Assessing Uncertainty in Simulation-Based Maritime Risk Assessment

Recent work in the assessment of risk in maritime transportation systems has used simulation-based probabilistic risk assessment techniques. In the Prince William Sound and Washington State Ferries risk assessments, the studies' recommendations were backed up by estimates of their impact made using such techniques and all recommendations were implemented. However, the level of uncertainty about these estimates was not available, leaving the decisionmakers unsure whether the evidence was sufficient to assess specific risks and benefits. The first step toward assessing the impact of uncertainty in maritime risk assessments is to model the uncertainty in the simulation models used. In this article, a study of the impact of proposed ferry service expansions in San Francisco Bay is used as a case study to demonstrate the use of Bayesian simulation techniques to propagate uncertainty throughout the analysis. The conclusions drawn in the original study are shown, in this case, to be robust to the inherent uncertainties. The main intellectual merit of this work is the development of Bayesian simulation technique to model uncertainty in the assessment of maritime risk. However, Bayesian simulations have been implemented only as theoretical demonstrations. Their use in a large, complex system may be considered state of the art in the field of computational sciences.     

The Role of Analysis on the 17 Most Political Acres on the Face of the Earth

Benefit‐cost and risk analyses associated with agency rule making are required by presidential order and are reviewed by the Office of Management and Budget's (OMB) Office of Information and Regulatory Affairs (OIRA). This White House oversight of agency regulatory analysis means that OIRA's work, though primarily analytic, competes daily with the intense political pressures that characterize all work in the White House. Even in the face of the demands of politics, analysis is respected and utilized. A balance between politics and analysis is maintained in several ways. Several structural characteristics of the White House help protect analysis, such as a strong complement of offices whose role is primarily analytic (for example, the Council of Economic Advisors, the Office of Science and Technology Policy, and OMB). OIRA's ability to successfully coordinate its regulatory review with White House officials ensures that it serves as an agent for presidential regulatory policy at the same time it champions higher quality benefit‐cost and risk analysis. This role as intermediary between analytic and political judgment also results from OIRA's expertise in such analysis and its reputation for the discretion necessary to maintain the trust of the president's upper level staff.     

Lay representations of workplace stress: What do people really mean when they say they are stressed?

An individual's beliefs in relation to stress are likely to affect their perceptions, and hence their work-related actions (such as absenteeism). In this paper, lay representations of work stress were investigated utilising semi-structured interviews with 45 individuals from a range of occupations. The meaning of occupational stress, its antecedents and outcomes, and ways by which it may be managed were examined. Dominant factors were established through the use of thematic content analysis. Similarities and differences were found between lay and professional discourses on work stress. Results indicate that lay representations of occupational stress are multi-faceted. Little consensus was found in how participants interpreted the concept: a diverse range of personal, environmental, and societal factors was highlighted. A different (and arguably more complex) range of definitions of job stress and the manner in which it impacts on individuals was revealed than has been reported in previous studies. The causes of stress at work were perceived as being predominantly organisational, but the impact of stress on the employee was more salient than organisational outcomes. Paradoxically, secondary and tertiary stress management techniques were thought to be more effective than interventions designed to prevent stress at work. Interviewees with line management responsibility were more likely to emphasise individual responsibility for managing stress, most others maintained that the individual and the organisation are equally responsible. The potential value of examining lay representations of job stress to the discipline of Occupational Health Psychology is discussed and suggestions for future research are made.     

Talking about work stress: Discourse analysis and implications for stress interventions

This study used discourse analysis to explore the way in which employees understand work stress. Twenty-two female clerical workers in a Canadian city participated in focus group meetings where they talked about and made sense of their experiences of work stress. The women's accounts were analysed using discourse analysis methods (i.e. an examination of how talk is constructed). The findings revealed that talking about being stressed provides a socially acceptable way of expressing discomfort and regaining a sense of importance that is lost through feeling under-valued and under-appreciated in the organization. In contrast, admitting to being unable to cope with stress was considered to be 'abnormal'. The stress discourse fosters a sense of helplessness and ambiguity by not acknowledging external influences on clerical workers' experiences, such as their place within the power structure of the organization, and by limiting their sense of agency and control over problems experienced at work. The implications of these findings for organizational culture and interventions are discussed. For example, employers are encouraged to be conscious of the messages being sent to employees about how negative emotions or distressing experiences at work are to be addressed (i.e. how 'stress' is to be managed). Recommendations are made for future research using discourse analysis, such as the examination of alternative discourses that aim to improve conditions at work.     

Integrated Analysis: Combining Risk and Economic Assessments While Preserving the Separation of Powers

This article presents a process for an integrated policy analysis that combines risk assessment and benefit-cost analysis. This concept, which explicitly combines the two types of related analyses, seems to contradict the long-accepted risk analysis paradigm of separating risk assessment and risk management since benefit-cost analysis is generally considered to be a part of risk management. Yet that separation has become a problem because benefit-cost analysis uses risk assessment results as a starting point and considerable debate over the last several years focused on the incompatibility of the use of upper bounds or "safe" point estimates in many risk assessments with benefit-cost analysis. The problem with these risk assessments is that they ignore probabilistic information. As advanced probabilistic techniques for risk assessment emerge and economic analysts receive distributions of risks instead of point estimates, the artificial separation between risk analysts and the economic/decision analysts complicates the overall analysis. In addition, recent developments in countervailing risk theory suggest that combining the risk and benefit-cost analyses is required to fully understand the complexity of choices and tradeoffs faced by the decisionmaker. This article also argues that the separation of analysis and management is important, but that benefit-cost analysis has been wrongly classified into the risk management category and that the analytical effort associated with understanding the economic impacts of risk reduction actions need to be part of a broader risk assessment process.     

Occupational health psychology

Abstract

An individual's beliefs in relation to stress are likely to affect their perceptions, and hence their work-related actions (such as absenteeism). In this paper, lay representations of work stress were investigated utilising semi-structured interviews with 45 individuals from a range of occupations. The meaning of occupational stress, its antecedents and outcomes, and ways by which it may be managed were examined. Dominant factors were established through the use of thematic content analysis. Similarities and differences were found between lay and professional discourses on work stress. Results indicate that lay representations of occupational stress are multi-faceted. Little consensus was found in how participants interpreted the concept: a diverse range of personal, environmental, and societal factors was highlighted. A different (and arguably more complex) range of definitions of job stress and the manner in which it impacts on individuals was revealed than has been reported in previous studies. The causes of stress at work were perceived as being predominantly organisational, but the impact of stress on the employee was more salient than organisational outcomes. Paradoxically, secondary and tertiary stress management techniques were thought to be more effective than interventions designed to prevent stress at work. Interviewees with line management responsibility were more likely to emphasise individual responsibility for managing stress, most others maintained that the individual and the organisation are equally responsible. The potential value of examining lay representations of job stress to the discipline of Occupational Health Psychology is discussed and suggestions for future research are made.     

Sensitivity Analysis Using Risk Measures

In a quantitative model with uncertain inputs, the uncertainty of the output can be summarized by a risk measure. We propose a sensitivity analysis method based on derivatives of the output risk measure, in the direction of model inputs. This produces a global sensitivity measure, explicitly linking sensitivity and uncertainty analyses. We focus on the case of distortion risk measures, defined as weighted averages of output percentiles, and prove a representation of the sensitivity measure that can be evaluated on a Monte Carlo sample, as a weighted average of gradients over the input space. When the analytical model is unknown or hard to work with, nonparametric techniques are used for gradient estimation. This process is demonstrated through the example of a nonlinear insurance loss model. Furthermore, the proposed framework is extended in order to measure sensitivity to constant model parameters, uncertain statistical parameters, and random factors driving dependence between model inputs.     

A Common Rationale for Global Sensitivity Measures and Their Estimation

Measures of sensitivity and uncertainty have become an integral part of risk analysis. Many such measures have a conditional probabilistic structure, for which a straightforward Monte Carlo estimation procedure has a double‐loop form. Recently, a more efficient single‐loop procedure has been introduced, and consistency of this procedure has been demonstrated separately for particular measures, such as those based on variance, density, and information value. In this work, we give a unified proof of single‐loop consistency that applies to any measure satisfying a common rationale. This proof is not only more general but invokes less restrictive assumptions than heretofore in the literature, allowing for the presence of correlations among model inputs and of categorical variables. We examine numerical convergence of such an estimator under a variety of sensitivity measures. We also examine its application to a published medical case study.     

Impression management techniques in crowdfunding: An analysis of Kickstarter videos using artificial intelligence

Crowdfunding entrepreneurs increasingly use videos to present their venture and themselves to potential investors. To do this effectively, they consciously or unconsciously use several impression management techniques. Based on human and automated (AI-based analysis) of technology-related videos, we determine which impression management techniques help crowdfunding creators to attract a higher number of backers and meet their financial targets. Our results indicate that self-presentation and exemplification techniques are positively associated with crowdfunding success, while intimidation is negatively related to crowdfunding success. Our findings show that automated analysis of videos using advanced AI can replace human coding since measures based on human coding become insignificant when AI-based measures are included in the analysis. This illustrates the insights companies can create through the large scale automated analysis of video content using AI, including outside of a crowdfunding context.     

On How to Deal with Deep Uncertainties in a Risk Assessment and Management Context

Recently, several authors have presented interesting contributions on how to meet deep or severe uncertainties in a risk analysis setting. In this article, we provide some reflections on some of the foundational pillars that this work is based on, including the meaning of concepts such as deep uncertainty, known probabilities, and correct models, the aim being to contribute to a strengthening of the scientific platform of the work, as well as providing new insights on how to best implement management policies meeting these uncertainties. We also provide perspectives on the boundaries and limitations of analytical approaches for supporting decision making in cases of deep uncertainties. A main conclusion of the article is that deep uncertainties call for managerial review and judgment that sees beyond the analytical frameworks studied in risk assessment and risk management contexts, including those now often suggested to be used, such as robust optimization techniques. This managerial review and judgment should be seen as a basic element of the risk management.     

A Protocol for the Global Sensitivity Analysis of Impact Assessment Models in Life Cycle Assessment

The life cycle assessment (LCA) framework has established itself as the leading tool for the assessment of the environmental impact of products. Several works have established the need of integrating the LCA and risk analysis methodologies, due to the several common aspects. One of the ways to reach such integration is through guaranteeing that uncertainties in LCA modeling are carefully treated. It has been claimed that more attention should be paid to quantifying the uncertainties present in the various phases of LCA. Though the topic has been attracting increasing attention of practitioners and experts in LCA, there is still a lack of understanding and a limited use of the available statistical tools. In this work, we introduce a protocol to conduct global sensitivity analysis in LCA. The article focuses on the life cycle impact assessment (LCIA), and particularly on the relevance of global techniques for the development of trustable impact assessment models. We use a novel characterization model developed for the quantification of the impacts of noise on humans as a test case. We show that global SA is fundamental to guarantee that the modeler has a complete understanding of: (i) the structure of the model and (ii) the importance of uncertain model inputs and the interaction among them.     

Integrating performance and risk aspects of supply chain design processes

Abstract

Supply chain design is a complex and relatively poorly structured process, involving choosing many decisional parameters and it usually requires consideration of numerous sources of uncertainty. Many conventional processes of supply chain design involve taking a deterministic approach, using point estimates, on important measures of supply chain effectiveness such as cost, quality, delivery reliability and service levels. Supply chain disruptions are often separately considered as risks, both in the research literature and in practice, meaning that a purely traditional risk management and risk minimization approach is taken. We have developed and applied an approach that combines the intellect and experience of the supply chain designer with the power of evaluation provided by a Monte Carlo simulation model, which uses decision analysis techniques to explicitly incorporate the full spectrum of uncertain quantities across the set of alternative supply chain designs being considered. After defining and setting out the general decision variables and uncertainty factors for 16 distinct supply chain design decision categories, we then apply that approach to combine the decision-makers’ heuristics with the probabilistic modeling approach, iteratively, to achieve the best of both elements of such an approach. This novel approach to fully integrating performance and risk elements of supply chain designs is then illustrated with a case study. Finally, we call for further developmental research and field work to refine this approach.     

Incorporating quality into data envelopment analysis of nursing home performance: A case study

When using data envelopment analysis (DEA) as a benchmarking technique for nursing homes, it is essential to include measures of the quality of care. We survey applications where quality has been incorporated into DEA models and consider the concerns that arise when the results show that quality measures have been effectively ignored. Three modeling techniques are identified that address these concerns. Each of these techniques requires some input from management as to the proper emphasis to be placed on the quality aspect of performance. We report the results of a case study in which we apply these techniques to a DEA model of nursing home performance. We examine in depth not only the resulting efficiency scores, but also the benchmark sets and the weights given to the input and output measures. We find that two of the techniques are effective in insuring that DEA results discriminate between high and low quality performance.     

Classifying capital projects for top management decision-making

This paper is an attempt to redress a perceived imbalance in the research and literature of capital project analysis. There is a plethora of work on the quantitative aspects of project analysis, yet empirical surveys show limited use of such techniques. There is limited work on the organizational processes of project selection, but there appears to be little or no work taking place on formal organizational systems for project selection.The more quantitative literature generally takes a deductive approach using a model developed from microeconomics based on the assumption that the objective of the firm is the maximization of owners' wealth. In this paper it is argued that there is a need to take an inductive approach and place the capital investment decision in its organizational setting. This paper looks at the formal capital budgeting system within the corporate setting.It is suggested that the specific capital investment decision takes place against the background of a set of policies and constraints, which are more or less explicit.The selection of corporate projects is a complex issue, but to provide some structure so that the problems can be processed by the organization, a capital budgeting system is designed. Part of this system is the classification of projects according to some criteria. It would therefore appear that the purpose of this classification system is to aid capital project selection within an organizational context.A consideration of classification systems for capital projects is undertaken, this is followed by a survey of the classification systems which have been proposed in the capital investment literature. Finally a classification system based upon specific criteria is proposed.     

Understanding Organizational Safety Using Value-Focused Thinking

Decision analysis is recognized as the right way to make risk management decisions, using probabilistic techniques to assess the accident risk. It is also accepted that the decisions that individuals in the organization make affect the likelihood of an accident and thus managerial and organizational factors should be included in the risk modeling process. However, decision analytic techniques have not been used to understand the decisions that are made by these individuals. The initial domain for this research is marine transportation. We use the framework of value-focused thinking in order to understand safety decisions made within our research partner organization, a major domestic oil tanker operator. We describe the results of interviews held with managers and employees from this organization. Through these interviews, we sought to understand the values these experts apply in their roles within the organization and the objectives they seek to achieve to contribute to its overall safety performance. The end result is a framework that not only portrays the fundamental objectives of safe operations for various roles in the organization, but also interconnects these different decision contexts. We believe that this approach is fundamentally different from those used in previous work and that this is an interesting application of value-focused thinking.     

Sensitivity of Concentration and Risk Predictions in the PRESTO and MMSOILS Multimedia Models: Regression Technique Assessment

This paper describes the application of two multimedia models, PRESTO and MMSOILS, to predict contaminant migration from a landfill that contains an organic chemical (methylene chloride) and a radionuclide (uranium-238). Exposure point concentrations and human health risks are predicted, and distributions of those predictions are generated using Monte Carlo techniques. Analysis of exposure point concentrations shows that predictions of uranium-238 in groundwater differ by more than one order of magnitude between models. These differences occur mainly because PRESTO simulates uranium-238 transport through the groundwater using a one-dimensional algorithm and vertically mixes the plume over an effective mixing depth, whereas MMSOILS uses a three-dimensional algorithm and simulates a plume that resides near the surface of the aquifer.A sensitivity analysis, using stepwise multiple linear regression, is performed to evaluate which of the random variables are most important in producing the predicted distributions of exposure point concentrations and health risks. The sensitivity analysis shows that the predicted distributions can be accurately reproduced using a small subset of the random variables. Simple regression techniques are applied, for comparison, to the same scenarios, and results are similar. The practical implication of this analysis is the ability to distinguish between important versus unimportant random variables in terms of their sensitivity to selected endpoints.     

The recruitment and selection of hospital medical consultants

Abstract

The literature on selection has been dominated in recent decades by the ‘prediction’ paradigm. Such an approach requires a substantial number of positions to be filled in order to compute validity coefficients. There are other approaches, using a different paradigm, when concentrated on adjusting the organization and the employee. Here careful analysis of job demands is essential. This article describes such an analysis, taking the medical consultant as an example. The method can be used in an iterative way. It is useful when one has to work with small numbers of positions.     

Risk in Benefit-Cost Analysis

In recent years, benefit-cost analysis has been increasingly applied to large societal decision problems (such as developing a fast breeder energy economy) which involve both risks to society and analysis of very long-term consequences possibly extending over many human generations. This paper examines the philosophical underpinnings of the technique which is a special case of utilitarianism, and compares implications of the technique to those arising from alternative ethical systems in analyzing questions of public safety. Ethical systems which emphasize the good of the whole, such as utilitarianism, are shown to differ sharply in decision outcomes from those which emphasize the rights of the individual, such as libertarianism. It is suggested that benefit-cost analysis should be broadened to include alternative weightings of benefits and costs consistent with a variety of ethical views.     

Can a team have too much cohesion? The dark side to network density

The goal of most work teams is high performance. Prior studies suggest that performance within work groups is influenced by that group’s social network topology. Research has generally revealed to date that group cohesion (i.e., network density) is positively related to team performance under certain conditions. However, more recent research has indicated that this is not the full story. Recent research suggests that an inverse curvilinear relationship exists between social network measures (of which group cohesion is one) and team performance. In response to the need for understanding this relationship more fully, and leveraging the promising new insights that can be garnered with the use of social network analysis (SNA), this study employs SNA as a tool to explore the structural cohesiveness of teams of travel agents. This research extends our understanding of the relationship between intragroup social network relations and team performance by confirming an inverse curvilinear relationship exists between group cohesion and team performance. This paper leverages email communication to determine the social networks of each team, and then examines such in light of team performance. In total, an analysis of more than 7 million emails was undertaken. This study was conducted with work teams within a service organization. Each team in the study carries out the same tasks, i.e., identical task contingency, yet represents a distinct unit of analysis. The study confirms that social network topology is a valuable predictor of team performance and confirms that, like so many other social network measures, group cohesion and team performance share an inverse ‘U’ shaped relationship, not strictly a positive one as previously posited.     

Measuring Uncertainty Importance: Investigation and Comparison of Alternative Approaches

Uncertainty importance measures are quantitative tools aiming at identifying the contribution of uncertain inputs to output uncertainty. Their application ranges from food safety (Frey & Patil (2002)) to hurricane losses (Iman et al. (2005a, 2005b)). Results and indications an analyst derives depend on the method selected for the study. In this work, we investigate the assumptions at the basis of various indicator families to discuss the information they convey to the analyst/decisionmaker. We start with nonparametric techniques, and then present variance-based methods. By means of an example we show that output variance does not always reflect a decisionmaker state of knowledge of the inputs. We then examine the use of moment-independent approaches to global sensitivity analysis, i.e., techniques that look at the entire output distribution without a specific reference to its moments. Numerical results demonstrate that both moment-independent and variance-based indicators agree in identifying noninfluential parameters. However, differences in the ranking of the most relevant factors show that inputs that influence variance the most are not necessarily the ones that influence the output uncertainty distribution the most.