Managing uncertainty in decision support models foreword to the special issue

http://www.sciencedirect.com/science/article/B6V8S-4KGPP34-2/1/b8b0563410520cbad9402d23e6ee42e

Managing uncertainty in decision support models foreword to the special issue

http://www.sciencedirect.com/science/article/B6V8S-4KGPP34-2/1/b8b0563410520cbad9402d23e6ee42e

Integrating uncertainty into public energy research and development decisions

Public energy research and development (R&D) is recognized as a key policy tool for transforming the world’s energy system in a cost-effective way. However, managing the uncertainty surrounding technological change is a critical challenge for designing robust and cost-effective energy policies. The design of such policies is particularly important if countries are going to both meet the ambitious greenhouse-gas emissions reductions goals set by the Paris Agreement and achieve the required harmonization with the broader set of objectives dictated by the Sustainable Development Goals. The complexity of informing energy technology policy requires, and is producing, a growing collaboration between different academic disciplines and practitioners. Three analytical components have emerged to support the integration of technological uncertainty into energy policy: expert elicitations, integrated assessment models, and decision frameworks. Here we review efforts to incorporate all three approaches to facilitate public energy R&D decision-making under uncertainty. We highlight emerging insights that are robust across elicitations, models, and frameworks, relating to the allocation of public R&D investments, and identify gaps and challenges that remain

Water Quality Modeling: A Review of the Analysis of Uncertainty

This paper addresses very important issues of uncertainty in water quality modeling. However, the issue of uncertainty is important not only for those who are interested in developing or using water quality models, but also for a wide audience of researchers involved in environmental modeling. Although the author discusses issues which are not investigated in the framework of the project "Decision Support Systems for Managing Large International Rivers," this does not mean that the problems discussed in the paper are irrelevant to the scope of the project. His comprehensive review provides interesting and important information and may stimulate a critical evaluation of the concepts and opinions presented

BOARD INVITED REVIEW: Prospects for improving management of animal disease introductions using disease-dynamic models

Management and policy decisions are continually made to mitigate disease introductions in animal populations despite often limited surveillance data or knowledge of disease transmission processes. Science-based management is broadly recognized as leading to more effective decisions yet application of models to actively guide disease surveillance and mitigate risks remains limited. Disease-dynamic models are an efficient method of providing information for management decisions because of their ability to integrate and evaluate multiple, complex processes simultaneously while accounting for uncertainty common in animal diseases. Here we review disease introduction pathways and transmission processes crucial for informing disease management and models at the interface of domestic animals and wildlife. We describe how disease transmission models can improve disease management and present a conceptual framework for integrating disease models into the decision process using adaptive management principles. We apply our framework to a case study of African swine fever virus in wild and domestic swine to demonstrate how disease-dynamic models can improve mitigation of introduction risk. We also identify opportunities to improve the application of disease models to support decision-making to manage disease at the interface of domestic and wild animals. First, scientists must focus on objective-driven models providing practical predictions that are useful to those managing disease. In order for practical model predictions to be incorporated into disease management a recognition that modeling is a means to improve management and outcomes is important. This will be most successful when done in a cross-disciplinary environment that includes scientists and decisionmakers representing wildlife and domestic animal health. Lastly, including economic principles of value-of-information and cost-benefit analysis in disease-dynamic models can facilitate more efficient management decisions and improve communication of model forecasts. Integration of disease-dynamic models into management and decision-making processes is expected to improve surveillance systems, risk mitigations, outbreak preparedness, and outbreak response activities

Support in decision-making under uncertainty: a project risk assessment tool development and supplementary advances

A foremost dispute that persists on the contemporary world’s agenda is change. The on-going social/technological/economic changes create a competitive and challenging environment for companies to endure. To benefit from these changes, world economies partially depend on emerging Small and Medium Enterprises (SMEs) and their adaptability skills, and subsequently the development of an integrated capability to innovate has become the prime strategy for most of SMEs to subsist and grow. However, innovation and change are always somewhat bonded to an inherent risk development, which subsequently brings on the necessity of a revision of risk management approaches in innovative processes, whose importance SMEs tend to disregard. Additionally, little efforts have been made to improve and create empirical models, metrics and tools to assist SMEs managing latent risks in their innovative projects. This work seeks to present and discuss a solution to support SMEs in engaging on systematic risk management practices, which consists on an integrated risk assessment and response support web-based tool - Spotrisk® - designed for SMEs. On the other hand, an inherent subjectivity is linked with risk management and identification processes, due to uncertainty trait of its nature, for each individual perceives situations according to his own idiosyncrasy, which brings complications in normalizing risk profiles and procedures. This essay aims to bring insights concerning the support in decision-making processes under uncertainty, by addressing issues related with the risk behavior character among individuals. To address such issues, subjects of neuroscience or psychology are explored and models to identify such character are proposed, as well as models to improve presented tool. This work attempts to go beyond the restrictive aim of endeavoring on technical improvement dissertation, and in embraces an exploratory conceptualization concerning micro, small and medium businesses’ traits regarding risk characters and project risk assessment tools

Supporting Situation Awareness and Decision Making in Weather Forecasting

Weather forecasting is full of uncertainty, and as in domains such as air traffic control or medical decision making, decision support systems can affect a forecaster’s ability to make accurate and timely judgments. Well-designed decision aids can help forecasters build situation awareness (SA), a construct regarded as a component of decision making. SA involves the ability to perceive elements within a system, comprehend their significance, and project their meaning into the future in order to make a decision. However, how SA is affected by uncertainty within a system has received little attention. This tension between managing uncertainty, situation assessment, and the impact that technology has on the two, is the focus of this dissertation. To address this tension, this dissertation is centered on the evaluation of a set of coupled models that integrate rainfall observations and hydrologic simulations, coined “the FLASH system” (Flooded Locations and Simulated Hydrographs project). Prediction of flash flooding is unique from forecasting other weather-related threats due to its multi-disciplinary nature. In the United States, some weather forecasters have limited hydrologic forecasting experience. Unlike FLASH, current flash flood forecasting tools are based upon rainfall rates, and with the recent expansion into coupled rainfall and hydrologic models, forecasters have to learn quickly how to incorporate these new data sources into their work. New models may help forecasters to increase their prediction skill, but no matter how far the technology advances, forecasters must be able to accept and integrate the new tools into their work in order to gain any benefit. A focus on human factors principles in the design stage can help to ensure that by the time the product is transitioned into operational use, the decision support system addresses users’ needs while minimizing task time, workload, and attention constraints. This dissertation discusses three qualitative and quantitative studies designed to explore the relationship between flash flood forecasting, decision aid design, and SA. The first study assessed the effects of visual data aggregation methods on perception and comprehension of a flash flood threat. Next, a mixed methods approach described how forecasters acquire SA and mitigate situational uncertainty during real-time forecasting operations. Lastly, the third study used eye tracking assessment to identify the effects of an automated forecasting decision support tool on SA and information scanning behavior. Findings revealed that uncertainty management in forecasting involves individual, team, and organizational processes. We make several recommendations for future decision support systems to promote SA and performance in the weather forecasting domain

Taming Uncertainty in the Assurance Process of Self-Adaptive Systems: a Goal-Oriented Approach

Goals are first-class entities in a self-adaptive system (SAS) as they guide the self-adaptation. A SAS often operates in dynamic and partially unknown environments, which cause uncertainty that the SAS has to address to achieve its goals. Moreover, besides the environment, other classes of uncertainty have been identified. However, these various classes and their sources are not systematically addressed by current approaches throughout the life cycle of the SAS. In general, uncertainty typically makes the assurance provision of SAS goals exclusively at design time not viable. This calls for an assurance process that spans the whole life cycle of the SAS. In this work, we propose a goal-oriented assurance process that supports taming different sources (within different classes) of uncertainty from defining the goals at design time to performing self-adaptation at runtime. Based on a goal model augmented with uncertainty annotations, we automatically generate parametric symbolic formulae with parameterized uncertainties at design time using symbolic model checking. These formulae and the goal model guide the synthesis of adaptation policies by engineers. At runtime, the generated formulae are evaluated to resolve the uncertainty and to steer the self-adaptation using the policies. In this paper, we focus on reliability and cost properties, for which we evaluate our approach on the Body Sensor Network (BSN) implemented in OpenDaVINCI. The results of the validation are promising and show that our approach is able to systematically tame multiple classes of uncertainty, and that it is effective and efficient in providing assurances for the goals of self-adaptive systems

Structuring Decisions Under Deep Uncertainty

Innovative research on decision making under ‘deep uncertainty’ is underway in applied fields such as engineering and operational research, largely outside the view of normative theorists grounded in decision theory. Applied methods and tools for decision support under deep uncertainty go beyond standard decision theory in the attention that they give to the structuring of decisions. Decision structuring is an important part of a broader philosophy of managing uncertainty in decision making, and normative decision theorists can both learn from, and contribute to, the growing deep uncertainty decision support literature