My Loss Is Your Loss … Sometimes: Loss Aversion and the Effect of Motivational Biases

Findings from previous studies of individual decision-making behavior predict that losses will loom larger than gains. It is less clear, however, if this loss aversion applies to the way in which individuals attribute value to the gains and losses of others, or if it is robust across a broad spectrum of policy and management decision contexts. Consistent with previous work, the results from a series of experiments reported here revealed that subjects exhibited loss aversion when evaluating their own financial gains and losses. The presence of loss aversion was also confirmed for the way in which individuals attribute value to the financial gains and losses of others. However, similar evaluations within social and environmental contexts did not exhibit loss aversion. In addition, research subjects expected that individuals who were unknown to them would significantly undervalue the subjects' own losses across all contexts. The implications of these findings for risk-based policy and management are many. Specifically, they warrant caution when relying upon loss aversion to explain or predict the reaction of affected individuals to risk-based decisions that involve moral or protected values. The findings also suggest that motivational biases may lead decisionmakers to assume that their attitudes and beliefs are common among those affected by a decision, while those affected may expect unfamiliar others to be unable to identify and act in accordance with shared values.     

Risk Perception and Risk Analysis in a Hyperpartisan and Virtuously Violent World

I shall discuss, from a personal perspective, research on risk perception that has created an understanding of the dynamic interplay between an appreciation of risk that resides in us as a feeling and an appreciation of risk that results from analysis. In some circumstances, feelings reflect important social values that deserve to be considered along with traditional analyses of physical and economic risk. In other situations, both feelings and analyses may be shaped by powerful cognitive biases and deep social and partisan prejudices, causing nonrational judgments and decisions. This is of concern if risk analysis is to be applied, as it needs to be, in managing existential threats such as pandemic disease, climate change, or nuclear weapons amidst a divisive political climate.     

The Role of Decision Analysis in Risk Analysis: A Retrospective

In commemorating the 40th anniversary of Risk Analysis, this article takes a retrospective look at some of the ways in which decision analysis (as a “sibling field”) has contributed to the development both of the journal, and of risk analysis as a field. I begin with some early foundational papers from the first decade of the journal's history. I then review a number of papers that have applied decision analysis to risk problems over the years, including applications of related methods such as influence diagrams, multicriteria decision analysis, and risk matrices. The article then reviews some recent trends, from roughly the last five years, and concludes with observations about the parallel evolution of risk analysis and decision analysis over the decades—especially with regard to the importance of representing multiple stakeholder perspectives, and the importance of behavioral realism in decision models. Overall, the extensive literature surveyed here supports the view that the incorporation of decision-analytic perspectives has improved the practice of risk analysis.     

Decision Analysis and Risk Management Decision Making: Issues and Methods

This paper provides an overview of decision analysis and its use in risk management decision making. The paper discusses the distinctive characteristics of decision analysis and compares these characteristics with those of its principal alternative—cost–benefit analysis. The paper also discusses each of the steps in a decision analysis and the strengths and limitations of the method.     

商业银行操作风险外部数据的内生偏差研究

本文在总结目前商业银行操作风险管理现状的基础上,阐述了建立操作损失数据库的必要性和意义内部数据对商业银行至关重要,然而内部损失数据总是不足的,有必要结合外部数据度量操作风险,尤其对于中国商业银行历史操作风险损失数据奇缺的情况。本文从国际银行界的角度总结了操作风险外部损失数据库的类型,分析了不同类型外部数据存在的内生偏差及可能的修正措施,提供了处理外部数据进行操作风险度量的思路。     

Ethics, Decision Analysis, and Public Risk

In any analysis of a decision problem involving public risks, ethical implications are introduced. In some cases, these ethical implications may be introduced simply because an analysis is being done. Additional ethical implications may be inherently part of the methodology being utilized or introduced into the specific analysis of the decision problem. In this paper, we investigate where and how ethical implications enter when using the methodology of decision analysis to examine problems involving public risks. We conclude that the methodology of decision analysis is sufficiently robust to allow for numerous different ethical viewpoints to be accounted for in any specific analysis. Stated alternatively, decision analyses of public risks can be conducted in a manner consistent with utilitarianism, deontological theories, libertarianism, egalitarianism, and so forth. However, any specific analysis has embedded within it numerous ethical implications. This suggests that the careful ethical scrutiny of analyses involving the methodology of decision analysis should be placed on the specific application and not on the methodology per se or on the fact that an analysis is undertaken.     

A Decision Framework for Managing Risk to Airports from Terrorist Attack

This article presents an asset‐level security risk management framework to assist stakeholders of critical assets with allocating limited budgets for enhancing their safety and security against terrorist attack. The proposed framework models the security system of an asset, considers various threat scenarios, and models the sequential decision framework of attackers during the attack. Its novel contributions are the introduction of the notion of partial neutralization of attackers by defenders, estimation of total loss from successful, partially successful, and unsuccessful actions of attackers at various stages of an attack, and inclusion of the effects of these losses on the choices made by terrorists at various stages of the attack. The application of the proposed method is demonstrated in an example dealing with security risk management of a U.S. commercial airport, in which a set of plausible threat scenarios and risk mitigation options are considered. It is found that a combination of providing blast‐resistant cargo containers and a video surveillance system on the airport perimeter fence is the best option based on minimum expected life‐cycle cost considering a 10‐year service period.     

Does a Reactor Need a Safety Backfit? Case Study on Communicating Decision and Risk Analysis Information to Managers

An approach to communicating decision and risk analysis findings to managers is illustrated in a real case context. This article consists essentially of a report prepared for senior managers of the Nuclear Regulatory Commission to help them make a reactor safety decision. It illustrates the communication of decision analysis findings relating to technical risks, costs, and benefits in support of a major risk management decision: whether or not to require a safety backfit. Its focus is on the needs of decision makers, and it introduces some novel communication devices.     

Multicriteria Decision Analysis: A Comprehensive Decision Approach for Management of Contaminated Sediments

Contaminated sediments and other sites present a difficult challenge for environmental decisionmakers. They are typically slow to recover or attenuate naturally, may involve multiple regulatory agencies and stakeholder groups, and engender multiple toxicological and ecotoxicological risks. While environmental decision-making strategies over the last several decades have evolved into increasingly more sophisticated, information-intensive, and complex approaches, there remains considerable dissatisfaction among business, industry, and the public with existing management strategies. Consequently, contaminated sediments and materials are the subject of intense technology development, such as beneficial reuse or in situ treatment. However, current decision analysis approaches, such as comparative risk assessment, benefit-cost analysis, and life cycle assessment, do not offer a comprehensive approach for incorporating the varied types of information and multiple stakeholder and public views that must typically be brought to bear when new technologies are under consideration. Alternatively, multicriteria decision analysis (MCDA) offers a scientifically sound decision framework for management of contaminated materials or sites where stakeholder participation is of crucial concern and criteria such as economics, environmental impacts, safety, and risk cannot be easily condensed into simple monetary expressions. This article brings together a multidisciplinary review of existing decision-making approaches at regulatory agencies in the United States and Europe and synthesizes state-of-the-art research in MCDA methods applicable to the assessment of contaminated sediment management technologies. Additionally, it tests an MCDA approach for coupling expert judgment and stakeholder values in a hypothetical contaminated sediments management case study wherein MCDA is used as a tool for testing stakeholder responses to and improving expert assessment of innovative contaminated sediments technologies.     

A Multicriteria Decision Analysis Model and Risk Assessment Framework for Carbon Capture and Storage

Multicriteria decision analysis (MCDA) has been applied to various energy problems to incorporate a variety of qualitative and quantitative criteria, usually spanning environmental, social, engineering, and economic fields. MCDA and associated methods such as life‐cycle assessments and cost‐benefit analysis can also include risk analysis to address uncertainties in criteria estimates. One technology now being assessed to help mitigate climate change is carbon capture and storage (CCS). CCS is a new process that captures CO₂ emissions from fossil‐fueled power plants and injects them into geological reservoirs for storage. It presents a unique challenge to decisionmakers (DMs) due to its technical complexity, range of environmental, social, and economic impacts, variety of stakeholders, and long time spans. The authors have developed a risk assessment model using a MCDA approach for CCS decisions such as selecting between CO₂ storage locations and choosing among different mitigation actions for reducing risks. The model includes uncertainty measures for several factors, utility curve representations of all variables, Monte Carlo simulation, and sensitivity analysis. This article uses a CCS scenario example to demonstrate the development and application of the model based on data derived from published articles and publicly available sources. The model allows high‐level DMs to better understand project risks and the tradeoffs inherent in modern, complex energy decisions.     

电视购物消费者知觉风险与购买决策行为分析

电视购物作为一种无店铺营销的商业零售模式,在国内的发展中一直深受信任危机的困扰。本研究引入心理学知觉风险的概念,通过调查目前消费者对电视购物的风险知觉及其购买行为,来了解目前国内电视购物的消费者对电视购物有怎样的风险知觉,消费者对电视购物的风险知觉对其购买决策有怎样的影响,以及消费者特征、消费者对电视购物的风险知觉和其购买决策行为之间的关系和影响作用。     

A New Approach to Hazardous Materials Transportation Risk Analysis: Decision Modeling to Identify Critical Variables

We take a novel approach to analyzing hazardous materials transportation risk in this research. Previous studies analyzed this risk from an operations research (OR) or quantitative risk assessment (QRA) perspective by minimizing or calculating risk along a transport route. Further, even though the majority of incidents occur when containers are unloaded, the research has not focused on transportation-related activities, including container loading and unloading. In this work, we developed a decision model of a hazardous materials release during unloading using actual data and an exploratory data modeling approach. Previous studies have had a theoretical perspective in terms of identifying and advancing the key variables related to this risk, and there has not been a focus on probability and statistics-based approaches for doing this. Our decision model empirically identifies the critical variables using an exploratory methodology for a large, highly categorical database involving latent class analysis (LCA), loglinear modeling, and Bayesian networking. Our model identified the most influential variables and countermeasures for two consequences of a hazmat incident, dollar loss and release quantity , and is one of the first models to do this. The most influential variables were found to be related to the failure of the container. In addition to analyzing hazmat risk, our methodology can be used to develop data-driven models for strategic decision making in other domains involving risk.     

Entropy Principles in Decision Making Under Risk

This paper considers decision problems where: (1) The exact probability distribution over the states of nature is not precisely known, but certain prior information is available about the possibilities of these outcomes; (2) A prior distribution over the states of nature is known, but new constraint information about the probabilities becomes available. The maximum entropy principle asserts that the probability distribution with maximum entropy, satisfying the prior knowledge, should be used in the decision problem. The minimum cross-entropy principle says that the posterior distribution is the one which minimizes cross-entropy, subject to the new constraint information. The entropy principles have not gone uncriticized, and this literature, together with that justifying the principles, is surveyed. Both principles are illustrated in a number of situations where the distribution is either discrete or continuous. The discrete distribution case with prior interval estimates based on expert opinions is considered in detail.     

信用评分模型的设计与决策分析

本文对商业银行信贷业务中的贷款者的信用问题,运用决策分析提出了信用评分的设计原理及临界分值的确定方法,给出了最优贷款策略的期望收益和风险的计算公式。最后,以一例说明之。     

The Effect of Cognitive Load on Decision Making with Graphically Displayed Uncertainty Information

An experiment examined the ability of five graphical displays to communicate uncertainty information when end users were under cognitive load (i.e., remembering an eight‐digit number). The extent to which people could accurately derive information from the graphs and the adequacy of decisions about optimal behaviors based on the graphs were assessed across eight scenarios in which probabilistic outcomes were described. Results indicated that the load manipulation did not have an overall effect on derivation of information from the graphs (i.e., mean and probability estimation) but did suppress the ability to optimize behavioral choices based on the graph. Cognitive load affected people's use of some graphical displays (basic probability distribution function) more than others. Overall, the research suggests that interpreting basic characteristics of uncertainty data is unharmed under conditions of limited cognitive resources, whereas more deliberative processing is negatively affected.     

The Cry Wolf Effect and Weather‐Related Decision Making

Despite improvements in forecasting extreme weather events, noncompliance with weather warnings among the public remains a problem. Although there are likely many reasons for noncompliance with weather warnings, one important factor might be people's past experiences with false alarms. The research presented here explores the role of false alarms in weather‐related decision making. Over a series of trials, participants used an overnight low temperature forecast and advice from a decision aid to decide whether to apply salt treatment to a town's roads to prevent icy conditions or take the risk of withholding treatment, which resulted in a large penalty when freezing temperatures occurred. The decision aid gave treatment recommendations, some of which were false alarms, i.e., treatment was recommended but observed temperatures were above freezing. The rate at which the advice resulted in false alarms was manipulated between groups. Results suggest that very high and very low false alarm rates led to inferior decision making, but that lowering the false alarm rate slightly did not significantly affect compliance or decision quality. However, adding a probabilistic uncertainty estimate in the forecasts improved both compliance and decision quality. These findings carry implications about how weather warnings should be communicated to the public.     

What's Time Got to Do with It? Inattention to Duration in Interpretation of Survival Graphs

Reports of randomized clinical trials often use survival curves to summarize clinical outcomes over time and graphically demonstrate evidence of treatment effectiveness. Survival curves can also be used in patient communications to display how health risks accumulate over time. In a randomized survey experiment, administered online, we tested whether people viewing survival curves appropriately adjust their risk perceptions to account for the duration shown. Internet users (N= 864) were recruited from a demographically balanced U.S. panel. Participants read about a hypothetical disease and then viewed one of four survival graphs that displayed mortality risks with and without treatment. Survival graphs showed either a visually large or visually small difference between treatments and were labeled to represent either 5-year or 15-year risk statistics. Participants then provided ratings of disease seriousness, as well as treatment effectiveness for each possible treatment. Variations in ratings corresponded more with visual dissimilarity than with changes in the statistical risk exhibited, with participants perceiving somewhat greater disease seriousness and significant differences in treatment effectiveness in large visual difference graphs. We conclude that when people interpret survival curves, they often fail to sufficiently account for the timeframe represented and perceive more risk and larger differences when identical risks are displayed over longer periods of time. We recommend that all presentations of survival graphics, whether to patients, physicians, or scientists, emphasize duration information (e.g., in the title) and remind readers that attending to graph axis labels is the only way to pierce these visual illusions.     

Use of Advances in Technology for Maritime Risk Assessment

The maritime industry is moving toward a "goal-setting" risk-based regime. This opens the way to safety engineers to explore and exploit flexible and advanced risk modeling and decision-making approaches in the design and operation processes. In this article, following a brief review of the current status of maritime risk assessment, a design/operation selection framework and a design/operation optimization framework are outlined. A general discussion of control engineering techniques and their application to risk modeling and decision making is given. Four novel risk modeling and decision-making approaches are then outlined with illustrative examples to demonstrate their use. Such approaches may be used as alternatives to facilitate risk modeling and decision making in situations where conventional techniques cannot be appropriately applied. Finally, recommendations on further exploitation of advances in general engineering and technology are suggested with respect to risk modeling and decision making.     

Probabilistic Risk Analysis and Terrorism Risk

Since the terrorist attacks of September 11, 2001, and the subsequent establishment of the U.S. Department of Homeland Security (DHS), considerable efforts have been made to estimate the risks of terrorism and the cost effectiveness of security policies to reduce these risks. DHS, industry, and the academic risk analysis communities have all invested heavily in the development of tools and approaches that can assist decisionmakers in effectively allocating limited resources across the vast array of potential investments that could mitigate risks from terrorism and other threats to the homeland. Decisionmakers demand models, analyses, and decision support that are useful for this task and based on the state of the art. Since terrorism risk analysis is new, no single method is likely to meet this challenge. In this article we explore a number of existing and potential approaches for terrorism risk analysis, focusing particularly on recent discussions regarding the applicability of probabilistic and decision analytic approaches to bioterrorism risks and the Bioterrorism Risk Assessment methodology used by the DHS and criticized by the National Academies and others.