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.     

RANK GENERATION,PRESERVATION, AND REVERSAL IN THE ANALYTIC HIERARCHY DECISION PROCESS

Decision making has the objective of finding the best alternative or set of alternatives by considering a number of goals, objectives, criteria, competitors, and other important factors. The analytic hierarchy process is a decision aid used to assist a decision maker in sorting out the complexity of a decision problem and making use of his or her judgments. A decision maker must be assured that the arithmetic operations of any such decision process are the right ones—that they surface the correct ranking and values of the alternatives and preserve or alter ranks appropriately when new alternatives are added or deleted. In this paper it will be shown that with absolute measurement, rank always is preserved, with relative measurement, rank changes with nspect to scveral criteria only because of the structural dependence (involving both numbers and measurements) of criteria on alternatives. A discussion of the effect on rank of replicas and near replicas of the alternatives also is given.     

Multiple criteria decision making for maritime investment policy in the developing countries

This model for multiple criteria decision making enables one to select an optimal investment policy for developing the marine industry in developing countries. The model is composed of: the mathematical model of national economy (LP model) for optimizing an investment scheme under economic and technological constraints; the model of I-O analysis for estimating the change of industrial structures induced by the investment; and the decision making model to which the results of the preceding models are fed back so as to enable the policy planner to select a feasible policy based on his own multiple criteria such as the value of GDP growth rate, international payments, unemployment rate, etc. Due consideration is paid in this study to the role of the marine industries, e.g. shipping and ports, which can be said to be the pivot of economic development in a developing country in connection with its foreign trade policy. A case study is carried out to verify this hypothesis.     

“庄家”与信息披露的质量

采用时间系列研究方法 ,本文通过研究公司信息披露与股票价格和交易量之间的关系 ,探讨了“庄家”的存在对信息披露的质量 (信息含量 )的影响 ,我们的研究结果表明“庄家”的存在降低了信息披露的质量。另外 ,我们还发现不同类型的信息披露对股票价格及交易量的影响不同 ,其中以会计盈余及盈余相关信息的影响最为显著;信息披露与价格的相关性高于与交易量的相关性     

Trust, the Asymmetry Principle, and the Role of Prior Beliefs

Within the risk literature there is an ongoing debate on whether trust is vulnerable or enduring. Previous research on nuclear energy by Slovic in 1993 has shown that negative events have much greater impact on self-reported trust than do positive events. Slovic attributes this to the asymmetry principle: specifically, that trust is much easier to destroy than to create. In a questionnaire survey concerning genetically modified (GM) food in Britain (n= 396) we similarly find that negative events have a greater impact on trust than positive events. Because public opinion in Britain is skewed in the direction of opposition toward GM food, the pattern of results could either be caused by the fact that negative information is more informative than positive information (a negativity bias) or reflect the influence of people's prior attitudes toward the issue (a confirmatory bias). The results were largely in line with the confirmatory bias hypothesis: participants with clear positive or negative beliefs interpreted events in line with their existing attitude position. However, for participants with intermediate attitudes, negative items still had greater impact than the positive. This latter finding suggests that, congruent with the negativity bias hypothesis, negative information may still be more informative than positive information for undecided people. The study also identified the labeling of GM products, consulting the public, making biotechnology companies liable for any damage, and making a test available to detect GM produce as being particularly important preconditions for maintaining trust in the regulation of agricultural biotechnology.     

基于机场繁忙程度的航班延误波及分析

航班延误是全球航空业面临的一大难题。航班运行过程中,对于执行航班环任务的飞机,机场繁忙程度直接影响飞机过站时间长短,进而影响航班离港延误程度。文中构建到港延误对离港延误的波及贝叶斯网络预测模型时,加入机场繁忙程度这一因素,以机场飞机起降架次作为刻画该因素的指标,并通过贝叶斯网络结构学习得到机场繁忙程度的影响关系图。10次10折交叉验证的结果表明,与直接用到港延误预测离港延误的模型相比,加入机场繁忙程度因素的模型能够更准确地预估航班延误波及情况。     

A national risk analysis model (NRAM) for the assessment of COVID-19 epidemic

COVID-19 has caused a critical health concern and severe economic crisis worldwide. With multiple variants, the epidemic has triggered waves of mass transmission for nearly 3 years. In order to coordinate epidemic control and economic development, it is important to support decision-making on precautions or prevention measures based on the risk analysis for different countries. This study proposes a national risk analysis model (NRAM) combining Bayesian network (BN) with other methods. The model is built and applied through three steps. (1) The key factors affecting the epidemic spreading are identified to form the nodes of BN. Then, each node can be assigned state values after data collection and analysis. (2) The model (NRAM) will be built through the determination of the structure and parameters of the network based on some integrated methods. (3) The model will be applied to scenario deduction and sensitivity analysis to support decision-making in the context of COVID-19. Through the comparison with other models, NRAM shows better performance in the assessment of spreading risk at different countries. Moreover, the model reveals that the higher education level and stricter government measures can achieve better epidemic prevention and control effects. This study provides a new insight into the prevention and control of COVID-19 at the national level.     

A Bayesian Method to Mine Spatial Data Sets to Evaluate the Vulnerability of Human Beings to Catastrophic Risk

Vulnerability of human beings exposed to a catastrophic disaster is affected by multiple factors that include hazard intensity, environment, and individual characteristics. The traditional approach to vulnerability assessment, based on the aggregate‐area method and unsupervised learning, cannot incorporate spatial information; thus, vulnerability can be only roughly assessed. In this article, we propose Bayesian network (BN) and spatial analysis techniques to mine spatial data sets to evaluate the vulnerability of human beings. In our approach, spatial analysis is leveraged to preprocess the data; for example, kernel density analysis (KDA) and accumulative road cost surface modeling (ARCSM) are employed to quantify the influence of geofeatures on vulnerability and relate such influence to spatial distance. The knowledge‐ and data‐based BN provides a consistent platform to integrate a variety of factors, including those extracted by KDA and ARCSM to model vulnerability uncertainty. We also consider the model's uncertainty and use the Bayesian model average and Occam's Window to average the multiple models obtained by our approach to robust prediction of the risk and vulnerability. We compare our approach with other probabilistic models in the case study of seismic risk and conclude that our approach is a good means to mining spatial data sets for evaluating vulnerability.     

Designing optimal food safety monitoring schemes using Bayesian network and integer programming: The case of monitoring dioxins and DL-PCBs

Efficient food safety monitoring should achieve optimal resource allocation. In this article, a methodology is presented to optimize the use of resources for food safety monitoring aimed at identifying noncompliant samples and estimating background level of hazards in food products. A Bayesian network (BN) model and an optimization model were combined in a single framework. The framework was applied to monitoring dioxins and dioxin-like polychlorinated biphenyls (DL-PCBs) in primary animal-derived food products in the Netherlands. The BN model was built using a national dataset with monitoring results of dioxins and DL-PCBs in animal-derived food products over a 10-year period (2008–2017). These data were used to estimate the probability of detecting suspect samples with dioxins and DL-PCBs levels above preset thresholds, given certain sample conditions. The results of the BN model were then inserted into the optimization model to compute an optimal monitoring scheme. Model estimates showed that the probability of dioxins and DL-PCBs exceeding threshold limits was higher in laying hen eggs and sheep meat than in other animal-derived food (except deer meat). Compared with the monitoring scheme used in the Netherlands in 2018, the optimal monitoring scheme would save around 10,000 EUR per year. This could be obtained by reallocating monitoring resources from products with lower probability of dioxin and DL-PCBs exceeding threshold limits (e.g., pig meat) to products with higher probability (e.g., bovine animal meat), and by shifting sample collection from the last quarter of the year toward the first three quarters of the year.     

Exposure Assessment of TSEs from the Landspreading of Meat and Bone Meal

Recent changes in European legislation have meant that certain processed abattoir waste, which has been appropriately heat treated and ground to a specified particle size, can be spread on nonpasture agricultural land. This has opened the way for the potential landspreading of mammalian meat and bone meal (mMBM) derived from animals slaughtered for human consumption. This article reports on two separate case studies (Study 1 carried out in Great Britain (GB) and Study 2 carried out in Ireland) on the potential exposure to TSE infectivity following the spreading of abattoir waste (derived from animals slaughtered for human consumption) on nonpasture agricultural land. For Study 1, the average TSE infectivity on nonpasture agricultural land per year from sheep with scrapie was found to be higher (five orders of magnitude) than that estimated for BSE in cattle (3.9 x 10(-3) Ovine Oral ID(50)/ton of soil compared to 3.3 x 10(-8) Bovine Oral ID(50)/ton of soil). The mean estimate for BSE in sheep was 8.1 x 10(-6) Ovine Oral ID(50)/ton of soil. The mean level of infectivity in mMBM was assessed to be 1.2 x 10(-5) and 2.36 x 10(-5) ID(50)/ton of mMBM for Study 1 and 2, respectively. For Study 2 the spreading of mMBM was estimated to result in infectivity on nonpasture land of 1.62 x 10(-8) Bovine Oral ID(50)/m(3). The mean simulated probability of infection per year per bovine animal was 1.11 x 10(-9). Given the low infectivity density and corresponding low risks to bovines the spreading of mMBM could be considered a viable alternative for the utilization of mMBM.     

Urban risks due to climate change in the Andean municipality of Pasto,Colombia: A Bayesian network approach

The current trends of climate change will increase people's exposure to urban risks related to events such as landslides, floods, forest fires, food production, health, and water availability, which are stochastic and very localized in nature. This research uses a Bayesian network (BN) approach to analyze the intensity of such urban risks for the Andean municipality of Pasto, Colombia, under climate change scenarios. The stochastic BN model is linked to correlational models and local scenarios of representative concentration trajectories (RCP) to project the possible risks to which the municipality of Pasto will be exposed in the future. The results show significant risks in crop yields, food security, water availability and disaster risks, but no significant risks on the incidence of acute diarrheal diseases (ADD) and acute respiratory infections (ARI), whereas positive outcomes are likely to occur in livestock production, influenced by population growth. The advantage of the BN approach is the possibility of updating beliefs in the probabilities of occurrence of events, especially in developing, intermediate cities with information-limited contexts.     

Exploring Risk Judgments in a Trade Dispute Using Bayesian Networks

Bayesian networks (BNs) are graphical modeling tools that are generally recommended for exploring what‐if scenarios, visualizing systems and problems, and for communication between stakeholders during decision making. In this article, we investigate their potential for exploring different perspectives in trade disputes. To do so, we draw on a specific case study that was arbitrated by the World Trade Organization (WTO): the Australia‐New Zealand apples dispute. The dispute centered on disagreement about judgments contained within Australia's 2006 import risk analysis (IRA). We built a range of BNs of increasing complexity that modeled various approaches to undertaking IRAs, from the basic qualitative and semi‐quantitative risk analyses routinely performed in government agencies, to the more complex quantitative simulation undertaken by Australia in the apples dispute. We found the BNs useful for exploring disagreements under uncertainty because they are probabilistic and transparently represent steps in the analysis. Different scenarios and evidence can easily be entered. Specifically, we explore the sensitivity of the risk output to different judgments (particularly volume of trade). Thus, we explore how BNs could usefully aid WTO dispute settlement. We conclude that BNs are preferable to basic qualitative and semi‐quantitative risk analyses because they offer an accessible interface and are mathematically sound. However, most current BN modeling tools are limited compared with complex simulations, as was used in the 2006 apples IRA. Although complex simulations may be more accurate, they are a black box for stakeholders. BNs have the potential to be a transparent aid to complex decision making, but they are currently computationally limited. Recent technological software developments are promising.     

Labor Mobility,Job Preferences,and Income Distribution

A model is presented to illustrate the role of labor mobility in income distribution. Worker preference for different occupations and wage comparisons are combined to determine the allocation of labor between sectors and to trace the behavior of the Gini coefficient as mobility increases. The Gini coefficient fails to fall monotonically with increasing labor mobility, making its interpretation ambiguous. It is also shown that the Gini coefficient has a lower nonzero limit, a fact compatible with empirical findings.     

Examining the Antecedents and Consequences of CIO Strategic Decision‐Making Authority: An Empirical Study*

Despite the strategic importance of information technology (IT) to contemporary firms, chief information officers (CIO) often still have varying degrees of strategic decision‐making authority. In this study, we apply the theory of managerial discretion to define CIO strategic decision‐making authority and argue that the CIO's level of strategic decision‐making authority directly influences IT's contribution to organization performance. We also draw on the power and politics perspective in the strategic decision‐making literature to identify the direct antecedents to the CIO's strategic decision‐making authority. A theoretical model is presented and empirically tested using survey data collected from a cross‐industry sample of 174 matched pairs of CIOs and top business executives through structural equation modeling. The results suggest that organizational climate, organizational support for IT, the CIO's structural power, the CIO's level of strategic effectiveness, and a strong partnership between the CIO and top management team directly influence the CIO's level of strategic decision‐making authority within the organization. The results also suggest that the CIO's strategic decision‐making authority in the organization directly influences the contribution of IT to firm performance and that effective CIOs have a greater influence on IT's contribution when provided with strategic decision‐making authority.     

INCOMPLETE SOCIAL CONTRACTS

There is a long normative ‘Social Contract’ tradition that attempts to characterize ex‐post income inequalities that are agreeable to all ‘behind a veil of ignorance.’ This paper takes a similar normative approach to characterize social decision‐making procedures. It is shown that quite generally some form of majority‐voting is preferred to unanimity ‘behind a veil of ignorance’ whenever society faces deadweight costs in making compensating transfers. Deviations from unanimity (or ex‐post Pareto optimality) are ex‐ante efficient to the extent that they economize on costly compensating transfers. Put another way, the optimal decision rule trades off the benefits of minority protection and those from greater flexibility. (JEL: H11, G33, G34, D63, P16, P48)     

Feedback in DSS as a Source of Control: Experiments with the Timing of Feedback

Well-designed feedback can improve decision making but to date there has been no comprehensive study of feedback in decision support systems (DSS) that could guide developers in its design. This work examines the opportunities and means to enhance cognitive control in decision making by providing appropriate feedback with DSS. It concentrates on the timing of feedback, which has been shown to affect the use of feedback and also demonstrates the potential advantages of using information technology over and above manual decision environments. Two experiments test the effects of controlling the timing of cognitive feedback on the user's cognitive control. The first tests the effect of timing one source of cognitive feedback, and the second tests the effect of timing two sources of feedback. The findings suggest that the design of feedback should become a routine part of the development of DSS.     

Risk-Based Approaches to Deal with Uncertainty in a Data-Poor System: Stakeholder Involvement in Hazard Identification for Marine National Parks and Marine Sanctuaries in Victoria, Australia

Management responsibilities for the system of marine national parks and sanctuaries declared in Victoria, Australia in 2002 have created imperatives for robust, scientifically defendable approaches to identifying threats to valued ecological attributes of the parks, setting management priorities, and developing monitoring systems. We are developing a protocol for ecological risk assessment in the parks that has due regard for the perception of risks by individuals, and ensures that stakeholder values are an intrinsic part of decision making. The inclusive and transparent protocol provides an opportunity for stakeholder involvement in the identification of valued attributes, as well as in the assessment of associated risks. Our approach brings together ideas about how science enters the community engagement domain in ways that promote collaboration and transparency in decision making. A series of stakeholder workshops across the state drew on the expertise of agency staff, community groups, fishers, industry representatives, academics, and knowledgeable park neighbors to identify hazards of major concern in the parks. Many hazards involved predictable, tangible threats like pollution and exotic species, but the approach also identified a number of less obvious threats related to governance issues and the knowledge-base for the parks. Importantly, the workshops with their broad range of stakeholders identified threats not previously considered by the management agency in its internal assessments, and several of these "new" threats are already the subject of action by the agency. The deliberate incorporation of local knowledge and local networks in the risk assessment process also provided opportunities for greater engagement of stakeholders with the management agency.     

How to support the application of multiple criteria decision analysis? Let us start with a comprehensive taxonomy

Decision making is a complex task that involves a multitude of perspectives, constraints, and variables. Multiple Criteria Decision Analysis (MCDA) is a process that has been used for several decades to support decision making. It includes a series of steps that systematically help Decision Maker(s) (DM(s)) and stakeholders in structuring a decision making problem, identifying their preferences, and building a decision recommendation consistent with those preferences. Over the last decades, many studies have demonstrated the conduct of the MCDA process and how to select an MCDA method. Until now, there has not been a review of these studies, nor a proposal of a unified and comprehensive high-level representation of the MCDA process characteristics (i.e., features), which is the goal of this paper. We introduce a review of the research that defines how to conduct the MCDA process, compares MCDA methods, and presents Decision Support Systems (DSSs) to recommend a relevant MCDA method or a subset of methods. We then synthesize this research into a taxonomy of characteristics of the MCDA process, grouped into three main phases, (i) problem formulation, (ii) construction of the decision recommendation, and (iii) qualitative features and technical support. Each of these phases includes a subset of the 10 characteristics that helps the analyst implementing the MCDA process, while also being aware of the implication of these choices at each step. By showing how decision making can be split into manageable and justifiable steps, we reduce the risk of overwhelming the analyst, as well as the DMs/stakeholders during the MCDA process. A questioning strategy is also proposed to demonstrate how to apply the taxonomy to map MCDA methods and select the most relevant one(s) using real case studies. Additionally, we show how the DSSs for MCDA method recommendation can be grouped into three main clusters. This proposal can enhance a traceable and categorizable development of such systems.     

A STRATEGY FOR RISK REDUCTION IN INCENTIVE CONTRACTING*

An important question with respect to government incentive contracts that has received little attention is: What alternatives to the current practice of making single-stage choices of incentive sharing rates can lead to situations in which these sharing rates can be chosen under reduced risk or cost uncertainty? This paper provides a decision-theoretic framework that illustrates a possibility of reducing risk, from the viewpoints of both the contracting parties, given some negotiated estimate of costs to be incurred. It is shown that such a possibility can arise in a situation in which it is technically feasible to separate or partition the contractual work into closely related tasks or units. Specifically, it is demonstrated that for a given cost estimate (target cost) and target profit, a set of optimal sharing rates (one for each unit) yields a higher risk-adjusted value of the contract than the single-stage sharing rate for the entire contract.