Exploring death anxiety among elderly people: a literature review and empirical investigation

Given the growing number of elderly persons in society and concerns about their health and well-being, the aim was to review the available literature on their death anxiety, and to explore features of this experience among a small sample of older men and women in care facilities. In both the review and empirical parts of this study, components and correlates of death anxiety were investigated. The review revealed limited research focus on death anxiety among the elderly, particularly among those in institutions, but suggested both components and correlates for inclusion in our empirical study. Results showed that, among our elderly participants in an assisted living facility (N = 49; age range: 60-96 years), there were higher levels of fear for others and of the dying process than for fear of the unknown. Notably, among the correlates identified, fear for significant others was associated with poor physical health; fear of the dying process was related to low self-esteem, little purpose in life, and poor mental well-being. Gender differences in death anxiety were found: women showed greater fear for the death of loved ones and for the consequences of their own death on these loved ones, than did men. These patterns are discussed in the light of concerns about the welfare of elderly persons; scientific implications are also considered.     

A Systems‐Based Risk Assessment Framework for Intentional Electromagnetic Interference (IEMI) on Critical Infrastructures

Modern infrastructures are becoming increasingly dependent on electronic systems, leaving them more vulnerable to electrical surges or electromagnetic interference. Electromagnetic disturbances appear in nature, e.g., lightning and solar wind; however, they may also be generated by man‐made technology to maliciously damage or disturb electronic equipment. This article presents a systematic risk assessment framework for identifying possible, consequential, and plausible intentional electromagnetic interference (IEMI) attacks on an arbitrary distribution network infrastructure. In the absence of available data on IEMI occurrences, we find that a systems‐based risk assessment is more useful than a probabilistic approach. We therefore modify the often applied definition of risk, i.e., a set of triplets containing scenario, probability, and consequence, to a set of quadruplets: scenario, resource requirements, plausibility, and consequence. Probability is “replaced” by resource requirements and plausibility, where the former is the minimum amount and type of equipment necessary to successfully carry out an attack scenario and the latter is a subjective assessment of the extent of the existence of attackers who possess the motivation, knowledge, and resources necessary to carry out the scenario. We apply the concept of intrusion areas and classify electromagnetic source technology according to key attributes. Worst‐case scenarios are identified for different quantities of attacker resources. The most plausible and consequential of these are deemed the most important scenarios and should provide useful decision support in a countermeasures effort. Finally, an example of the proposed risk assessment framework, based on notional data, is provided on a hypothetical water distribution network.     

Adaptive Management: A Paradigm for Remediation of Public Facilities Following a Terrorist Attack

Terrorist actions are aimed at maximizing harm (health, psychological, economical, and political) through the combined physical impacts of the act and fear. Immediate and effective response to a terrorist act is critical to limit human and environmental harm, effectively restore facility function, and maintain public confidence. Though there have been terrorist attacks in public facilities that we have learned from, overall our experiences in restoration of public facilities following a terrorist attack are limited. Restoration of public facilities following a release of a hazardous material is inherently far more complex than in industrial settings and has many unique technical, economic, social, and political challenges. For example, there may be a great need to quickly restore the facility to full operation and allow public access even though it was not designed for easy or rapid restoration, and critical information is needed for quantitative risk assessment and effective restoration must be anticipated to be incomplete and uncertain. Whereas present planning documents have substantial linearity in their organization, the “adaptive management” paradigm provides a constructive parallel paradigm for restoration of public facilities that anticipates and plans for uncertainty, inefficiencies, and stakeholder participation. Adaptive management grew out of the need to manage and restore natural resources in highly complex and changing environments with limited knowledge about causal relationships and responses to restoration actions. Similarities between natural resource management and restoration of a public facility after a terrorist attack suggest that integration of adaptive management principles explicitly into restoration processes will result in substantially enhanced and flexible responses necessary to meet the uncertainties of potential terrorist attacks.     

The Psychophysics of Terror Attack Casualty Counts

In communicating the risk that terror attacks pose to the public, government agencies and other organizations must understand which characteristics of an attack contribute to the public's perception of its severity. An attack's casualty count is one of the most commonly used metrics of a terror attack's severity, yet it is unclear whether the public responds to information about casualty count when forming affective and cognitive reactions to terror attacks. This study sought to characterize the “psychophysical function” relating terror attack casualty counts to the severity of the affective and cognitive reactions they elicit. We recruited n = 684 Mechanical Turk participants to read a realistic vignette depicting either a biological or radiological terror attack, whose death toll ranged from 20 to 50,000, and rated their levels of fear and anger along with the attack's severity. Even when controlling for the perceived plausibility of the scenarios, participants’ severity ratings of each attack were logarithmic with respect to casualty count, while ratings of fear and anger did not significantly depend on casualty count. These results were consistent across attack weapon (biological vs. radiological) and time horizon of the casualties (same-day or anticipated to occur over several years). These results complement past work on life loss valuation and highlight a potential bifurcation between the public's affective and cognitive evaluations of terror attacks.     

Bayesian Treatment of Model Uncertainty for Partially Applicable Models

This article discusses how analyst's or expert's beliefs on the credibility and quality of models can be assessed and incorporated into the uncertainty assessment of an unknown of interest. The proposed methodology is a specialization of the Bayesian framework for the assessment of model uncertainty presented in an earlier paper. This formalism treats models as sources of information in assessing the uncertainty of an unknown, and it allows the use of predictions from multiple models as well as experimental validation data about the models’ performances. In this article, the methodology is extended to incorporate additional types of information about the model, namely, subjective information in terms of credibility of the model and its applicability when it is used outside its intended domain of application. An example in the context of fire risk modeling is also provided.     

Effects of exposure to occupational violence and the mediating impact of fear

This paper examines the consequences of exposure to work-related violence and in particular, focuses upon the role of fear (of violence) as a mediating variable. The sample comprises UK public house licensees-individuals who manage public houses and hold the licence permitting the sale of alcoholic drinks on the premises. Questionnaires were sent to 479 licensees and a response rate of 51% was achieved. Each questionnaire measured exposure to a range of violent assaults, fear of violence, general well-being, job satisfaction and organizational commitment. Based upon a final sample of 242 pub licensees, regression analyses showed that as the severity of the violence to which licensees were exposed increased, so fear of becoming a victim and symptoms of impaired well-being increased, while job satisfaction and organizational commitment decreased. Hierarchical analyses showed that fear of violence fully mediated the negative impact of exposure upon each of these other variables. It is argued that the management of fear appraisals should be integrated into organizational responses to work-place violence.     

The NAS Risk Paradigm as a Medium for Communication

Many journalists, public interest groups and other recipients of risk assessment information are familiar with the National Academy of Sciences risk assessment paradigm. From time to time, paradigm concepts appear in news features or community group discussions on environmental issues. With knowledge of the paradigm common to scientists, journalists, and other interested parties, the paradigm is a potentially important medium for communication between risk scientists, journalists, and the public. Specifically, the paradigm offers widely-accepted organizing principles for presenting risk information, a common language for addressing a variety of issues and concepts, and a flexible analytical system that accommodates the diversity of scientific information and policy perspectives that characterize the risk assessment process. In addition, the paradigm outlines important relationships and distinctions between risk assessment and risk management. Informed and creative use of these features of the paradigm can guide and simplify interviews between journalists or community groups and their expert sources, clarify presentation of risk information, and promote collaboration between risk scientists, journalists, and others to assure complete, objective and fair comment on risk issues of interest to the public.     

Exploring the Uncertainties in Cancer Risk Assessment Using the Integrated Probabilistic Risk Assessment (IPRA) Approach

Current methods for cancer risk assessment result in single values, without any quantitative information on the uncertainties in these values. Therefore, single risk values could easily be overinterpreted. In this study, we discuss a full probabilistic cancer risk assessment approach in which all the generally recognized uncertainties in both exposure and hazard assessment are quantitatively characterized and probabilistically evaluated, resulting in a confidence interval for the final risk estimate. The methodology is applied to three example chemicals (aflatoxin, N‐nitrosodimethylamine, and methyleugenol). These examples illustrate that the uncertainty in a cancer risk estimate may be huge, making single value estimates of cancer risk meaningless. Further, a risk based on linear extrapolation tends to be lower than the upper 95% confidence limit of a probabilistic risk estimate, and in that sense it is not conservative. Our conceptual analysis showed that there are two possible basic approaches for cancer risk assessment, depending on the interpretation of the dose‐incidence data measured in animals. However, it remains unclear which of the two interpretations is the more adequate one, adding an additional uncertainty to the already huge confidence intervals for cancer risk estimates.     

Geographic Distributions of Extreme Weather Risk Perceptions in the United States

Weather and climate disasters pose an increasing risk to life and property in the United States. Managing this risk requires objective information about the nature of the threat and subjective information about how people perceive it. Meteorologists and climatologists have a relatively firm grasp of the historical objective risk. For example, we know which parts of the United States are most likely to experience drought, heat waves, flooding, snow or ice storms, tornadoes, and hurricanes. We know less about the geographic distribution of the perceived risks of meteorological events and trends. Do subjective perceptions align with exposure to weather risks? This question is difficult to answer because analysts have yet to develop a comprehensive and spatially consistent methodology for measuring risk perceptions across geographic areas in the United States. In this project, we propose a methodology that uses multilevel regression and poststratification to estimate extreme weather and climate risk perceptions by geographic area (i.e., region, state, forecast area, and county). Then we apply the methodology using data from three national surveys (n = 9,542). This enables us to measure, map, and compare perceptions of risk from multiple weather hazards in geographic areas across the country.     

Some Limitations of Qualitative Risk Rating Systems

Qualitative systems for rating animal antimicrobial risks using ordered categorical labels such as “high,”“medium,” and “low” can potentially simplify risk assessment input requirements used to inform risk management decisions. But do they improve decisions? This article compares the results of qualitative and quantitative risk assessment systems and establishes some theoretical limitations on the extent to which they are compatible. In general, qualitative risk rating systems satisfying conditions found in real‐world rating systems and guidance documents and proposed as reasonable make two types of errors: (1) Reversed rankings, i.e., assigning higher qualitative risk ratings to situations that have lower quantitative risks; and (2) Uninformative ratings, e.g., frequently assigning the most severe qualitative risk label (such as “high”) to situations with arbitrarily small quantitative risks and assigning the same ratings to risks that differ by many orders of magnitude. Therefore, despite their appealing consensus‐building properties, flexibility, and appearance of thoughtful process in input requirements, qualitative rating systems as currently proposed often do not provide sufficient information to discriminate accurately between quantitatively small and quantitatively large risks. The value of information (VOI) that they provide for improving risk management decisions can be zero if most risks are small but a few are large, since qualitative ratings may then be unable to confidently distinguish the large risks from the small. These limitations suggest that it is important to continue to develop and apply practical quantitative risk assessment methods, since qualitative ones are often unreliable.     

Evaluating the Simplified Conjoint Expected Risk Model: Comparing the Use of Objective and Subjective Information

The simplified Conjoint Expected Risk (CER) model by Holtgrave and Weber posits that perceived risk is a linear combination of the subjective judgments of the probabilities of harm, benefit, and status quo, and the expected harm and benefit of an activity. It modifies Luce and Weber's original CER model—that uses objective information to evaluate financial gambles—to accommodate activities such as health/technology activities where values of the model variables are subjective. If the simplified model is a valid modification of the original model, its performance should not be sensitive to the use of subjective information. However, because people may evaluate information differently when objective information is provided to them than when they generate information on their own, the performance of the simplified CER model may not be robust to the source of model-variable information. We compared the use of objective and subjective information, and results indicate that the estimates of the simplified CER model parameters and the proportion of variance in risk judgments accounted for by the model are similar under these two conditions. Thus, the simplified CER model is viable with activities for which harm and benefit information is subjective.     

路演中董事长面部表情与IPO后公司业绩变脸

投资者是否可以利用“软”信息来识别公司的真实价值?社会心理学理论认为人在提供虚假信息(如撒谎)的时候会流露出负面情绪,进而反映到面部表情上。论文以2009年至2014年在创业板上市的公司为样本,分析公司上市后的业绩变脸幅度与其董事长在IPO路演讲话时的负面情绪水平之间的关系。为了提高结论的可靠性,本研究首先基于实验室实验方法设计欺骗行为实验,检验了Facereader软件测量面部表情给出的负面情绪值与欺骗行为之间的相关性。本研究指出可以利用董事长在IPO路演讲话的面部表情来识别公司的真实价值,该结论为保护投资者利益提供了一条新的途径。     

Designing Graphs to Communicate Risks: Understanding How the Choice of Graphical Format Influences Decision Making

Previous research suggests that the choice of graphical format for communicating risk information affects both understanding of the risk magnitude and the likelihood of acting to decrease risk. However, the mechanisms through which these effects work are poorly understood. To explore these mechanisms using a real‐world scenario, we examined the relative impact of two graphical displays for depicting the risk of exposure to unexploded ammunition during potential land redevelopment. One display depicted only the foreground information graphically (a bar graph of the number of people harmed), and a second depicted the foreground and background graphically (a stacked bar graph representing both the number harmed and at risk). We presented 296 participants with either the foreground‐only or the foreground and background graphical display and measured a broad set of outcome variables, examining (1) the graphical display effect on each of the outcome measures and (2) the pathways by which any display effects work to influence decision making. We found that the foreground‐only graphical display increased perceived likelihood and experienced fear, which produced greater worry, which in turn increased risk aversion. In addition, a positive evaluation of the communication materials increased support for policies related to land redevelopment, whether those policies were risk taking or risk mitigating. Finally, the foreground‐only graphical display decreased understanding of the risk magnitude, showing that approaches to accomplish one risk communication goal (promoting risk‐averse decisions) may do so at the expense of another goal (increasing understanding).     

Risk Perceptions,Optimism, and Natural Hazards

This article uses the panel survey developed for the Health and Retirement Study to evaluate whether Hurricane Andrew in 1992 altered longevity expectations of respondents who lived in Dade County, Florida, the location experiencing the majority of about 20 billion dollars of damage. Longevity expectations have been used as a proxy measure for both individual subjective risk assessments and dispositional optimism. The panel structure allows comparison of those respondents' longevity assessments when the timing of their survey responses bracket Andrew with those of individuals where it does not. After controlling for health effects, the results indicate a significant reduction in longevity expectations due to the information respondents appear to have associated with the storm.     

The Role of Behavioral Responses in the Total Economic Consequences of Terrorist Attacks on U.S. Air Travel Targets

U.S. airports and airliners are prime terrorist targets. Not only do the facilities and equipment represent high‐value assets, but the fear and dread that is spread by such attacks can have tremendous effects on the U.S. economy. This article presents the methodology, data, and estimates of the macroeconomic impacts stemming from behavioral responses to a simulated terrorist attack on a U.S. airport and on a domestic airliner. The analysis is based on risk‐perception surveys of these two scenarios. The responses relate to reduced demand for airline travel, shifts to other modes, spending on nontravel items, and savings of potential travel expenditures by U.S. resident passengers considering flying domestic routes. We translate these responses to individual spending categories and feed these direct impact results into a computable general equilibrium (CGE) model of the U.S. economy to ascertain the indirect and total impacts on both the airline industry and the economy as a whole. Overall, the estimated impacts on GDP of both types of attacks exceed $10B. We find that the behavioral economic impacts are almost an order of magnitude higher than the ordinary business interruption impacts for the airliner attack and nearly two orders of magnitude higher for the airport attack. The results are robust to sensitivity tests on the travel behavior of U.S. residents in response to terrorism.     

Risk analysis and risk management in an uncertain world.

The tragic attacks of September 11 and the bioterrorist threats with respect to anthrax that followed have raised a set of issues regarding how we deal with events where there is considerable ambiguity and uncertainty about the likelihood of their occurrence and their potential consequences. This paper discusses how one can link the tools of risk assessment and our knowledge of risk perception to develop risk management options for dealing with extreme events. In particular, it suggests ways that the members of the Society for Risk Analysis can apply their expertise and talent to the risks associated with terrorism and discusses the changing roles of the public and private sectors in dealing with extreme events.     

A Comprehensive Network Security Risk Model for Process Control Networks

The risk of cyber attacks on process control networks (PCN) is receiving significant attention due to the potentially catastrophic extent to which PCN failures can damage the infrastructures and commodity flows that they support. Risk management addresses the coupled problems of (1) reducing the likelihood that cyber attacks would succeed in disrupting PCN operation and (2) reducing the severity of consequences in the event of PCN failure or manipulation. The Network Security Risk Model (NSRM) developed in this article provides a means of evaluating the efficacy of candidate risk management policies by modeling the baseline risk and assessing expectations of risk after the implementation of candidate measures. Where existing risk models fall short of providing adequate insight into the efficacy of candidate risk management policies due to shortcomings in their structure or formulation, the NSRM provides model structure and an associated modeling methodology that captures the relevant dynamics of cyber attacks on PCN for risk analysis. This article develops the NSRM in detail in the context of an illustrative example.     

Model uncertainty and choices made by modelers: lessons learned from the International Atomic Energy Agency model intercomparisons.

The treatment of uncertainties associated with modeling and risk assessment has recently attracted significant attention. The methodology and guidance for dealing with parameter uncertainty have been fairly well developed and quantitative tools such as Monte Carlo modeling are often recommended. However, the issue of model uncertainty is still rarely addressed in practical applications of risk assessment. The use of several alternative models to derive a range of model outputs or risks is one of a few available techniques. This article addresses the often-overlooked issue of what we call "modeler uncertainty," i.e., difference in problem formulation, model implementation, and parameter selection originating from subjective interpretation of the problem at hand. This study uses results from the Fruit Working Group, which was created under the International Atomic Energy Agency (IAEA) BIOMASS program (BIOsphere Modeling and ASSessment). Model-model and model-data intercomparisons reviewed in this study were conducted by the working group for a total of three different scenarios. The greatest uncertainty was found to result from modelers' interpretation of scenarios and approximations made by modelers. In scenarios that were unclear for modelers, the initial differences in model predictions were as high as seven orders of magnitude. Only after several meetings and discussions about specific assumptions did the differences in predictions by various models merge. Our study shows that parameter uncertainty (as evaluated by a probabilistic Monte Carlo assessment) may have contributed over one order of magnitude to the overall modeling uncertainty. The final model predictions ranged between one and three orders of magnitude, depending on the specific scenario. This study illustrates the importance of problem formulation and implementation of an analytic-deliberative process in risk characterization.     

Use of Fuzzy Evidential Reasoning in Maritime Security Assessment

Over the last few years, there has been a growing international recognition that the security performance of the maritime industry needs to be reviewed on an urgent basis. A large number of optional maritime security control measures have been proposed through various regulations and publications in the post-9/11 era. There is a strong need for a sound and generic methodology, which is capable of taking into account multiple selection criteria such as the cost effectiveness of the measures based on reasonable security assessment. The use of traditional risk assessment and decision-making approaches to deal with potential terrorism threats in a maritime security area reveals two major challenges. They are lack of capability of analyzing security in situations of high-level uncertainty and lack of capability of processing diverse data in a utility form suitable as input to a risk inference mechanism. To deal with such difficulties, this article proposes a subjective security-based assessment and management framework using fuzzy evidential reasoning (ER) approaches. Consequently, the framework can be used to assemble and process subjective risk assessment information on different aspects of a maritime transport system from multiple experts in a systematic way. Outputs of this model can also provide decisionmakers with a transparent tool to evaluate maritime security policy options for a specific scenario in a cost-effective manner.     

An Integrated Risk Assessment Model of Township‐Scaled Land Subsidence Based on an Evidential Reasoning Algorithm and Fuzzy Set Theory

Land subsidence risk assessment (LSRA) is a multi‐attribute decision analysis (MADA) problem and is often characterized by both quantitative and qualitative attributes with various types of uncertainty. Therefore, the problem needs to be modeled and analyzed using methods that can handle uncertainty. In this article, we propose an integrated assessment model based on the evidential reasoning (ER) algorithm and fuzzy set theory. The assessment model is structured as a hierarchical framework that regards land subsidence risk as a composite of two key factors: hazard and vulnerability. These factors can be described by a set of basic indicators defined by assessment grades with attributes for transforming both numerical data and subjective judgments into a belief structure. The factor‐level attributes of hazard and vulnerability are combined using the ER algorithm, which is based on the information from a belief structure calculated by the Dempster‐Shafer (D‐S) theory, and a distributed fuzzy belief structure calculated by fuzzy set theory. The results from the combined algorithms yield distributed assessment grade matrices. The application of the model to the Xixi‐Chengnan area, China, illustrates its usefulness and validity for LSRA. The model utilizes a combination of all types of evidence, including all assessment information—quantitative or qualitative, complete or incomplete, and precise or imprecise—to provide assessment grades that define risk assessment on the basis of hazard and vulnerability. The results will enable risk managers to apply different risk prevention measures and mitigation planning based on the calculated risk states.