An Integrated Framework for Environmental Multi‐Impact Spatial Risk Analysis

Quantitative risk analysis is being extensively employed to support policymakers and provides a strong conceptual framework for evaluating decision alternatives under uncertainty. Many problems involving environmental risks are, however, of a spatial nature, i.e., containing spatial impacts, spatial vulnerabilities, and spatial risk‐mitigation alternatives. Recent developments in multicriteria spatial analysis have enabled the assessment and aggregation of multiple impacts, supporting policymakers in spatial evaluation problems. However, recent attempts to conduct spatial multicriteria risk analysis have generally been weakly conceptualized, without adequate roots in quantitative risk analysis. Moreover, assessments of spatial risk often neglect the multidimensional nature of spatial impacts (e.g., social, economic, human) that are typically occurring in such decision problems. The aim of this article is therefore to suggest a conceptual quantitative framework for environmental multicriteria spatial risk analysis based on expected multi‐attribute utility theory. The framework proposes: (i) the formal assessment of multiple spatial impacts; (ii) the aggregation of these multiple spatial impacts; (iii) the assessment of spatial vulnerabilities and probabilities of occurrence of adverse events; (iv) the computation of spatial risks; (v) the assessment of spatial risk mitigation alternatives; and (vi) the design and comparison of spatial risk mitigation alternatives (e.g., reductions of vulnerabilities and/or impacts). We illustrate the use of the framework in practice with a case study based on a flood‐prone area in northern Italy.     

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.     

Assessment of Catastrophic Risk Using Bayesian Network Constructed from Domain Knowledge and Spatial Data

Prediction of natural disasters and their consequences is difficult due to the uncertainties and complexity of multiple related factors. This article explores the use of domain knowledge and spatial data to construct a Bayesian network (BN) that facilitates the integration of multiple factors and quantification of uncertainties within a consistent system for assessment of catastrophic risk. A BN is chosen due to its advantages such as merging multiple source data and domain knowledge in a consistent system, learning from the data set, inference with missing data, and support of decision making. A key advantage of our methodology is the combination of domain knowledge and learning from the data to construct a robust network. To improve the assessment, we employ spatial data analysis and data mining to extend the training data set, select risk factors, and fine‐tune the network. Another major advantage of our methodology is the integration of an optimal discretizer, informative feature selector, learners, search strategies for local topologies, and Bayesian model averaging. These techniques all contribute to a robust prediction of risk probability of natural disasters. In the flood disaster's study, our methodology achieved a better probability of detection of high risk, a better precision, and a better ROC area compared with other methods, using both cross‐validation and prediction of catastrophic risk based on historic data. Our results suggest that BN is a good alternative for risk assessment and as a decision tool in the management of catastrophic risk.     

Use of Multicriteria Decision Analysis to Support Weight of Evidence Evaluation

Weight of evidence (WOE) methods are key components of ecological and human health risk assessments. Most WOE applications rely on the qualitative integration of diverse lines of evidence (LOE) representing impact on ecological receptors and humans. Recent calls for transparency in assessments and justifiability of management decisions are pushing the community to consider quantitative methods for integrated risk assessment and management. This article compares and contrasts the type of information required for application of individual WOE techniques and the outcomes that they provide in ecological risk assessment and proposes a multicriteria decision analysis (MCDA) framework for integrating individual LOE in support of management decisions. The use of quantitative WOE techniques is illustrated for a hypothetical but realistic case study of selecting remedial alternatives at a contaminated aquatic site. Use of formal MCDA does not necessarily eliminate biases and judgment calls necessary for selecting remedial alternatives, but allows for transparent evaluation and fusion of individual LOE. It also provides justifiable methods for selecting remedial alternatives consistent with stakeholder and decision‐maker values.     

Assessing financial risks using a multicriteria sorting procedure: the case of country risk assessment

The assessment of financial risks is a problem of major interest for corporate entities (organizations, financial institutions, firms, etc.). The vulnerable economic and financial environments necessitate the development of operational approaches to measure and control financial risks. Most of the methodologies that have been proposed in the past employ a probabilistic notion of risk. This paper proposes an alternative approach to measure financial risks, considering their multidimensional nature. The proposed approach is based on the multicriteria decision aid (MCDA) method Multi-Group Hierarchical DIScrimination (M.H.DIS). The aim of the M.H.DIS method within the financial risk assessment context is to develop a set of additive utility functions that classify the considered alternatives (firms, investment projects, portfolios, countries, etc.) into predefined risk classes. The efficiency of the method is illustrated through a case study regarding the country risk assessment problem. Using the M.H.DIS method a discrimination model is developed that classifies the countries into four groups, and measures the corresponding creditworthiness and risk of the countries. Several validation tests are performed in order to compare the classification results obtained through M.H.DIS to the results obtained through multiple discriminant analysis.     

Understanding the Elusive Black Box of Dynamic Capabilities

A major challenge for managers in turbulent environments is to make sound decisions quickly. Dynamic capabilities have been proposed as a means for addressing turbulent environments by helping managers extend, modify, and reconfigure existing operational capabilities into new ones that better match the environment. However, because dynamic capabilities have been viewed as an elusive black box, it is difficult for managers to make sound decisions in turbulent environments if they cannot effectively measure dynamic capabilities. Therefore, we first seek to propose a measurable model of dynamic capabilities by conceptualizing, operationalizing, and measuring dynamic capabilities. Specifically, drawing upon the dynamic capabilities literature, we identify a set of capabilities—sensing the environment, learning, coordinating, and integrating—that help reconfigure existing operational capabilities into new ones that better match the environment. Second, we propose a structural model where dynamic capabilities influence performance by reconfiguring existing operational capabilities in the context of new product development (NPD). Data from 180 NPD units support both the measurable model of dynamic capabilities and also the structural model by which dynamic capabilities influence performance in NPD by reconfiguring operational capabilities, particularly in higher levels of environmental turbulence. The study's implications for managerial decision making in turbulent environments by capturing the elusive black box of dynamic capabilities are discussed.     

商业银行信用风险评估预测模型研究

依据商业银行信用风险的内涵,指出信用风险评估应当充分考虑信贷资金安全系数的不 确定性和信用风险的相对性特征,并以“信用风险度”作为系统的输出,构建了基于人工神经网 络的信用风险评估预测模型,为有效转变信用风险的分类评估模式,提供更为全面的信贷决策 支持奠定了基础.     

Understanding Community Resilience from a PRA Perspective Using Binary Decision Diagrams

Probabilistic risk assessment (PRA) is a useful tool to assess complex interconnected systems. This article leverages the capabilities of PRA tools developed for industrial and nuclear risk analysis in community resilience evaluations by modeling the food security of a community in terms of its built environment as an integrated system. To this end, we model the performance of Gilroy, CA, a moderate‐size town, with regard to disruptions in its food supply caused by a severe earthquake. The food retailers of Gilroy, along with the electrical power network, water network elements, and bridges are considered as components of a system. Fault and event trees are constructed to model the requirements for continuous food supply to community residents and are analyzed efficiently using binary decision diagrams (BDDs). The study also identifies shortcomings in approximate classical system analysis methods in assessing community resilience. Importance factors are utilized to rank the importance of various factors to the overall risk of food insecurity. Finally, the study considers the impact of various sources of uncertainties in the hazard modeling and performance of infrastructure on food security measures. The methodology can be applicable for any existing critical infrastructure system and has potential extensions to other hazards.     

Enhancing knowledge construction processes within multicriteria decision analysis: The Collaborative Value Modelling framework

This paper proposes a new Collaborative Value Modelling framework, that combines Delphi and multicriteria decision conferencing, to build widely informed evaluation models. Multicriteria Decision Analysis (MCDA) is commonly used to help decision-makers and other stakeholders in complex evaluation contexts. Further to the technical soundness and meaningfulness of the methods and tools used, it is critical to design adequate social processes to promote shared understanding around key evaluation issues while capturing multiple stakeholders’ values and perspectives. Multicriteria decision conferencing processes have been typically adopted for collaborative modelling using MCDA methods in decision conferences with relatively small groups. Such a socio-technical approach has proven to be effective, in a variety of contexts, in creating a collaborative environment that enables surfacing individual beliefs, identifying common concerns, managing eventual value conflicts and promoting agreement in group model building. But, extending this framework to broader participatory contexts requires a different design of the social process, in order to ensure that model building captures the full panoply of points of view. This challenge can be tackled by enhancing multicriteria decision conferencing with an all-embracing (Web-)Delphi participatory process. We depart from the existing collaborative knowledge acquisition methodology to design, with the Delphi method, a participatory knowledge construction process that elicits and analyses individual judgemental knowledge from a (very) large and diverse number of stakeholders. The knowledge acquired is then digested by a small group of key-players, in a subsequent decision conferencing, to collaboratively develop a widely informed multicriteria evaluation model. This new Web-Delphi-decision conferencing social setting has been tested already in real complex evaluation contexts using a specific multicriteria method, the Measuring Attractiveness by a Categorical Based Evaluation Technique (MACBETH), to develop a variety of value modelling activities. We call this socio-technical design the Collaborative Value Modelling framework. Here, we describe its real use to support the construction of value functions, focusing on how the judgemental knowledge collected flows between the participatory and collaborative stages of the framework. Results validate that enhancing MACBETH decision conferencing with an ex ante Web-Delphi process fosters higher participation and collaboration in multicriteria modelling.     

Tiered Approach to Resilience Assessment

Regulatory agencies have long adopted a three‐tier framework for risk assessment. We build on this structure to propose a tiered approach for resilience assessment that can be integrated into the existing regulatory processes. Comprehensive approaches to assessing resilience at appropriate and operational scales, reconciling analytical complexity as needed with stakeholder needs and resources available, and ultimately creating actionable recommendations to enhance resilience are still lacking. Our proposed framework consists of tiers by which analysts can select resilience assessment and decision support tools to inform associated management actions relative to the scope and urgency of the risk and the capacity of resource managers to improve system resilience. The resilience management framework proposed is not intended to supplant either risk management or the many existing efforts of resilience quantification method development, but instead provide a guide to selecting tools that are appropriate for the given analytic need. The goal of this tiered approach is to intentionally parallel the tiered approach used in regulatory contexts so that resilience assessment might be more easily and quickly integrated into existing structures and with existing policies.     

Multicriteria decision support for global e-government evaluation

E-government refers to the use of information and communication technologies (ICT) by governments to provide digital services to citizens and businesses over the Internet, at local, national or international level. Benchmarking and assessing e-government is therefore necessary to monitor performance and progress by individual countries and identify areas for improvement. Although such measurements have already been initiated by various organizations, they scarcely highlight the multidimensional nature of the assessment. This paper outlines a multicriteria methodology to evaluate e-government using a system of eight evaluation criteria that are built on four points of view: (1) infrastructures, (2) investments, (3) e-processes, and (4) users’ attitude. The overall evaluation is obtained through an additive value model which is assessed with the involvement of a single decision maker–evaluator and the use of a multicriteria ordinal regression approach. Specifically, the UTA II method is used, whose interactive application process is divided in two phases. Its implementation is supported by MIIDAS (multicriteria interactive intelligent decision aiding system). This research work aims at supporting potential stakeholders to perform a global e-government evaluation, based on their own viewpoints and preferences. Finally, 21 European countries are evaluated and ranked considering the latest criteria data.     

Beyond Markowitz with multiple criteria decision aiding

The paper is about portfolio selection in a non-Markowitz way, involving uncertainty modeling in terms of a series of meaningful quantiles of probabilistic distributions. Considering the quantiles as evaluation criteria of the portfolios leads to a multiobjective optimization problem which needs to be solved using a Multiple Criteria Decision Aiding (MCDA) method. The primary method we propose for solving this problem is an Interactive Multiobjective Optimization (IMO) method based on so-called Dominance-based Rough Set Approach (DRSA). IMO-DRSA is composed of two phases: computation phase, and dialogue phase. In the computation phase, a sample of feasible portfolio solutions is calculated and presented to the Decision Maker (DM). In the dialogue phase, the DM indicates portfolio solutions which are relatively attractive in a given sample; this binary classification of sample portfolios into ‘good’ and ‘others’ is an input preference information to be analyzed using DRSA; DRSA is producing decision rules relating conditions on particular quantiles with the qualification of supporting portfolios as ‘good’; a rule that best fits the current DM’s preferences is chosen to constrain the previous multiobjective optimization in order to compute a new sample in the next computation phase; in this way, the computation phase yields a new sample including better portfolios, and the procedure loops a necessary number of times to end with the most preferred portfolio. We compare IMO-DRSA with two representative MCDA methods based on traditional preference models: value function (UTA method) and outranking relation (ELECTRE IS method). The comparison, which is of methodological nature, is illustrated by a didactic example.     

Risk Assessment Can Be a Game‐Changing Information Technology—But Too Often It Isn't

The printing press was a game‐changing information technology. Risk assessment could be also. At present, risk assessments are commonly used as one‐time decision aids: they provide justification for a particular decision, and afterwards usually sit on a shelf. However, when viewed as information technologies, their potential uses are much broader. Risk assessments: (1) are repositories of structured information and a medium for communication; (2) embody evaluative structures for setting priorities; (3) can preserve information over time and permit asynchronous communication, thus encouraging learning and adaptation; and (4) explicitly address uncertain futures. Moreover, because of their “what‐if” capabilities, risk assessments can serve as a platform for constructive discussion among parties that hold different values. The evolution of risk assessment in the nuclear industry shows how such attributes have been used to lower core‐melt risks substantially through improved templates for maintenance and more effective coordination with regulators (although risk assessment has been less commonly used in improving emergency‐response capabilities). The end result of this evolution in the nuclear industry has been the development of “living” risk assessments that are updated more or less in real time to answer even routine operational questions. Similar but untapped opportunities abound for the use of living risk assessments to reduce risks in small operational decisions as well as large policy decisions in other areas of hazard management. They can also help improve understanding of and communication about risks, and future risk assessment and management. Realization of these opportunities will require significant changes in incentives and active promotion by the risk analytic community.     

Concepts,Theory, and Techniques DISTRIBUTED COMPUTER SYSTEM DESIGN: A MULTICRITERIA DECISION-MAKING METHODOLOGY*

The design of distributed computer systems (DCSs) requires compromise among several conflicting objectives. For instance, high system availability conflicts with low cost which in turn conflicts with quick response time. This paper presents an approach, based on multi-criteria decision-making techniques, to arrive at a good design in this multiobjective environment. An interactive procedure is developed to support the decision making of system designers. Starting from an initial solution, the procedure presents a sequence of non-dominated vectors to designers, allowing them to explore systematically alternative possibilities on the path to a final design. The model user has control over trade-offs among different design objectives. This paper focuses on the details of the mathematical model used to provide decision support. Accordingly, a formulation of DCS design as a multicriteria decision problem is developed. The exchange search heuristic used to generate nondominated solutions also is presented. We argue that multicriteria models provide a more realistic formulation of the DCS design problem than the single-criterion models used widely in the literature. While obtaining a clear definition of design objectives (single or multiple) is an important activity, by explicitly acknowledging the trade-offs among multiple objectives in the design process, our methodology is more likely to produce a better overall design than methods addressing a single criterion in isolation.     

A Prototype Expert Decision Support System for the Market Appraisal of the Single Family Residence

The assessment of the current market value of residential property is a potential source of anxiety for the typical homeowner. Common criticisms of the existing appraisal process include excessive subjectivity and inconsistency across properties and through time. This paper examines the nature of decision support required for valuation decisions and the appropriateness of applying expert system technologies to evaluate the recoverable value of the single-family residence. The expert system approach allows for the integration of the qualitative and quantitative aspects of appraisal, while introducing the beneficial dimensions of increased objectivity, comprehensiveness, and consistency. A prototype expert system is offered that enables the end user to conduct a market analysis of a subject property. Unlike most expert system applications that seek a diagnostic or classification response, this study explores a numerical intent for the system, in a decision-making environment that is traditionally viewed as highly judgmental. The system's early validation results show promise of proving effective as such an evaluation aid.     

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.     

A Meta-Goal Programming approach to cardinal preferences aggregation in multicriteria problems

Solving multicriteria decision making problems often requires the assessment of certain preferential information. In some occasions, this information must be given by several individuals or social groups, and these individual assessments need to be aggregated into single global preferences. This cardinal preferences aggregation problem has been tackled using different techniques, including multicriteria decision making ones. In this paper, a Meta-Goal Programming approach is proposed, where different target values can be set on several achievement functions that measure the goodness of the global assessments. This methodology presents strong advantages due to its modeling flexibility and its ability to find balanced solutions. The proposed approach is demonstrated with an illustrative example and a series of computational experiments, and it is shown that the Meta-Goal Programming method produces results with better values of the achievement functions than other classical and multicriteria approaches.     

A multicriteria outranking approach for modeling corporate credit ratings: An application of the Electre Tri-nC method

Corporate credit ratings are widely used in financial services for risk management, investment, and financing decisions. In this study, the use of a recently developed multicriteria outranking approach, namely the Electre Tri-nC method, is examined for constructing internal credit rating models in an expert-based judgmental framework. The models are constructed in a multicriteria classification (sorting) setting and the results are analyzed in terms of their internal properties as well as their deviations from risk rating categories defined by rating agencies (i.e. external benchmarking). A simulation/scenario analysis is conducted to examine the results and performance of the outranking models in relation to their parameters. Empirical results are provided for a sample of European firms rated by three leading rating agencies.