists of possible mitigative actions, little is known about public efficacy beliefs in the context of climate change. Prior efficacy studies rely on conflicting constructs and measures of efficacy, and links between efficacy and risk management actions are muddled. As a result, much remains to learn about how laypersons think about the ease and effectiveness of potential mitigative actions. To bring clarity and inform risk communication and management efforts, we investigate how people think about efficacy in the context of climate change risk management by analyzing unprompted and prompted beliefs from two national surveys (N = 405, N = 1,820). In general, respondents distinguish little between effective and ineffective climate strategies. While many respondents appreciate that reducing fossil fuel use is an effective risk mitigation strategy, overall assessments reflect persistent misconceptions about climate change causes, and uncertainties about the effectiveness of risk mitigation strategies. Our findings suggest targeting climate change risk communication and management strategies to (1) address gaps in people's existing mental models of climate action, (2) leverage existing public understanding of both potentially effective mitigation strategies and the collective action dilemma at the heart of climate change action, and (3) take into account ideologically driven reactions to behavior change and government action framed as climate action.     

Risk Analysis: Celebrating the Accomplishments and Embracing Ongoing Challenges

As part of the celebration of the 40th anniversary of the Society for Risk Analysis and Risk Analysis: An International Journal, this essay reviews the 10 most important accomplishments of risk analysis from 1980 to 2010, outlines major accomplishments in three major categories from 2011 to 2019, discusses how editors circulate authors’ accomplishments, and proposes 10 major risk-related challenges for 2020–2030. Authors conclude that the next decade will severely test the field of risk analysis.     

行业系统性风险的动态特征及其影响因素

现代经济主体间网络关联性越来越强,风险很容易在不同行业间扩散,因此有效识别并分析系统性风险是防范金融危机的关键步骤。基于条件风险价值(CoVaR)和边际期望损失(MES)两个指标,对巨潮行业指数系统性风险的静态和动态特征进行了研究。结果发现,各行业间系统性风险的相关性较强,动态特征显示2009年年初和2016年3月为系统性风险的两个峰值;从分行业来看,材料行业的系统性风险最高,而消费和医药行业的系统性风险最低。采用动态面板模型分析影响行业系统性风险的市场面因素发现,短期涨幅较高、长期涨幅较低及流动性较充分的行业,其系统性风险往往更低。因此,应加强对系统性风险较高行业的监管力度,建立好金融防火墙,防止外部金融风险的过度传染;同时应加强对各行业的实时监控,尤其是关注短期暴涨暴跌及流动性充分与否的监控。     

A Probabilistic Framework for Risk Analysis of Widespread Flood Events: A Proof‐of‐Concept Study

This article presents a flood risk analysis model that considers the spatially heterogeneous nature of flood events. The basic concept of this approach is to generate a large sample of flood events that can be regarded as temporal extrapolation of flood events. These are combined with cumulative flood impact indicators, such as building damages, to finally derive time series of damages for risk estimation. Therefore, a multivariate modeling procedure that is able to take into account the spatial characteristics of flooding, the regionalization method top‐kriging, and three different impact indicators are combined in a model chain. Eventually, the expected annual flood impact (e.g., expected annual damages) and the flood impact associated with a low probability of occurrence are determined for a study area. The risk model has the potential to augment the understanding of flood risk in a region and thereby contribute to enhanced risk management of, for example, risk analysts and policymakers or insurance companies. The modeling framework was successfully applied in a proof‐of‐concept exercise in Vorarlberg (Austria). The results of the case study show that risk analysis has to be based on spatially heterogeneous flood events in order to estimate flood risk adequately.     

Cross‐Sectional Psychological and Demographic Associations of Zika Knowledge and Conspiracy Beliefs Before and After Local Zika Transmission

Perceptions of infectious diseases are important predictors of whether people engage in disease‐specific preventive behaviors. Having accurate beliefs about a given infectious disease has been found to be a necessary condition for engaging in appropriate preventive behaviors during an infectious disease outbreak, while endorsing conspiracy beliefs can inhibit preventive behaviors. Despite their seemingly opposing natures, knowledge and conspiracy beliefs may share some of the same psychological motivations, including a relationship with perceived risk and self‐efficacy (i.e., control). The 2015–2016 Zika epidemic provided an opportunity to explore this. The current research provides some exploratory tests of this topic derived from two studies with similar measures, but different primary outcomes: one study that included knowledge of Zika as a key outcome and one that included conspiracy beliefs about Zika as a key outcome. Both studies involved cross‐sectional data collections that occurred during the same two periods of the Zika outbreak: one data collection prior to the first cases of local Zika transmission in the United States (March–May 2016) and one just after the first cases of local transmission (July–August). Using ordinal logistic and linear regression analyses of data from two time points in both studies, the authors show an increase in relationship strength between greater perceived risk and self‐efficacy with both increased knowledge and increased conspiracy beliefs after local Zika transmission in the United States. Although these results highlight that similar psychological motivations may lead to Zika knowledge and conspiracy beliefs, there was a divergence in demographic association.     

Risk Modeling of Interdependent Complex Systems of Systems: Theory and Practice

The emergence of the complexity characterizing our systems of systems (SoS) requires a reevaluation of the way we model, assess, manage, communicate, and analyze the risk thereto. Current models for risk analysis of emergent complex SoS are insufficient because too often they rely on the same risk functions and models used for single systems. These models commonly fail to incorporate the complexity derived from the networks of interdependencies and interconnectedness (I–I) characterizing SoS. There is a need to reevaluate currently practiced risk analysis to respond to this reality by examining, and thus comprehending, what makes emergent SoS complex. The key to evaluating the risk to SoS lies in understanding the genesis of characterizing I–I of systems manifested through shared states and other essential entities within and among the systems that constitute SoS. The term “essential entities” includes shared decisions, resources, functions, policies, decisionmakers, stakeholders, organizational setups, and others. This undertaking can be accomplished by building on state‐space theory, which is fundamental to systems engineering and process control. This article presents a theoretical and analytical framework for modeling the risk to SoS with two case studies performed with the MITRE Corporation and demonstrates the pivotal contributions made by shared states and other essential entities to modeling and analysis of the risk to complex SoS. A third case study highlights the multifarious representations of SoS, which require harmonizing the risk analysis process currently applied to single systems when applied to complex SoS.     

影子银行、地方政府债务及金融发展动态关系研究——基于省级面板VAR模型

基于2004—2017年中国省级面板数据,运用面板向量自回归(PVAR)模型,使用系统GMM估计、脉冲响应函数、方差分解以及格兰杰因果关系检验等方法分析了影子银行、地方政府债务及金融发展之间的动态关系.结果表明:影子银行、地方政府债务与金融发展水平三者之间存在动态耦合关系.在地方政府融资能力受到约束的情况下,影子银行为地方政府提供了多元的融资方式,在增加政府融资能力的同时提升了政府债务水平;而地方政府债务需求显著推动了影子银行规模的快速发展.同时,影子银行过度扩张危害了金融市场的健康发展,降低金融发展水平,继而使地方政府的融资渠道受到约束.但金融发展并不能有效约束影子银行规模,原因在于,政府融资需求是影子银行的主要动力,若不能控制地方政府的借贷行为则无法从源头解决问题.监管机构在去杠杆的过程中,应该综合考虑影子银行与地方政府债务、金融发展之间的动态关系,如此才能够实现预期的政策效果.     

Managing the Risk of Aggressive Dog Behavior: Investigating the Influence of Owner Threat and Efficacy Perceptions

Aggressive behavior in pet dogs is a serious problem for dog owners across the globe, with bite injuries representing a serious risk to both people and other dogs. The effective management of aggressive behavior in dogs represents a challenging and controversial issue. Although positive reinforcement training methods are now considered to be the most effective and humane technique to manage the risk of aggression, punishment‐based methods continue to be used. Unfortunately, there has been little scientific study into the various factors influencing whether dog owners choose to use positive reinforcement techniques to manage aggression in their dogs. As such, current understanding of how best to encourage and support dog owners to use these methods remains extremely limited. This article uses a survey methodology based on protection motivation theory (PMT) to investigate the factors that influence owner use of positive reinforcement methods to manage aggressive behavior, in an attempt to understand potential barriers and drivers of use. In addition, the article provides an initial exploration of the potential role of wider psychological factors, including owner emotional state, social influence, and cognitive bias. Findings show that the perceived efficacy of positive reinforcement methods and the perceived ability of owners to effectively implement the technique are both key factors predicting future intentions and current reported use. Future interventions should focus on enhancing owner confidence in the effective use of positive reinforcement techniques across multiple scenarios, as well as helping owners manage their own emotional responses when they encounter challenging situations and setbacks.     

Decision‐making under uncertainty in child protection: Creating a just and learning culture

The argument is made for having a positive error culture in child protection to improve decision‐making and risk management. This requires organizations to accept that mistakes are likely and to treat them as opportunities for learning and improving. In contrast, in many organizations, a punitive reaction to errors leads to workers hiding them and developing a defensive approach to their practice with children and families. The safety management literature has shown how human error is generally not simply due to a “bad apple” but made more or less likely by the work context that helps or hinders good performance. Improving safety requires learning about the weaknesses in the organization that contribute to poor performance. To create a learning culture, people need to feel that when they talk about mistakes or weak practice, there will be a constructive response from their organization. One aspect of reducing the blame culture is to develop a shared understanding of how practice will be judged and how those appraising practice will avoid the hindsight bias. To facilitate a positive error culture, a set of risk principles are presented that offer a set of criteria by which practice should be appraised.