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.     

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.     

Decision Making Under Uncertainty—An Example for Seismic Risk Management

Decision-making techniques are used to select the "best" alternatives under multiple and often conflicting criteria. Multicriteria decision making (MCDM) necessitates to incorporate uncertainties in the decision-making process. The major thrust of this article is to extend the framework proposed by Yager^{( 1 )} for multiple decisionmakers and fuzzy utilities (payoffs). In addition, the concept of expert credibility factor is introduced. The proposed approach is demonstrated for an example of seismic risk management using a heuristic hierarchical structure. A step-by-step formulation of the proposed approach is illustrated using a hypothetical example and a three-story reinforced concrete building.     

一类序贯决策方法的提出:透镜与直觉模糊信息的视角

与大多数关注经典秘书问题的序贯决策研究不同,本文针对决策对象不变而其状态更新的一类序贯决策问题,提供决策方法。相较已有研究而言,本文从透镜与直觉模糊信息的视角提出这类序贯决策的一般方法,更加具有普适性,可应用于应急管理、风险投资等多个方向。该决策方法具有如下特点：1）借鉴透镜模型,通过当期可观测的线索,预测这类序贯决策问题的下一期状态;2）考虑到决策环境具有不确定性,采用直觉模糊数对决策者获取的信息线索进行刻画;3）为最大化模拟现实决策者,在前景理论框架下提出状态更新的序贯决策方法。本文提出的决策方法依据在于决策者对决策对象的预测"前景"值和预测绩效,基于此提出了具体的决策步骤,并辅以案例进行分析。通过对不同决策情形下决策结果的比较分析和对案例的稳健性检验,验证了该决策方法的适用性和有效性。     

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.     

Stochastic Dominance: An Approach to Decision Making Under Risk

This paper introduces stochastic dominance as a technique to reduce the set of possible actions that a decision maker must consider in a decision problem under risk. The procedure usually does not choose an optimal action, but instead eliminates certain actions as unacceptable. Very little need be known about the decision maker's utility function. Two possible applications are presented: upgrading buildings to better withstand an earthquake; and choosing a site for a LNG facility.     

Intergenerational Risk Decision Making: A Practical Example

There is no such thing as intergenerational decision making, at least not yet. In fact, there is no such thing as intragenerational decision making in the context of maximizing overall social good given resource limitations, there are just decisions being made in an ad hoc fashion. Even if one assumes that there is such a thing as intragenerational decision making, no uniform standard or guidance exists to make societal decisions for the common good. Risks to society are judged unevenly within the same agency and across agencies. Decisions are made in isolation and not weighed in the societal context of what is intra or intergenerationally important. The National Academy of Public Administration (NAPA) has set forth a framework for intergenerational decision making that provides a consistent and fair basis for making tough decisions in order to address difficult issues such as the long-term disposal of nuclear wastes. NAPA recognizes that there is an intergenerational obligation that must encompass broader questions than the narrow issue of waste disposal since resources are finite and needs are great. The fundamental principles are based on sustainability with the overarching objective that "no generation should needlessly, now or in the future, deprive its successors of the opportunity to enjoy a quality of life equivalent to its own." Coupled with this objective are four supporting principles of trusteeship, sustainability, chain of obligation, and precaution. The NAPA process also recognizes that no decision can be final and that a "rolling future" view is better than making decisions for "all time." It attempts to balance the needs of the present with those of the future in an open and transparent process that is aimed at producing a decision, not just endless analysis. The U.S. Congress and president should develop a rational standard by which to judge laws that involve intra and intergenerational issues relative to the overall societal good. Present regulations need to be evaluated relative to a uniform level of risk and benefit to assess where the limited money available can do the most good for both the present and future generations in the context of NAPA sustainability principles. It is hoped that decision makers will take a serious look at this process since it can work to resolve stakeholder stalemate.     

基于Vague集包含度的模糊多目标决策

将Vague集的包含度应用于模糊条件下的多目标决策问题。首先,定义vague集的包含度,利用正常蕴涵算子给出Vague包含度的一系列具体公式,并通过定义Vague集的基数将模糊集的部分包含度公式推广到Vague集。然后给出基于Vague集包含度的模糊多目标决策方法。最后,通过例子阐明方法的有效性。     

Improving Default Risk Prediction Using Bayesian Model Uncertainty Techniques

Credit risk is the potential exposure of a creditor to an obligor's failure or refusal to repay the debt in principal or interest. The potential of exposure is measured in terms of probability of default. Many models have been developed to estimate credit risk, with rating agencies dating back to the 19th century. They provide their assessment of probability of default and transition probabilities of various firms in their annual reports. Regulatory capital requirements for credit risk outlined by the Basel Committee on Banking Supervision have made it essential for banks and financial institutions to develop sophisticated models in an attempt to measure credit risk with higher accuracy. The Bayesian framework proposed in this article uses the techniques developed in physical sciences and engineering for dealing with model uncertainty and expert accuracy to obtain improved estimates of credit risk and associated uncertainties. The approach uses estimates from one or more rating agencies and incorporates their historical accuracy (past performance data) in estimating future default risk and transition probabilities. Several examples demonstrate that the proposed methodology can assess default probability with accuracy exceeding the estimations of all the individual models. Moreover, the methodology accounts for potentially significant departures from “nominal predictions” due to “upsetting events” such as the 2008 global banking crisis.     

Sensitivity Analysis, Monte Carlo Risk Analysis, and Bayesian Uncertainty Assessment

Standard statistical methods understate the uncertainty one should attach to effect estimates obtained from observational data. Among the methods used to address this problem are sensitivity analysis, Monte Carlo risk analysis (MCRA), and Bayesian uncertainty assessment. Estimates from MCRAs have been presented as if they were valid frequentist or Bayesian results, but examples show that they need not be either in actual applications. It is concluded that both sensitivity analyses and MCRA should begin with the same type of prior specification effort as Bayesian analysis.     

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.     

Risk Attitude,Ambiguity Intolerance and Decision Making: An Exploratory Investigation*

Although recent research has identified attitudes towards ambiguity and risk to be important determinants of choice behavior [8] [18], no prior work jointly assessed the roles of both attitudes. We conducted a laboratory experiment using a real decision scenario and conducted exploratory analyses of the relationship between attitudes towards risk and ambiguity and the decision taken by the subjects. The results support the prediction that attitudes towards both risk and ambiguity affect choice behavior. Our exploratory analyses indicate interesting avenues for future research, including an examination of the decision process itself.     

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.     

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.     

Updating Uncertainty in an Integrated Risk Assessment: Conceptual Framework and Methods

Bayesian methods are presented for updating the uncertainty in the predictions of an integrated Environmental Health Risk Assessment (EHRA) model. The methods allow the estimation of posterior uncertainty distributions based on the observation of different model outputs along the chain of the linked assessment framework. Analytical equations are derived for the case of the multiplicative lognormal risk model where the sequential log outputs (log ambient concentration, log applied dose, log delivered dose, and log risk) are each normally distributed. Given observations of a log output made with a normally distributed measurement error, the posterior distributions of the log outputs remain normal, but with modified means and variances, and induced correlations between successive log outputs and log inputs. The analytical equations for forward and backward propagation of the updates are generally applicable to sums of normally distributed variables. The Bayesian Monte-Carlo (BMC) procedure is presented to provide an approximate, but more broadly applicable method for numerically updating uncertainty with concurrent backward and forward propagation. Illustrative examples, presented for the multiplicative lognormal model, demonstrate agreement between the analytical and BMC methods, and show how uncertainty updates can propagate through a linked EHRA. The Bayesian updating methods facilitate the pooling of knowledge encoded in predictive models with that transmitted by research outcomes (e.g., field measurements), and thereby support the practice of iterative risk assessment and value of information appraisals.     

Quantitative Risk Assessment from Farm to Fork and Beyond: A Global Bayesian Approach Concerning Food-Borne Diseases

A novel approach to the quantitative assessment of food-borne risks is proposed. The basic idea is to use Bayesian techniques in two distinct steps: first by constructing a stochastic core model via a Bayesian network based on expert knowledge, and second, using the data available to improve this knowledge. Unlike the Monte Carlo simulation approach as commonly used in quantitative assessment of food-borne risks where data sets are used independently in each module, our consistent procedure incorporates information conveyed by data throughout the chain. It allows "back-calculation" in the food chain model, together with the use of data obtained "downstream" in the food chain. Moreover, the expert knowledge is introduced more simply and consistently than with classical statistical methods. Other advantages of this approach include the clear framework of an iterative learning process, considerable flexibility enabling the use of heterogeneous data, and a justified method to explore the effects of variability and uncertainty. As an illustration, we present an estimation of the probability of contracting a campylobacteriosis as a result of broiler contamination, from the standpoint of quantitative risk assessment. Although the model thus constructed is oversimplified, it clarifies the principles and properties of the method proposed, which demonstrates its ability to deal with quite complex situations and provides a useful basis for further discussions with different experts in the food chain.     

Decision Making for Group Risk Reduction: Dealing with Epistemic Uncertainty

Group risk is usually represented by FN curves showing the frequency of different accident sizes for a given activity. Many governments regulate group risk through FN criterion lines, which define the tolerable location of an FN curve. However, to compare different risk reduction alternatives, one must be able to rank FN curves. The two main problems in doing this are that the FN curve contains multiple frequencies, and that there are usually large epistemic uncertainties about the curve. Since the mid 1970s, a number of authors have used the concept of “disutility” to summarize FN curves in which a family of disutility functions was defined with a single parameter controlling the degree of “risk aversion.” Here, we show it to be risk neutral, disaster averse, and insensitive to epistemic uncertainty on accident frequencies. A new approach is outlined that has a number of attractive properties. The formulation allows us to distinguish between risk aversion and disaster aversion, two concepts that have been confused in the literature until now. A two‐parameter family of disutilities generalizing the previous approach is defined, where one parameter controls risk aversion and the other disaster aversion. The family is sensitive to epistemic uncertainties. Such disutilities may, for example, be used to compare the impact of system design changes on group risks, or might form the basis for valuing reductions in group risk in a cost‐benefit analysis.     

Towards a Fuzzy Bayesian Network Based Approach for Safety Risk Analysis of Tunnel‐Induced Pipeline Damage

Tunneling excavation is bound to produce significant disturbances to surrounding environments, and the tunnel‐induced damage to adjacent underground buried pipelines is of considerable importance for geotechnical practice. A fuzzy Bayesian networks (FBNs) based approach for safety risk analysis is developed in this article with detailed step‐by‐step procedures, consisting of risk mechanism analysis, the FBN model establishment, fuzzification, FBN‐based inference, defuzzification, and decision making. In accordance with the failure mechanism analysis, a tunnel‐induced pipeline damage model is proposed to reveal the cause‐effect relationships between the pipeline damage and its influential variables. In terms of the fuzzification process, an expert confidence indicator is proposed to reveal the reliability of the data when determining the fuzzy probability of occurrence of basic events, with both the judgment ability level and the subjectivity reliability level taken into account. By means of the fuzzy Bayesian inference, the approach proposed in this article is capable of calculating the probability distribution of potential safety risks and identifying the most likely potential causes of accidents under both prior knowledge and given evidence circumstances. A case concerning the safety analysis of underground buried pipelines adjacent to the construction of the Wuhan Yangtze River Tunnel is presented. The results demonstrate the feasibility of the proposed FBN approach and its application potential. The proposed approach can be used as a decision tool to provide support for safety assurance and management in tunnel construction, and thus increase the likelihood of a successful project in a complex project environment.     

Decision Making with Uncertain Judgments: A Stochastic Formulation of the Analytic Hierarchy Process*

In the analytic hierarchy process (AHP), priorities are derived via a deterministic method, the eigenvalue decomposition. However, judgments may be subject to error. A stochastic characterization of the pairwise comparison judgment task is provided and statistical models are introduced for deriving the underlying priorities. Specifically, a weighted hierarchical multinomial logit model is used to obtain the priorities. Inference is then conducted from the Bayesian viewpoint using Markov chain Monte Carlo methods. The stochastic methods are found to give results that are congruent with those of the eigenvector method in matrices of different sizes and different levels of inconsistency. Moreover, inferential statements can be made about the priorities when the stochastic approach is adopted, and these statements may be of considerable value to a decision maker. The methods described are fully compatible with judgments from the standard version of AHP and can be used to construct a stochastic formulation of it.