Search

During the review process for the paper "Architecting Portfolios of Systems" in this issue one of the reviewers suggested that part of the reviewer‐author dialog be published because it clarified some issues beyond the paper itself and illustrated some different perspectives. This paper elaborates three major topics: The categorization of different types of collections of systems, the concept of requisite modeling in how we choose to model systems under development across a spectrum of rigor, and how to judge the success of research in systems engineering processes and methods. [ABSTRACT FROM AUTHOR]

Conservation decisions are challenging, not only because they often involve difficult conflicts among outcomes that people value, but because our understanding of the natural world and our effects on it is fraught with uncertainty. Value of Information (VoI) methods provide an approach for understanding and managing uncertainty from the standpoint of the decision maker. These methods are commonly used in other fields (e.g. economics, public health) and are increasingly used in biodiversity conservation. This decision‐analytical approach can identify the best management alternative to select where the effectiveness of interventions is uncertain, and can help to decide when to act and when to delay action until after further research. We review the use of VoI in the environmental domain, reflect on the need for greater uptake of VoI, particularly for strategic conservation planning, and suggest promising areas for new research. We also suggest common reporting standards as a means of increasing the leverage of this powerful tool. The environmental science, ecology and biodiversity categories of the Web of Knowledge were searched using the terms 'Value of Information,' 'Expected Value of Perfect Information,' and the abbreviation 'EVPI.' Google Scholar was searched with the same terms, and additionally the terms decision and biology, biodiversity conservation, fish, or ecology. We identified 1225 papers from these searches. Included studies were limited to those that showed an application of VoI in biodiversity conservation rather than simply describing the method. All examples of use of VOI were summarised regarding the application of VoI, the management objectives, the uncertainties, the models used, how the objectives were measured, and the type of VoI. While the use of VoI appears to be on the increase in biodiversity conservation, the reporting of results is highly variable, which can make it difficult to understand the decision context and which uncertainties were considered. Moreover, it was unclear if, and how, the papers informed management and policy interventions, which is why we suggest a range of reporting standards that would aid the use of VoI. The use of VoI in conservation settings is at an early stage. There are opportunities for broader applications, not only for species‐focussed management problems, but also for setting local or global research priorities for biodiversity conservation, making funding decisions, or designing or improving protected area networks and management. The long‐term benefits of applying VoI methods to biodiversity conservation include a more structured and decision‐focused allocation of resources to research. [ABSTRACT FROM AUTHOR]

Evidence‐based medicine (EBM), one of the most important movements in health care, has been a lightning rod for controversy. Conflicts about the meaning and value of EBM are owing in part to lack of clarity about basic questions regarding its development, the importance of expertise and intuition, and the role of evidence in clinical decision making. These issues have persisted in part because of unclarity at the outset, but also because of how EBM evolved, why it was introduced when it was, and how it was modified following its introduction. This paper traces the evolution of EBM from clinical epidemiology (CE) and the internal dispute that precipitated the developers to establish EBM as a distinct approach to clinical practice. The paper proposes that health care industrialization also had a significant role in EBM's emergence and that industrialization influenced the decision to merge EBM with the method of normative decision making known as decision analysis (DA). The paper discusses the impact of this merger, in particular how it led to EBM's identification with managed care and has added momentum to the effort at forging a connection between a normative decision model and clinical judgement. This effort would turn clinical decision making into a conduit for bringing administrative rules and regulations into the consulting room and would result in expertise becoming a surplus skill. The paper closes by discussing a challenge yet unmet by EBM's advocates and critics—to chronicle the dangers that EBM in the framework of DA during the current era of industrialization poses to health and health care, and discover ways of unhinging the relationship between model and judgement. [ABSTRACT FROM AUTHOR]

This article applies a transformation into probabilities of preference, to rank papers submitted to the XLI Brazilian Symposium of Operational Research. Some 42 papers were divided into sets according to their main topics of interest and measures for 12 attributes were considered, for each paper separately, and also for sets of papers on the same topic, based on the referees' evaluations. Four alternative forms of composition of the probabilistic preferences are illustrated. [ABSTRACT FROM AUTHOR]

Some e‐platforms such as Amazon and JD.com are increasingly active in financing business, but theoretical research on this business is not comprehensive enough. This paper uses game theory model to compare the equilibrium of e‐platform financing (EPF) and bank financing (BF) in terms of pricing, quality, suppliers' competition, participants' profits, and consumer welfare. We find that a monopolistic supplier can always enjoy a lower loan interest rate and borrow more loans to improve production quantity or quality under EPF, which benefits to the participants and consumers. Interestingly, the e‐platform provides free loans and only profits from commission fee under some conditions. When there are two suppliers competing for the market share, the absolute advantage of EPF will change. If the supply chain or market environment is bad (production cost or commission rate is high; the potential market or initial budget is low), the e‐platform will offer lower interest rates than bank to reduce the strength gap between the suppliers; thus, the capital‐constrained supplier prefers EPF, which shows that the EPF business is an effective means to regulate upstream competition. However, when there are more competitive suppliers, the preferences of the suppliers with less initial capital are the same as the scenario of the two competitive suppliers, while the preference of the supplier with more initial capital is just the opposite. Finally, if the commission rate is endogenous, we demonstrate that the e‐platform will offer free loans but charge a relatively high commission fee. [ABSTRACT FROM AUTHOR]

A "portfolio‐of‐systems" is a collection of systems related by common management and budgeting, and at least some relationship to mission or missions. A portfolio‐of‐systems describes the not uncommon situation where an office is responsible for multiple systems in multiple phases of development but those systems are not related by common design, common manufacturing, or necessarily interconnected operationally. The relationship is fundamentally one of management, although other linkages might be present or designed if such additional linkages are deemed beneficial. The portfolio‐of‐systems concept differs from systems‐of‐systems and product‐line or family‐of‐systems constructs. This paper describes generalizations of architecting techniques for portfolio situations, particularly recognizing the presence of many largely independent components in multiple stages of development with an ongoing or episodic process. The paper presents two case studies of actual portfolios: One a portfolio of operational demonstration systems and the other a portfolio of weather satellites. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Lead pipe remediation budgets are limited and ought to maximize public health impact. This goal implies a nontrivial optimization problem; lead service lines connect water mains to individual houses, but any realistic replacement strategy must batch replacements at a larger scale. Additionally, planners typically lack a principled method for comparing the relative public health value of potential interventions and often plan projects based on nonhealth factors. This paper describes a simple process for estimating child health impact at a parcel level by cleaning and synthesizing municipal datasets that are commonly available but seldom joined due to data quality issues. Using geocoding as the core record linkage mechanism, parcel‐level toxicity data can be combined with school enrollment records to indicate where young children and lead lines coexist. A harm metric of estimated exposure‐years is described at the parcel level, which can then be aggregated to the project level and minimized globally by posing project selection as a 0/1 knapsack problem. Simplifying for use by nonexperts, the implied linear programming relaxation is solved with the greedy algorithm; ordering projects by benefit cost ratio produces a priority list that planners can then consider holistically alongside harder to quantify factors. A case study demonstrates the successful application of this framework to a small U.S. city's existing data to prioritize federal infrastructure funding. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper presents a network-based approach for analyzing team coordination and shared cognition in engineering design teams. The research setting is an Integrated Concurrent Engineering (ICE) laboratory in which teams of approximately 20 engineers are collocated to conduct rapid conceptual design of scientific spacecraft. A design structure matrix (DSM) of expected interactions is constructed from technical information flow data, and DSM representations of reported interactions are created using survey data from 10 ICE design teams. A comparative analysis of expected and reported interactions is used to calculate a metric of team coordination called socio-technical congruence (STC). To examine shared cognition, pairwise shared mental models (SMMs) are measured using pre- and post-session surveys on system design drivers. Shared knowledge networks (SMMs as edges) are constructed, and team learning is measured as the change in network structure over time. Analysis reveals statistically significant correlations between team learning and each of three technical attributes (system development time, launch mass, and mission concept maturity) and between team learning and team coordination. These results indicate that team members learn most from each other when working on difficult or unfamiliar problems and when expected and reported interactions are aligned. The paper concludes that team coordination and the design product in ICE are not necessarily directly related to each other but that both are related to shared cognition. Although this study focuses on conceptual design, it lays the foundation for future work examining the role of team coordination and shared cognition in full-scale system development programs. [ABSTRACT FROM AUTHOR]

Summary: Health care expenditures constitute a significant portion of governmental budgets. The percentage of fraud, waste and abuse within that spending has increased over years. This paper introduces the emerging area of statistical medical fraud assessment, which becomes crucial to handle the increasing size and complexity of the medical programmes. An overview of fraud types and detection is followed by the description of medical claims data. The utilisation of sampling, overpayment estimation and data mining methods in medical fraud assessment are presented. Recent unsupervised methods are illustrated with real world data. Finally, the paper introduces potential future research areas such as integrated decision making approaches and Bayesian methods and concludes with an overall discussion. The main goal of this exposition is to increase awareness about this important area among a broader audience of statisticians. [ABSTRACT FROM AUTHOR]

Abstract: Due to increasingly strict environmental regulation of marine transportation, vessel operators and other stakeholders are required to evaluate feasible compliance measures in the face of multiple criteria and with attention to uncertainties and risks. Several methods and models within operations research have been applied to explore such decision contexts, but little is reported on the problem structure itself and the key values, concerns, and uncertainties that apply to them. The objective of the paper is to present a problem structure for acquisition of marine emission reduction technologies in the Norwegian ferry fleet drawing on methods from decision science and systems engineering. To attain this objective, we utilize the SPADE methodology, which details five problem‐solving activities covering stakeholders, problem formulation, alternatives, decisions, and continuous evaluation. Each activity is informed by data collected through stakeholder interviews and literature analysis to establish an initial representation of acquisition decision issues. To keep a consistent and traceable problem structure, we provide a stakeholder diagram, value network, systemigram, and decision hierarchy centered around stakeholders and their values. These models may serve to inform decision‐makers in the development and appraisal of emission reduction technologies and strategies. The paper demonstrates the application of systems engineering as a problem‐structuring framework for complex, multidimensional marine technology acquisition decisions. [ABSTRACT FROM AUTHOR]

The recent benefit–risk framework (BRF) developed by the Food and Drug Administration (FDA) is intended to improve the clarity and consistency in communicating the reasoning behind the FDA's decisions, acting as an important advancement in US drug regulation. In the PDUFA VI implementation plan, the FDA states that it will continue to explore more structured or quantitative decision analysis approaches; however, it restricts their use within the current BRF that is purely qualitative. By contrast, European regulators and researchers have been long exploring the use of quantitative decision analysis approaches for evaluating drug benefit–risk balance. In this paper, we show how quantitative modelling, backed by decision theory, could complement and extend the FDA's BRF to better support the appraisal of evidence and improve decision outcomes. After providing relevant scientific definitions for benefit–risk assessment and describing the FDA and European Medicines Agency (EMA) frameworks, we explain the components of and differences between qualitative and quantitative approaches. We present lessons learned from the EMA experience with the use of quantitative modelling and we provide evidence of its benefits, illustrated by a real case study that helped to resolve differences of judgements among EMA regulators. [ABSTRACT FROM AUTHOR]

This paper identifies four attentional processes that increase efficiency and accuracy in repeated lexicographic tasks using an instructed strategy approach. We propose a framework to decompose attentional effort used to make a decision into four components: Orientation, Wrong Target, Duration, and Repetition. Orientation assesses attention to decision rules and the location of relevant information. Wrong Target measures wasted effort on unneeded information. Duration gauges time spent on each piece of needed information. Repetition measures the number of views on each relevant item. Greater Orientation is associated with lower effort in other components and increased accuracy. Repetition is most variable across individuals but generates the greatest improvement with practice. Duration is less affected by the other components and shows minimal improvement with experience. Finally, Wrong Target is similarly resistant to practice, but it is the only component strongly and positively associated with making errors. [ABSTRACT FROM AUTHOR]

Risks and benefit evaluation for controlled human infection studies, where healthy volunteers are deliberately exposed to infectious agents to evaluate vaccine efficacy, should be explicit, systematic, thorough, and non‐arbitrary. Decision analysis promotes these qualities using four steps: (1) determining explicit criteria and measures for evaluation, (2) identifying alternatives to the study, (3) defining the models used to estimate the measures for each alternative, and (4) running the models to produce the estimates and compare the alternatives. In this paper, we describe how decision analysis might be applied by funders and regulators, as well as by others contemplating the use of novel controlled human infection studies for vaccine development and evaluation. [ABSTRACT FROM AUTHOR]

In the 2010 Toronto summit, the leaders of the G‐20 countries agreed on the implementation of urgent fiscal consolidation plans, following the expansionary policies adopted to curb the recessionary effects of the financial crisis. Unprecedented cuts in public expenditures have taken place, particularly in the European Union periphery, reviving the discussion on the optimal size of the public sector. Supporters of fiscal restraint defend that bigger governments tend to be more inefficient, its opponents assume that government size determines the effectiveness of its performance. However, the social and economic impacts from contractionary fiscal policies ultimately depend on the level of public sector efficiency. Relatively inefficient governments have more scope to consolidate without compromising social welfare. In this paper, we adopt a multiple decision aiding approach, not previously employed for the assessment of complex macroeconomic performances, and employ the ELECTRE TRI‐C outranking method to categorize OECD countries on a set of criteria representing the quality of their public sectors. We then compare the obtained classifications with the share of each government's expenditures on GDP, to identify distinct levels of efficiency. Our analysis suggests that various countries exhibit a margin for efficiency gains, attenuating the social and economic effects of fiscal consolidation policies. [ABSTRACT FROM AUTHOR]

If quantity uncertainty exists in a first price auction that specifies a fixed bid, a participant must answer two questions when evaluating a candidate bid: "What are my chances of winning?" and "What is the effect of the winner's curse on my quantity estimate?" The winner's curse is the tendency of the winner of a first-price sealed-bid auction to be the bidder that most overvalues the items being offered. When value uncertainty is due to quantity uncertainty, the winner's curse implies that the bidder that most overestimates the quantity tends to win. Thus, if there is quantity uncertainty, a participant must adjust its bid for this tendency to overestimate quantities. This paper presents an empirical method to answer the above questions by estimating predictive distribution of the highest competing bid and the quantity bias caused by the winner's curse. The method is developed for timber auctions but is general to auctions where a fixed bid is called for and there is uncertainty in the mix and quantities of items being offered. An example that uses data from timber auctions is used to demonstrate the method. [ABSTRACT FROM AUTHOR]

Most models of investor behavior assume a time-state independent utility function and result in a deterministic solution where a given set of inputs uniquely specifies the decision. In contrast, a state preference model using a time-state dependent utility function is derived in this paper. The model allows the investment choice decision to be analyzed in a game theoretic context. The general solution is a mixed strategy which allows for a probabilistic interpretation of the decision. The approach presented in this paper can accommodate anomalies such as intransitivity of preference and satisficing as rational behavior. An example of a possible implementation is given along with interpretations of the outcomes. [ABSTRACT FROM AUTHOR]

This paper considers the question of how much time and effort should be spent in preparing a bid for a single item of known value sold at a first-price sealed-bid auction. A decision-theoretic approach to this bid decision summarizes the decision maker's knowledge of the competitive environment through his or her subjective probability distribution of the highest competing bid. Research activities such as collecting and analyzing bid histories are efforts to obtain additional information that reduces the uncertainty in the highest competing bid. The decision-theoretic concepts of expected value of perfect and imperfect information are used to place an economic value on such research activities. The results presented allow the decision maker to quantify the expected value of imperfect information when the uncertainty is normally distributed. The results show that additional research is most valuable prior to auctions the bidder expects to win. [ABSTRACT FROM AUTHOR]

In this paper, composite forecasting is considered from a Bayesian perspective. A forecast user combines two or more forecasts of an operationally relevant random variable. We consider the case where out performance is modeled as a realization from a multinomial process. The user has prior beliefs about the probability that a particular method outperforms all others, information which is summarized by the Dirichlet distribution. An empirical example with hog prices in the United States illustrates the method. [ABSTRACT FROM AUTHOR]

The paper discusses the value of information when a number of independent sources provide information related to a common set of states of nature. The starting point is the information economic model of information structures. The model is augmented to represent independence of informational sources by means of orthogonality of the information structures. A new mathematical operator, orthogonal product, is defined and its properties are probed. It is shown that this operator maintains some mathematical properties such as closure, association, unity element, null element, and so forth. It is demonstrated how the orthogonal product represents the notion of multisource information. The paper proves that an orthogonal product is generally more informative than its multipliers, namely, if cost is not considered a constraining factor, then there is a nonnegative value to obtaining a second opinion. An appendix to the paper expands this result to a case of partially dependent signals. The paper concludes with a numerical example and a discussion of the model's applicability for practical problems such as cost estimates. [ABSTRACT FROM AUTHOR]

Alternatives involving many factors are difficult to evaluate because of multiple underlying competing objectives. If evaluation is based on an incomplete set of factors, and if the purpose of the evaluation is to select a single overall best alternative, inferior alternatives may be selected with surprising frequency and/or severe negative impact. At the same time, sensitivity analysis of evaluation scores based on statistical criteria can easily mask the impact and the frequency of selection of inferior alternatives. In this paper, criteria appropriate to reflect the decision impact are developed and both the frequency and impact of the selection of inferior alternatives are demonstrated empirically. Previous studies based on statistical criteria [1] [9] indicated minimal impact on overall evaluation and selection. This paper demonstrates that high statistical criterion values coexist with frequent and/or serious errors of selection. [ABSTRACT FROM AUTHOR]

Two-source purchasing at differing factor-unit costs is a common real-world phenomenon. So too are uncertainty and risk aversion which, as this paper demonstrates, provides one explanation for why firms on occasion purchase from high-cost sellers. [ABSTRACT FROM AUTHOR]

Various consensus methods proposed for ranking problems yield controversial rankings and/or tied rankings which are vulnerable to considerable dispute These include Borda-Kendall (BK) and minimum-variance (MV) methods. This paper compares three continuous (ratio-scale) consensus scoring methods with BK and MV ranking methods. One method, termed GM, is an eigenvector scaling of the geometric-mean consensus matrix. GM allows for (1) paired-comparison voting inputs (as opposed to all-at-once ranking), (2) pick-the-winner preference voting, and (3) ratio-scale preference voting. GM is relatively simple to calculate on small computers or calculators, and merging of "close" candidates into tied rankings can be achieved by using an e-threshold tie rule discussed in this paper. The GM method thus can be used for paired-comparison voting to calculate both a ratio-scaled consensus index (based on a consensus eigenvector) and a ranking of candidates that allows for ties between "close" candidates. Eigenvalue analysis is used as a means of evaluating voter inconsistencies. [ABSTRACT FROM AUTHOR]

Information economics models evaluate the value of information under the assumption that decision makers wish to maximize their expected payoff. This assumption has been criticized for not being realistic enough since decision makers usually consider more than one business objective and might be satisfiers rather than optimizers. This paper attempts to apply an information economics model to decision situations where two business performance criteria, expected payoff and risk, are considered. In order to overcome the difficulty of unknown trade-off between the two criteria, one criterion is used as an objective to be optimized, while the other is set as a constraint. This may be interpreted as a combination of optimizing and satisficing approaches. It is shown how an information system can be evaluated in terms of both expected payoff and risk. The model suggests the trade-off between the two criteria as an additional trait of an information system. In the last part of the paper, a numerical example illustrates how a comparative evaluation of information structures is performed when risk minimization and expected payoff maximization are concurrently used as business performance criteria. [ABSTRACT FROM AUTHOR]

The views of Robert Winkler on research directions in decision making under uncertainty has properties that reflect his own research background, as show in his article "Research Directions in Decision Making Under Uncertainty." His decision to disregard references allows the paper to be free of value judgments about past work. The range of topics and emphasis make the paper a good outline for a textbook or a course which could follow on from introductory material on probability, decision trees and utility axioms. The focus of the article's reviewer is on the process of decision analysis rather than on technical or methodological developments.

This paper presents a short history of water resources systems analysis from its beginnings in the Harvard Water Program, through its continuing evolution toward a general field of water resources systems science. Current systems analysis practice is widespread and addresses the most challenging water issues of our times, including water scarcity and drought, climate change, providing water for food and energy production, decision making amid competing objectives, and bringing economic incentives to bear on water use. The emergence of public recognition and concern for the state of water resources provides an opportune moment for the field to reorient to meet the complex, interdependent, interdisciplinary, and global nature of today's water challenges. At present, water resources systems analysis is limited by low scientific and academic visibility relative to its influence in practice and bridled by localized findings that are difficult to generalize. The evident success of water resource systems analysis in practice (which is set out in this paper) needs in future to be strengthened by substantiating the field as the science of water resources that seeks to predict the water resources variables and outcomes that are important to governments, industries, and the public the world over. Doing so promotes the scientific credibility of the field, provides understanding of the state of water resources and furnishes the basis for predicting the impacts of our water choices. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper proposes a probabilistic approach for the pre-event assessment of seismic resilience of bridges, including uncertainties associated with expected damage, restoration process, and rebuilding/rehabilitation costs. A fragility analysis performs the probabilistic evaluation of the level of damage (none, slight, moderate, extensive, and complete) induced on bridges by a seismic event. Then, a probabilistic six-parameter sinusoidal-based function describes the bridge functionality over time. Depending on the level of regional seismic hazard, the level of performance that decision makers plan to achieve, the allowable economic impact, and the available budget for post-event rehabilitation activities, a wide spectrum of scenarios are provided. Possible restoration strategies accounting for the desired level of resilience and direct and indirect costs are investigated by performing a Monte Carlo simulation based on Latin hypercube sampling. Sensitivity analyses show how the recovery parameters affect the resilience assessment and seismic impact. Finally, the proposed approach is applied to an existing highway bridge located along a segment of I-15, between the cities of Corona and Murrieta, in California. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Risk‐based water resources planning is based on the premise that water managers should invest up to the point where the marginal benefit of risk reduction equals the marginal cost of achieving that benefit. However, this cost‐benefit approach may not guarantee robustness under uncertain future conditions, for instance under climatic changes. In this paper, we expand risk‐based decision analysis to explore possible ways of enhancing robustness in engineered water resources systems under different risk attitudes. Risk is measured as the expected annual cost of water use restrictions, while robustness is interpreted in the decision‐theoretic sense as the ability of a water resource system to maintain performance—expressed as a tolerable risk of water use restrictions—under a wide range of possible future conditions. Linking risk attitudes with robustness allows stakeholders to explicitly trade‐off incremental increases in robustness with investment costs for a given level of risk. We illustrate the framework through a case study of London's water supply system using state‐of‐the ‐art regional climate simulations to inform the estimation of risk and robustness. [ABSTRACT FROM AUTHOR]

The main purpose of this paper is to improve the production planning of Pakistan Tobacco Company by selecting the most preferred brand and subsequently generating maximum profit from it. As the company produces a variety of products, the technique of multi criteria decision making is used to select the most preferred brand. To generate the maximum output from the preferred brand, different methods of qualitative managerial analysis are used, which include decision analysis to decide 'why and where' the manufacturing should be carried out, transportation model to minimize the logistics cost while meeting the demand, and linear programming technique to maximize the profit generated in 2014-2015. The result obtained from analytical hierarchy process shows that the most preferred brand of the company with respect to price, quality, and comfort is John Player Gold Leaf. The decision analysis explains that this brand should be manufactured in the Jhelum factory of the company as it is more cost-effective to produce and there is a high availability of resources. Transportation model minimizes the logistics cost of this brand from the 2 factories while meeting the demand at each provinces, central warehouse. Linear programming contributes in generating a profit of 32.738 billion PKR with an amount of 0.35 million PKR, which is more than that of the current profit of the company in the year. These results will allow the top management of the company to take corrective decisions well in time, gain a core competency in cost reduction, and make the supply chain process more efficient. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper reviews literature on aesthetics and describes the development of vista and landscape aesthetics models. Spatially explicit variables were chosen to represent physical characteristics of natural landscapes that are important to aesthetic preferences. A vista aesthetics model evaluates the aesthetics of natural landscapes viewed from distances of more than 1000 m, and a landscape aesthetics model evaluates the aesthetic value of wetlands and forests within 1000 m from the viewer. Each of the model variables is quantified using spatially explicit metrics on a pixel-specific basis within EcoAIM™, a geographic information system (GIS)-based ecosystem services (ES) decision analysis support tool. Pixel values are 'binned' into ranked categories, and weights are assigned to select variables to represent stakeholder preferences. The final aesthetic score is the weighted sum of all variables and is assigned ranked values from 1 to 10. Ranked aesthetic values are displayed on maps by patch type and integrated within EcoAIM. The response of the aesthetic scoring in the models was tested by comparing current conditions in a discrete area of the facility with a Development scenario in the same area. The Development scenario consisted of two 6-story buildings and a trail replacing natural areas. The results of the vista aesthetic model indicate that the viewshed area variable had the greatest effect on the aesthetics overall score. Results from the landscape aesthetics model indicate a 10% increase in overall aesthetics value, attributed to the increase in landscape diversity. The models are sensitive to the weights assigned to certain variables by the user, and these weights should be set to reflect regional landscape characteristics as well as stakeholder preferences. This demonstration project shows that natural landscape aesthetics can be evaluated as part of a nonmonetary assessment of ES, and a scenario-building exercise provides end users with a tradeoff analysis in support of natural resource management decisions. Integr Environ Assess Manag 2017;13:926-938. © 2017 SETAC. [ABSTRACT FROM AUTHOR]

Operational risk is an increasingly important area of risk management. Scenarios are an important modelling tool in operational risk management as alternative viable methods may not exist. This can be due to challenging modelling, data and implementation issues, and other methods fail to take into account expert information. The use of scenarios has been recommended by regulators; however, scenarios can be unreliable, unrealistic and fail to take into account quantitative data. These problems have also been identified by regulators such as Basel, and presently little literature exists on addressing the problem of generating scenarios for operational risk. In this paper we propose a method for generating operational risk scenarios. We employ the method of cluster analysis to generate scenarios that enable one to combine expert opinion scenarios with quantitative operational risk data. We show that this scenario generation method leads to significantly improved scenarios and significant advantages for operational risk applications. In particular for operational risk modelling, our method leads to resolving the key problem of combining two sources of information without eliminating the information content gained from expert opinions, tractable computational implementation for operational risk modelling, improved stress testing, what-if analyses and the ability to apply our method to a wide range of quantitative operational risk data (including multivariate distributions). We conduct numerical experiments on our method to demonstrate and validate its performance and compare it against scenarios generated from statistical property matching for comparison. [ABSTRACT FROM AUTHOR]

Risk management of chemicals requires information about their adverse effects such as toxicity and persistence, for example. Testing of chemicals allows for improving the information base for regulatory decision-making on chemicals' production and use. Testing a large number of chemicals with limited time and resources forces a prioritization of chemicals. This paper proposes a decision model that provides a ranking of chemicals according to “urgency to test”. The model adopts a value-of-information approach describing the expected welfare gains from regulatory actions that respond to test information. We determine the value-of-information of tests revealing chemicals' levels of toxicity and persistence. We compare our findings to the prioritization of chemicals in the new European Chemicals Regulation “REACH”, where several tens of thousands of chemicals are to be tested in order to fill existing information gaps and to implement more effective risk management. We find that the main lines of chemicals' prioritization under REACH receive backing from our decision model. However, prioritization for testing can be further improved by accounting for testing costs and the sensitivity of regulatory action with respect to the test information. [ABSTRACT FROM AUTHOR]

The spread of nonindigenous (nonnative) species introduced into the United States is a significant and growing national problem and results in lost agricultural productivity, increased health problems, native species extinctions, and expensive prevention and eradication efforts. Thousands of nonindigenous species have either become established or spread, and introduction of aquatic nuisance species (ANS) into freshwater lakes threaten aquatic biodiversity. Expanding global trade is likely to increase the number of species that are spread across the globe, so the need to develop an approach to predict potential ANS invasions is great. Risk assessments currently being used to assess ANS risk rely on qualitative or semiquantitative information and expert opinion; thus, such approaches lack transparency and repeatability. A more quantitative approach is needed to augment the qualitative approaches currently in use. A quantitative approach with the use of the traditional ecological risk assessment (traditional ERA) framework combined with decision analysis tools was developed for assessing ANS risks in which the causative ecological risk agent is an organism rather than a chemical. This paper presents a systematic risk assessment framework that includes structured decision analysis to help organize and analyze pertinent data, state assumptions, address uncertainties in estimating the probability of an undesired ANS introduction, or spread and integrate these outputs with stakeholder values. This paper also describes when and how decision analysis tools can be used in such assessments for ANS. this framework and methodology will enable risk managers to systematically evaluate and compare alternatives and actions supporting ANS risk management and thus credibly prioritize resources, [ABSTRACT FROM AUTHOR]

This paper employs the multicriterion decision analysis (MCDA) technique in a novel way to address the efficiency of watershed instrumentation programs and the efficacy of watershed modeling. A case study of reconstructed watersheds in northern Alberta, Canada, is used to illustrate the proposed usage of the MCDA technique. The watersheds have been disturbed as a result of oil sands mining activities. Assessing the performance of the reconstructed watersheds with regard to restoring the hydrology of the disturbed watershed is a crucial issue for both the mining industry and other stakeholders. The problem is formulated in a multicriterion context. A payoff matrix containing seven evaluation criteria and three different soil covers as feasible alternatives is constructed. The system dynamics watershed (SDW) model is used to simulate the reconstructed watersheds over a period of 61 years using historical meteorological records. Accordingly, 61 payoff matrices that are populated using the results of the SDW model are evaluated. A multicriterion decision analysis framework is implemented to evaluate the different alternatives with respect to the chosen set of criteria. The three alternatives are ranked every year, and accordingly, the probability that a certain alternative dominates others is estimated. The alternative that has the highest probability of occupying the top rank over the period of analysis is indicated as the best alternative. The probability value is called the probability of making the right decision (PMRD). Various types of uncertainty analyses are conducted to evaluate the sensitivity of the final decision to changes in the scores of the evaluation matrices. An index, named the confidence in the PMRD, is developed to quantify the reliability of the results of the watershed model. The results highlight the utility of modeling as a possible alternative to some components of the intensive instrumentation program. Moreover, areas of deficiency and inaccuracies in the watershed model are identified for further improvements. [ABSTRACT FROM AUTHOR]

This paper formulates and discusses a series of sequential decision problems of the following common structure: A decision alternative of multiple attributes-that is, a job, an employee, or an investment alternative -- is to be selected within a certain fixed length of time. An unknown number of alternatives are presented sequentially, either deterministically or in a random manner. The decision maker can rank all the alternatives from best to worst without ties, and the decision to accept or reject an alternative is based solely on the relative ranks of those alternatives evaluated so far. The nonparametric sequential decision problem is first studied for a model involving a discrete time period and then generalized in terms of continuous time. Also considered is a variant of this problem involving a Bayesian estimation of (1) the uncertain probability of having an alternative at a given stage in the discrete-time model and (2) the arrival rate of alter- natives in the continuous-time model. The optimal selection strategy that maximizes the probability of selecting the absolute best alternative is illustrated with the job search problem and the single-machine job assignment problem. [ABSTRACT FROM AUTHOR]

A sustainable society index (SSI) has been designed to measure the sustainability of different countries in terms of economic, environmental and human well-being dimensions. These areas were subdivided into eight categories and 24 indicators. The paper shows that the SSI could be considered as a decision-making problem with multiple objectives. We propose a decision-analysis approach for estimation of the weights of indicators, categories and dimensions of well-being, with the target being a construction of overall scores for countries. A questionnaire was sent to experts from several countries, and the weights were estimated from their individual answers. The SSI 2010 results for different countries were recalculated through utilization of the estimated weights. There were some differences in the rankings compared with the earlier results with equal weights. Potential biases of the SSI approach used are critically evaluated, and opportunities to develop the SSI described. Copyright © 2016 John Wiley & Sons, Ltd and ERP Environment [ABSTRACT FROM AUTHOR]

A predicted difficult airway is sometimes considered a contra-indication to rapid sequence induction of general anaesthesia, even in an urgent case such as a category-1 caesarean section for fetal distress. However, formally assessing the risk is difficult because of the rarity and urgency of such cases. We have used decision analysis to quantify the time taken to establish anaesthesia, and probability of failure, of three possible anaesthetic methods, based on a systematic review of the literature. We considered rapid sequence induction of general anaesthesia with videolaryngoscopy, awake fibreoptic intubation and rapid spinal anaesthesia. Our results show a shorter mean (95% CI) time to induction of 100 (87-114) s using rapid sequence induction compared with 9 (7-11) min for awake fibreoptic intubation (p < 0.0001) and 6.3 (5.4-7.2) min for spinal anaesthesia (p < 0.0001). We calculate the risk of ultimate failed airway control after rapid sequence induction to be 21 (0-53) per 100,000 cases, and postulate that some mothers may accept such a risk in order to reduce potential fetal harm from an extended time interval until delivery. Although rapid sequence induction may not be the anaesthetic technique of choice for all cases in the circumstance of a category-1 caesarean section for fetal distress with a predicted difficult airway, we suggest that it is an acceptable option. [ABSTRACT FROM AUTHOR]

This paper proposes an optimal consensus model to derive weights for linguistic preference relations (LPRs). Two indexes, an individual-to-group consensus index ( ICI) and a collective consensus index ( CCI), are introduced. An iterative algorithm is presented to describe the consensus reaching process. By changing the weights and modifying a pair of individuals' comparison judgments-which have largest deviation value to the group judgments-the consensus reaching process can terminate, while both ICI and CCI are controlled with predefined thresholds. The algorithm aims to preserve the decision makers' original information as much as possible. The model and algorithm are then extended to handle the uncertain additive LPRs. Finally, two examples are given to show the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

The paper written by Robert Winkler titled "Research Directions in Decision Making Under Uncertainty" discusses a field of research that embraces many disciplines and has importance for the conduct of human affairs. Its essential focus is the question of how reasonable people, faced with complex, ill-defined and intractable decision problems, should direct their efforts toward identifying courses of action that will be as wise and beneficial as possible under the circumstances surrounding them. The restriction to decision making under uncertainty is hardly any restriction at all. The paper focuses on foundations and axiomatics and on the more practical problems of measuring factors.

The increasing production of oil and gas in the marine environment and the growing participation of companies of different nationalities and sizes require the use of tools to support the sustainable management of offshore oil and gas production units. This paper presents the results of the application of a sustainability indicator system, developed from the identification of the economic, environmental, social, and operational impacts of the activities of these production units. The sustainability performances of 3 oil and gas production units operating in the Brazilian marine environment were compared to the performance of one considered ideal, through the application of the PROMETHEE II and ordinal COPELAND multicriteria methods. The indicator system applied favored the analysis of the sustainability management of offshore oil and gas production activity in a multidimensional approach, considering the points of view of experts from various areas of knowledge, and proved to be a reliable tool to support the sustainable management of these offshore production units. [ABSTRACT FROM AUTHOR]

The formal mathematical structure for decision making under uncertainty was first expressed in Savage's axioms over 60 years ago. But while the underlying normative concepts for decision making under uncertainty remain constant, the practice of applying these concepts in real‐world settings, as conducted by decision analysis (DA) specialists working with agencies and interested parties, has seen a major transformation in recent decades. The purpose of this article is to provide perspectives that characterize and interpret how DA practice for societal risk management questions has grown and is being transformed over the last 40 years. It addresses a series of themes for parsing changes in how DA has evolved toward more flexible approaches, moving beyond strict theoretical assumptions and constrained settings, and addresses multiple interested parties to provide insights rather than a single correct answer. The article clarifies the path from the initial DA formulation as a set of normative axioms, through gradual change into what is now the most flexible and least restrictive form of policy analysis. The article shows how the practice of DA for societal risks has become more attuned to a wide array of interests and perspectives, more behaviorally informed, more creative, and more informative for governance process. It addresses the following themes: the evolution in the basic orientation of DA, the increasingly important role of stakeholders in DA practice, the importance and value of key problem‐structuring techniques, and evolution in approaches for eliciting values and technical judgments. [ABSTRACT FROM AUTHOR]

It is generally believed that risk-averse managers will not accept unfair gambles and therefore may not have the incentive to invest in high-risk projects, products or technology. This paper argues that this is not necessarily so. Rational, risk-averse managers with sufficient preference for positive skewness may undertake projects with payoff distributions that are unfair gambles. Furthermore, the minimum requited payoff is shown to be less for managers with preference for positive skewness than otherwise. Thus, a risk-averse manager with preference for positive skewness may accept potentially innovative high-risk projects that are rejected by those without such preference. [ABSTRACT FROM AUTHOR]

If the standard decision analysis approach was applied to a competitive situation, then the decision maker would attempt to assess a subjective probability distribution over all possible actions by the competitors or rivals. However, in assessing this probability distribution, the decision maker would quickly find hint/herself in an infinite regress. There is a range of possible dependencies between the decision maker's decision and the rival's action that lead to such a regress. This paper presents an approach to overcoming this infinite regress in performing decision analyses of competitive situations with a "pure" infinite regress. In a pure infinite regress there is a dependency between the decision maker's decision and the rival's choice of action: if the decision maker prefers one alternative to any other, then the rival's actions will reflect a belief by the rival that the preferred alternative is morn likely to be the decision maker's decision. The approach builds the decision maker's preferred rank order of alternatives, while simultaneously constructing a consistent model of the rival's beliefs about the decision maker's decision. [ABSTRACT FROM AUTHOR]

This research investigates whether the knowledge-based decision support system (KBDSS), paradigm provides the necessary supporting structure and developmental framework for product development evaluation. To address the research questions posed in this study, it is necessary to develop and implement KBDSS's at specific decision points along the product development cycle. This paper describes the design, development, and implementation of a KBDSS lo support a product development manager's decision concerning full-scale development of a new product. From the systems design perspective, this paper addresses the integration and innovative use of a variety of techniques for knowledge acquisition, modeling, and processing. The approach utilized obtains the benefits of normative modeling as well as the flexibility and developmental advantages of knowledge-based systems. Since its implementation, the system has been successfully used by a development manager to support his recommendation for an ongoing project. His complete satisfaction with this system served as the impetus for the design and development of a multi-expert system which was implemented at the strategic level. [ABSTRACT FROM AUTHOR]

Machine learning methods are currently the object of considerable study by the artificial intelligence community. Research on machine learning carries implications for decision making in that it seeks computational methods that mimic input-output behaviors found in classes of decision-making examples. At the same time, research in statistics and econometrics has resulted in the development of qualitative response models that can be applied to the same kind of problems addressed by machine-learning models—particularly those that involve a classification decision. This paper presents the theoretical structure of a generalized qualitative-response model and compares its performance to two seminal machine-learning models in two problem domains associated with audit decision making. The results suggest that the generalized qualitative-response model may be a useful alternative for certain problem domains. [ABSTRACT FROM AUTHOR]

A recent Decision Sciences paper considered maximizing the probability of achieving a profit target in a two-product newsboy problem. Numerical solutions to this problem revealed some intriguing properties, but the authors were unable to analytically explain many of their results. This paper presents an analytical solution procedure to this problem for the case of uniformly distributed demands. The analytical structure reveals more intriguing properties and these properties are proven and explained. [ABSTRACT FROM AUTHOR]

Decision analysis was used to study negotiations in the health care context. This paper found that analytical methods could answer several important questions related to complex negotiations, including whether contracts promote the interest of both parties, whether a decision aid could better meet the priorities of both parties, and whether one negotiator is more successful than the other in repeated negotiations. The paper concluded that micro-health care negotiations can be traced and studied with existing mathematical theories of negotiation. [ABSTRACT FROM AUTHOR]

This paper examines the statistical distribution of exchange rates for eight major currencies for the post-1973 floating rate period. The results show that spot rates, forward rates, and ex-post risk premia afl exhibit significant, persistent, but varying deviations from normality, and that the risk premia in forward rates reflect investor preferences for skewness and investor aversion towards standard deviation and kurtosis. These results imply that foreign currency forecasting and hedging practices, mean-variance portfolio analysis, pricing of foreign currency options, and other research involving exchange rates should account for these significant deviations from normality. [ABSTRACT FROM AUTHOR]

A major issue in value-function assessment is the possibility of receiving an indefinite response from a decision maker to a question about preferences. The conventional treatment requires a single, definite response from the decision maker. This approach either assumes that response error is zero or accepts the final solution as only approximate, without knowing how approximate it might be. In this paper we examine the issue of indefinite responses, define it more precisely, and present a method for incorporating it into decision making. The method is adaptive in that it is iterative and interactive with the decision maker who, at each iteration, is provided with information regarding the potential advantage of answering more trade-off questions and/or answering questions more carefully to reduce response error. In contrast to other approaches that "force" rationality and decisiveness, we work with irrationality and indecisiveness on the part of the decision maker. The method is demonstrated for the case of concave, increasing value functions. [ABSTRACT FROM AUTHOR]

This paper describes a useful extension of the well-known, single-period inventory or newsboy problem. Given a fixed number of identical seats available on a scheduled airline flight, what percentage should be offered for early sale at a predetermined discount fare and what percentage reserved for later sale at a higher full fare? This two-tiered pricing strategy with early discount pricing might be appropriate in any situation in which the price sensitivity of the inventoried items decreases as the end of the period approaches. Similar to the newsboy problem solution, the decision rule that maximizes expected profit is expressed as a simple function of the percentage difference in the two fares and two carefully defined probabilities. [ABSTRACT FROM AUTHOR]