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General Eisenhower's decisions to postpone and, one day later, to launch the "D-Day" invasion of Normandy are a gripping illustration of sequential decisions under uncertainty, suitable for any introductory decision analysis class. They're also the archetypal example of weather-sensitive decision making using a forecast. This paper develops a framework for analyzing weather-sensitive decisions with a focus on the less-familiar strategic decisions that determine how forecasts are produced and what operational alternatives are available so that decision makers can extract value from forecasts. We tell the story of the decisions made in the months before D-Day regarding how to set up the forecasting process and the myriad decisions implicating nation-level resources that prepared Allied forces not just to invade, but to hold open that decision until the last possible hour so that Eisenhower and his staff could use the critical forecasts. Finally, we overview the current state of the weather-forecasting enterprise, the current challenges of interest to decision analysts, and what this means for decision analysts seeking opportunities to help the weather enterprise improve forecasts and to help operational decision makers extract more value from modern weather forecasts.

The methodology presented here identifies an approach to accurately and economically analyze the effects on risk of various containment performance issues. Although this method facilitates the evaluation of potential containment improvements, it does so while utilizing the significant amount of information accumulated by the U.S. NRC Reactor Risk Reference Program. The use of hindsight and the acceptance of point estimate quantifications of risks allows the proposed methodology to be scrutable and understandable to the community as well as relatively simple and inexpensive to apply. A study of containment venting strategies was used to demonstrate the capabilities of the simplified containment event tree methodology. However, the methodology is flexible enough for a wide range of risk evaluations.

I: Introduction to Control Theory: Methods and Algorithms -- 1. Developments of Control Theory in Macroeconomics -- 2. Linear Controllability: Results and Open Questions -- 3. A System Theoretic Approach to the Theory of Economic Policy -- 4. Software for Economic Optimal Control Models -- II: Recent Developments of Control Theory: Objective Function Specification -- 5. Interactive Vector Optimization as a Complement to Optimal Control in Econometric Decision Models -- 6. Risk Reduction and the Robustness of Economic Policies -- 7. Optimal Economic Policies under a Crawling-Peg Exchange -- 8. Some Remarks on Forward Programming -- 9. Utility and Uncertainty in Intertemporal Choice -- 10. Gradient Methods for FIML Estimation of Econometric Models -- III: Recent Developments of Control Theory: A Game Theoretic Approach -- 11. Methods for the Simultaneous Use of Multiple Models in Optimal Policy Design -- 12. Optimal Policy Design in Interdependent Economies -- 13. Hierarchical Games for Macroeconomic Policy Analysis -- 14. A Dynamic and Stochastic Model of Price Leadership -- 15. Quality and Reputation Policies of Duopolists under Asymmetric Information -- IV: Economic and Econometric Analysis By Control Methods -- 16. A System Approach to Insurance Company Management -- 17. Capital Theoretics, Business Cycles and Feedback Policy: An Experiment in Macroeconomic Control -- 18. Exogeneity and Control.

AbstractThis paper discusses the main ideas of a new approach to discrete multi‐criteria comparison and choice from a small set of alternatives and its implementation as the Intelligent Decision Support System (IDSS) UniComBOS. The approach has been developed within the Verbal Decision Analysis paradigm. While seeking to use psychologically correct information on a decision maker's (DM's) preferences, the approach allows to take into account the individual DM's ability to deal with multi‐criteria alternatives. This is facilitated by a special graphical user interface, using colour differentiation of preferences, which assists a DM in making comparisons and provides the possibility for additional checks on the consistency of her/his answers. As in most multi‐criteria decision‐making aids, multi‐criteria descriptions of the alternatives are decomposed into their parts upon the least possible number of criteria ('units'). The DM makes pair‐wise comparison starting from single‐criterion 'units', and then the number of criteria in a 'unit' is incremented one‐by‐one, until either the best alternative is found by the newly introduced rule of U‐Dominance, or it is obvious that the comparison of the given multi‐criteria 'units' poses a great challenge to the DM. In the latter case, the system would not be able to find the single best alternative, but it will indicate a set of alternatives that are incomparable with each other and that are preferable for DM in comparison to any alternative not included in this set. Copyright © 2006 John Wiley & Sons, Ltd.

AbstractDecision making in food safety is a complex process that involves several criteria of different nature like the expected reduction in the number of illnesses, the potential economic or health‐related cost, or even the environmental impact of a given policy or intervention. Several multicriteria decision analysis (MCDA) algorithms are currently used, mostly individually, in food safety to rank different options in a multifactorial environment. However, the selection of the MCDA algorithm is a decision problem on its own because different methods calculate different rankings. The aim of this study was to compare the impact of different uncertainty sources on the rankings of MCDA problems in the context of food safety. For that purpose, a previously published data set on emerging zoonoses in the Netherlands was used to compare different MCDA algorithms: MMOORA, TOPSIS, VIKOR, WASPAS, and ELECTRE III. The rankings were calculated with and without considering uncertainty (using fuzzy sets), to assess the importance of this factor. The rankings obtained differed between algorithms, emphasizing that the selection of the MCDA method had a relevant impact in the rankings. Furthermore, considering uncertainty in the ranking had a high influence on the results. Both factors were more relevant than the weights associated with each criterion in this case study. A hierarchical clustering method was suggested to aggregate results obtained by the different algorithms. This complementary step seems to be a promising way to decrease extreme difference among algorithms and could provide a strong added value in the decision‐making process.

This second edition is extensively revised throughout with expanded discussion of modeling fundamentals and coverage of advances in model calibration and uncertainty analysis that are revolutionizing the science of groundwater modeling. The text is intended for undergraduate and graduate level courses in applied groundwater modeling and as a comprehensive reference for environmental consultants and scientists/engineers in industry and governmental agencies. Explains how to formulate a conceptual model of a groundwater system and translate it into a numerical model