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Writing a chapter on settlement geography in American Geography: Inventory and Prospect in 1954, Clyde Kohn observed that `` No analytical framework has yet been developed for settlement geography comparable to location principles in industrial geography or nationalism in political geography. This is a goal that has not yet been achieved. '' In the years that have elapsed since this comment was made, there have been momentous developments in the geographic analyzes of settlement patterns, events that have not only led to a significant reorientation of the geographer's research on the subject, but have also suggested several new topics of methodological debate within the discipline. Most of these developments have been focused by the reflector beam of a high interest in theoretical and quantitative work. The topics of discussion that revolve around the question of whether or not geographers should concern themselves with mathematical statistical analysis, are mostly dead topics today; to the extent that we claim to be sociologists we must use the language of science and mathematics. But there are some interesting and related questions that are still pertinent, particularly regarding the geographic analysis of settlement patterns. To what degree, for example, has there been a net gain in our understanding of settlement patterns and processes as a direct consequence of the large amounts of data analysis and empirical work that has been done on this topic? More significantly, in what sense has statistical analysis been related to theoretical hypotheses and models? It is easy to show, for example, that several probability models, each with different generating functions, can serve as an adequate description of a certain group of data. We must be guided by theory in selecting the appropriate model and in effect giving direction and meaning to most of our statistical analyzes. To what degree, then, has there been the development of a body of theory on human settlement patterns? In other words, how much progress has been made towards the goal identified by Kohn in 1954, that of developing an analytical framework for the study of human settlement patterns? These are the issues towards which this study is directed; For the most part, the discussion is geared toward a review of the work being done, but alternative methods are occasionally suggested. In the final section some implications of this geographic analysis for planning are discussed. ; Al escribir un capítulo sobre la geografía de los asentamientos en la obra que lleva por título American Geography: Inventory and Prospect, en 1954, Clyde Kohn observo que ''Todavía no se ha desarrollado marco analítico alguno para la geografía de los asentamientos comparable a los principios de ubicación en la geografía industrial o al nacionalismo en la geografía política. Este es un objetivo que aún no se ha logrado''. En los años que han transcurrido desde que se hizo este comentario, se han producido desarrollos trascendentales en los análisis geográficos de los patrones de asentamientos, eventos que no solo han acarreado una reorientación significativa de la investigación del geógrafo sobre el particular, sino también han sugerido varios temas nuevos de debate metodológico dentro de la disciplina. La mayoría de estos desarrollos han estado enfocados por el haz reflector de un alto interés en el trabajo teórico y cuantitativo. Los temas de discusión que giran en torno de la cuestión de si los geógrafos deberían o no preocuparse del análisis estadístico matemático, son su mayoría temas muertos hoy en día; hasta el grado en que alegamos ser sociólogos debemos usar el lenguaje de la ciencia y de la matemática. Pero hay algunas preguntas interesantes y relacionadas que son aún pertinentes, en particular en cuanto al análisis geográfico de los patrones de asentamiento. Hasta qué grado, por ejemplo, ¿ha habido una ganancia neta en nuestra comprensión de patrones y procesos del asentamiento como consecuencia directa de las grandes cantidades de análisis de datos y de trabajo empírico que se ha realizado sobre este tema? Mas significativamente, ¿en qué sentido ha estado relacionado el análisis estadístico con las hipótesis y modelos teóricos? Es fácil de demostrar, por ejemplo, que varios modelos de probabilidad, cada uno con diferentes funciones generadoras, pueden servir como descripción adecuada de un determinado grupo de datos. Debemos ser guiados por la teoría en la selección del modelo apropiado y en efecto dar dirección y significado a la mayoría de nuestros análisis estadísticos. ¿Hasta qué grado, entonces, ha existido el desarrollo de un cuerpo teórico sobre los patrones de asentamientos humanos? En otras palabras, ¿cuánto se ha progresado hacia el objetivo identificado por Kohn en 1954, el del desarrollo de un marco analítico para el estudio de patrones de asentamientos humanos? Estos son los temas hacia los cuales se dirige este estudio; en su mayor parte, la discusión está orientada hacia un repaso del trabajo que se está llevando a cabo, pero ocasionalmente se sugieren métodos alternativos. En la sección final se discuten algunas implicaciones de este análisis geográfico para la planificación

This paper investigates the impact of transition risk on a firm's low-carbon production. As the world is facing global climate changes, the Intergovernmental Panel on Climate Change (IPCC) has set the idealized carbon-neutral scenario around 2050. In the meantime, many carbon reduction scenarios, known as Shared Socioeconomic Pathways (SSPs) have been proposed in the literature for different production sectors in more comprehensive socioeconomic context. In this paper, we consider, on the one hand, a firm that aims to optimize its emission level under the double objectives of maximizing its production profit and respecting the emission mitigation scenarios. Solving the penalized optimization problem provides the optimal emission according to a given SSP benchmark. On the other hand, such transitions affect the firm's credit risk. We model the default time by using the structural default approach. We are particularly concerned with how the adopted strategies by following different SSPs scenarios may influence the firm's default probability.

This paper investigates the impact of transition risk on a firm's low-carbon production. As the world is facing global climate changes, the Intergovernmental Panel on Climate Change (IPCC) has set the idealized carbon-neutral scenario around 2050. In the meantime, many carbon reduction scenarios, known as Shared Socioeconomic Pathways (SSPs) have been proposed in the literature for different production sectors in more comprehensive socioeconomic context. In this paper, we consider, on the one hand, a firm that aims to optimize its emission level under the double objectives of maximizing its production profit and respecting the emission mitigation scenarios. Solving the penalized optimization problem provides the optimal emission according to a given SSP benchmark. On the other hand, such transitions affect the firm's credit risk. We model the default time by using the structural default approach. We are particularly concerned with how the adopted strategies by following different SSPs scenarios may influence the firm's default probability.

This paper investigates the impact of transition risk on a firm's low-carbon production. As the world is facing global climate changes, the Intergovernmental Panel on Climate Change (IPCC) has set the idealized carbon-neutral scenario around 2050. In the meantime, many carbon reduction scenarios, known as Shared Socioeconomic Pathways (SSPs) have been proposed in the literature for different production sectors in more comprehensive socioeconomic context. In this paper, we consider, on the one hand, a firm that aims to optimize its emission level under the double objectives of maximizing its production profit and respecting the emission mitigation scenarios. Solving the penalized optimization problem provides the optimal emission according to a given SSP benchmark. On the other hand, such transitions affect the firm's credit risk. We model the default time by using the structural default approach. We are particularly concerned with how the adopted strategies by following different SSPs scenarios may influence the firm's default probability.

This paper analyses the effects of financial globalization on growth in developing countries, focusing on its interaction with exchange rate volatility. Based on dynamic panel data models and the two-step system Generalized Method of Moments (system GMM) estimator, it replicates the method of Gaies et al. (2019a; 2019b) and extends it by exploring a new spillover effect of financial globalization in terms of exchange rate volatility measured by six different indicators. The findings show the positive influence of investment-globalization on growth through the traditional channel of capital accumulation and by reducing the negative impact of exchange rate volatility. These impacts are not ensured by indebtedness-globalization, thereby shedding light on the government's decision in developing countries on foreign capital control policy. These results are robust to changes in the estimator and variables used.

Our article provides a better understanding of risk management strategies for all energy market stakeholders. A good knowledge of optimal risk hedging strategies is not only important for energy companies but also for regulators and policy makers in a context of climate emergency. Indeed, the electricity sector is key to achieve energy and ecological transition. Electricity companies should be on frontline of climate change struggle. Taking the perspective of electricity retailers, we analyze a range of portfolios made of forward contracts and/or power plants for specific hourly clusters based on electricity market data from the integrated German-Austrian spot market. We prove that intra-day hedging with forward contracts is sub-optimal compared to financial options and physical assets. By demonstrating the contribution of intra-day hedging with options and physical assets, we highlight the specificities of electricity markets as hourly markets with strong volatility during peak hours. By simulating optimal hedging strategies, our article proposes a range of new portfolios for electricity retailers to manage their risks and reduce their sourcing costs. A lower hedging cost enables to allocate more resources to digitalization and energy efficiency services to take into account customers' expectations for more climate-friendly retailers. This is a virtuous circle. Retailers provide high value-added energy efficiency services so that consumers consume less. The latter contributes to reach electricity reduction targets to fight climate warming.

Data obtained from ISSR amplification may readily be extracted but only allows us to know, for each gene, if a specific allele is present or not. From this partial information we provide a probabilistic method to reconstruct the pedigree corresponding to some families of diploid cultivars. This method consists in determining for each individual what is the most likely couple of parent pair amongst all older individuals, according to some probability measure. The construction of this measure bears on the fact that the probability to observe the specific alleles in the child, given the status of the parents does not depend on the generation and is the same for each gene. This assumption is then justified from a convergence result of gene frequencies which is proved here. Our reconstruction method is applied to a family of 85 living accessions representing the common broom Cytisus scoparius.

Data obtained from ISSR amplification may readily be extracted but only allows us to know, for each gene, if a specific allele is present or not. From this partial information we provide a probabilistic method to reconstruct the pedigree corresponding to some families of diploid cultivars. This method consists in determining for each individual what is the most likely couple of parent pair amongst all older individuals, according to some probability measure. The construction of this measure bears on the fact that the probability to observe the specific alleles in the child, given the status of the parents does not depend on the generation and is the same for each gene. This assumption is then justified from a convergence result of gene frequencies which is proved here. Our reconstruction method is applied to a family of 85 living accessions representing the common broom Cytisus scoparius.

Data obtained from ISSR amplification may readily be extracted but only allows us to know, for each gene, if a specific allele is present or not. From this partial information we provide a probabilistic method to reconstruct the pedigree corresponding to some families of diploid cultivars. This method consists in determining for each individual what is the most likely couple of parent pair amongst all older individuals, according to some probability measure. The construction of this measure bears on the fact that the probability to observe the specific alleles in the child, given the status of the parents does not depend on the generation and is the same for each gene. This assumption is then justified from a convergence result of gene frequencies which is proved here. Our reconstruction method is applied to a family of 85 living accessions representing the common broom Cytisus scoparius.

David J. Caswell (" Analysis of National Strategies to Counter a Country's Nuclear Weapons Program ") is an officer in the U.S. Air Force and a research affiliate with the Center for International Security and Cooperation at Stanford University. David has served in various positions ranging from operational simulation development to operations analysis for national intelligence. He currently serves as an operations analyst in support of regional air and space employment in the Pacific. David received his Ph.D. in management science and engineering at Stanford University. His current research continues to apply computer science and operations research methods for gaining insights for nuclear policy and other international security issues. Address: http://www.stanford.edu/group/ERRG/davidc1.htm ; e-mail: david.caswell33@gmail.com . Kjell Hausken (" Governments' and Terrorists' Defense and Attack in a T-Period Game ") has since 1999 been a professor of economics and societal safety at the University of Stavanger, Norway. His research fields are strategic interaction, risk analysis, reliability, conflict, and terrorism. He holds a Ph.D. (thesis: "Dynamic Multilevel Game Theory") from the University of Chicago (1990–1994), and was a postdoc at the Max Planck Institute for the Studies of Societies (Cologne) from 1995 to 1998 and a visiting scholar at Yale School of Management from 1989 to 1990. He holds a doctorate program degree in administration from the Norwegian School of Economics and Business Administration, and an M.Sc. degree in electrical engineering from the Norwegian Institute of Technology. He completed military service at the Norwegian Defence Research Establishment, has published 110 articles, and is on the editorial board for Theory and Decision and Defence and Peace Economics. Address: Faculty of Social Sciences, University of Stavanger, N-4036 Stavanger, Norway; e-mail: kjell.hausken@uis.no . Ronald A. Howard (" Analysis of National Strategies to Counter a Country's Nuclear Weapons Program ") is a professor of management science and engineering in the School of Engineering at Stanford University. Professor Howard directs teaching and research in the Decision Analysis Program of the department, and is the director of the Decisions and Ethics Center, which examines the efficacy and ethics of social arrangements. He defined the profession of decision analysis in 1964 and has supervised more than 80 doctoral theses in decision analysis and related areas. His experience includes dozens of decision analysis projects that range over virtually all fields of application, from investment planning to research strategy, and from hurricane seeding to nuclear waste isolation. He has been a consultant to several companies and was a founding director and chairman of Strategic Decisions Group. He is president of the Decision Education Foundation, which he and colleagues founded to teach decision skills to young people. He has written four books, dozens of technical papers, and provided editorial service to seven technical journals. His society affiliations have included the Institute of Electrical and Electronics Engineers (Fellow); The Institute of Management Sciences, which he served as president, and the Institute for Operations Research and the Management Sciences (INFORMS) (Fellow). Continuing research interests are improving the quality of decisions, life-and-death decision making, and the creation of a coercion-free society. In 1986 he received the Frank P. Ramsey Medal "for Distinguished Contributions in Decision Analysis" from the Decision Analysis Special Interest Group of the Operations Research Society of America (the predecessor to the Decision Analysis Society of INFORMS). In 1998 he received from INFORMS the first award for the Teaching of Operations Research/Management Science Practice. In 1999 he was elected to the National Academy of Engineering. Address: Management Science and Engineering, Huang Engineering Center, 475 Via Ortega, Stanford University, Stanford, CA 94305-4121; e-mail: rhoward@stanford.edu . Joseph B. ("Jay") Kadane (" Partial-Kelly Strategies and Expected Utility: Small Edge Asymptotics ") is Leonard J. Savage University Professor of Statistics and Social Sciences, Emeritus, at Carnegie Mellon University. He received a B.S. in mathematics from Harvard and a Ph.D. in statistics from Stanford. He was recently elected to the American Academy of Arts and Sciences. His theoretical interests center on subjective Bayesian theory. His current applied interests include Internet security, medicine, law, physics, marketing, and air pollution. He serves as an expert witness in legal cases. His most recent book is Principles of Uncertainty, which is scheduled to be released in May 2011 by Chapman and Hall and will be available free on the Web for any noncommercial purpose. Address: Department of Statistics, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213; e-mail: kadane@stat.cmu.edu . Konstantinos V. Katsikopoulos (" Psychological Heuristics for Making Inferences: Definition, Performance, and the Emerging Theory and Practice ") holds a Ph.D. in industrial engineering and operations research from the University of Massachusetts Amherst and is currently a senior research scientist at the Center for Adaptive Behavior and Cognition of the Max Planck Institute for Human Development. He has been a visiting assistant professor of operations research at the Naval Postgraduate School and of systems engineering at the Massachusetts Institute of Technology. He has made contributions to the theory of bounded rationality and its applications to decisions "in the wild" in fields such as engineering design and medicine. Address: Max Planck Institute for Human Development, Lentzeallee 94, 14195 Berlin, Germany; e-mail: katsikop@mpib-berlin.mpg.de . L. Robin Keller (" Investment and Defense Strategies, Heuristics, and Games: From the Editor … ") is a professor of operations and decision technologies in the Merage School of Business at the University of California, Irvine. She received her Ph.D. and M.B.A. in management science and her B.A. in mathematics from the University of California, Los Angeles. She has served as a program director for the Decision, Risk, and Management Science Program of the U.S. National Science Foundation (NSF). Her research is on decision analysis and risk analysis for business and policy decisions and has been funded by NSF and the U.S. Environmental Protection Agency. Her research interests cover multiple attribute decision making, riskiness, fairness, probability judgments, ambiguity of probabilities or outcomes, risk analysis (for terrorism, environmental, health, and safety risks), time preferences, problem structuring, cross-cultural decisions, and medical decision making. She is currently Editor-in-Chief of Decision Analysis, published by the Institute for Operations Research and the Management Sciences (INFORMS). She is a Fellow of INFORMS and has held numerous roles in INFORMS, including board member and chair of the INFORMS Decision Analysis Society. She is a recipient of the George F. Kimball Medal from INFORMS. She has served as the decision analyst on three National Academy of Sciences committees. Address: The Paul Merage School of Business, University of California, Irvine, Irvine, CA 92697-3125; e-mail: lrkeller@uci.edu . Jeryl L. Mumpower (" Playing Squash Against Ralph Keeney: Should Weaker Players Always Prefer Shorter Games? ") is Director of the Master of Public Service and Administration Program at the Bush School of Government and Public Service at Texas A&M University, where he holds the Joe R. and Teresa Lozano Long Chair in Business and Public Policy. Previously he was at the Nelson A. Rockefeller College of Public Affairs and Policy, State University of New York at Albany, where he was a professor of public administration, public policy, public health, and information science and served in a variety of University-level administrative positions. His previous experience includes six years as a program director and policy analyst at the National Science Foundation. Mumpower received his B.A. from the College of William and Mary and his Ph.D. in social and quantitative psychology from the University of Colorado, Boulder. He is author or editor of nine books and more than 50 book chapters and articles. His research has addressed basic and applied topics in negotiation and bargaining, environmental policy, individual and group decision-making processes, the use of scientific expertise in public policy making, and risk analysis and management. Address: Bush School of Government and Public Service, Texas A&M University, 1092 Allen Building, 4220 TAMU, College Station, TX 77843-4220; e-mail: jmumpower@bushschool.tamu.edu . M. Elisabeth Paté-Cornell (" Analysis of National Strategies to Counter a Country's Nuclear Weapons Program ") is the Burt and Deedee McMurtry Professor and Chair, Department of Management Science and Engineering at Stanford University. Her specialty is engineering risk analysis with application to complex systems (including space systems and medical systems). Her research has focused on explicit consideration of human and organizational factors in the analysis of failure risks and, recently, on the use of game theory in risk analysis. Applications in the last few years have included counterterrorism and nuclear counterproliferation problems. She is a member of the National Academy of Engineering and of several boards (Aerospace, Draper, InQtel, etc.). She was a member of the President's Intelligence Advisory Board until December 2008. She holds an engineer degree (Applied Math/CS) from the Institut Polytechnique de Grenoble (France), and an M.S. in Operations Research and a Ph.D. in Engineering-Economic Systems, both from Stanford University. Address: Management Science and Engineering, Huang Engineering Center, 475 Via Ortega, Stanford University, Stanford, CA 94305-4121; e-mail: mep@stanford.edu . Jun Zhuang (" Governments' and Terrorists' Defense and Attack in a T-Period Game ") is an assistant professor of industrial and systems engineering at the University at Buffalo, the State University of New York. He has been a faculty member at SUNY Buffalo since he obtained his Ph.D. in summer 2008 from the University of Wisconsin–Madison. Dr. Zhuang's long-term research goal is to integrate operations research and game theory to better prepare for, mitigate, and manage both natural and man-made hazards. Other areas of interests include health care, transportation, logistics and supply chain management, and sustainability. Dr. Zhuang's research has been supported by the U.S. National Science Foundation, and by the U.S. Department of Homeland Security through the Center for Risk and Economic Analysis of Terrorism Events. Address: Department of Industrial and Systems Engineering, 403 Bell Hall, University at Buffalo, The State University of New York, Buffalo, NY 14260; e-mail: jzhuang@buffalo.edu .

Starting from the pioneering papers by Charnes, Cooper and Rhodes (CCR model) and Banker, Charnes and Cooper (BCC model), a large number of papers concerning Data Envelopment Analysis (DEA) with outputs uncertainty appeared in the literature. In particular, chance-constrained programming is the most used technique to include noise variations in data and to solve data envelopment analysis problems with uncertainty in data. Chance-constrained programming admits random data variations and permits constraint violations up to specified probability limits, allowing linear deterministic equivalent formulations in case a normal distribution of the data uncertainty is assumed. The standard DEA models rely on the assumption that inputs are minimized and outputs are maximized. However, both desirable and undesirable (e.g., pollutants or wastes) output factors may be present. The undesirable and desirable outputs should be treated differently when we evaluate the production performance: if inefficiency exists in the production, the undesirable pollutants should be reduced to improve efficiency. In order to include undesirable factors in DEA models, according to the literature, two different approaches can be used to model undesirable factors: one group of DEA models treats them as inputs, whereas a second group considers them as undesirable outputs. DEA models with undesirable factors are particularly suitable for models where several production inputs and desirable and undesirable outputs are taken into account, in order to provide an eco-efficiency measure. In this Ph.D thesis alternative DEA models, which consider both uncertain and undesirable outputs, are proposed and studied. In particular, in the first part of this thesis two different models with uncertain outputs and deterministic inputs are proposed with the aim to move away the classical chance-constrained method and to obtain a more accurate DMU ranking whatever situation occurs. Specifically speaking, the proposed models remove the hypothesis of normal data distribution and use a scenario generation approach to include data perturbations. For the sake of completeness, these models are compared with two further ones based on an expected value approach, where uncertainty is managed by means of the expected values of random factors both in the objective function and in the constraints. Deeply speaking, the main difference between the two proposed models and the expected value approaches lies in their mathematical formulation. In the new models, based on the scenario generation approach, the constraints concerning efficiency level are expressed for each scenario. On the other hand, in the expected value models the constraints are satisfied in expected value. As a consequence, the models proposed in the thesis result to be more selective in finding a ranking of efficiency, thus becoming useful strategic management tools aimed to determine a restrictive efficiency score ranking. In the second part of this study, we focus on environmental policy and eco-efficiency. Nowadays, one of the most intensively discussed concepts in the international political debate is, in fact, the concept of sustainability and the need for eco-efficient solutions that enable the production of goods and services with less energy and resources and with less waste and emissions (eco-efficiency). In particular, we consider the environmental impact of CO2 in cement and clinker production processes. Cement industry is, in fact, responsible for approximately 5% of the current worldwide CO2 emissions. DEA models can provide an appropriate methodological approach for developing eco-efficiency indicators. A cross-country comparison of the eco-efficiency level of the worldwide cement industry is presented by applying both a data envelopment analysis and a directional distance function approach. These tools result to be particularly suitable for models where several production inputs and desirable and undesirable outputs are taken into account. Strong and weak disposability assumptions are analyzed in order to evaluate the impact of environmental regulations interpreted as the cost of regulation. The few papers appeared in the literature of eco-efficiency in cement production analyze the emission performance trends only from an interstate point of view. In this thesis a worldwide study has been carried on, covering 90% of the world's cement production by means of 21 countries, European (EU) and non-European (non-EU) ones. The obtained results show that the efficiency level mainly depends on decisions to invest in alternative raw materials and alternative fuels, both in the case of regulated countries and in the case of voluntary emission-trading schemes. This study highlights, both at national and international levels, the possibility of reducing CO2 emissions and expanding cement production. The use of alternative raw materials, alternative fuels and the possibility of producing blended cements, which require less energy consumption and reduce pollutant emissions, seem to be appropriate means. Environmental regulations can provide incentives in terms of tax exemption benefits or more restrictive pollutant limits. Finally, we try to answer to the following questions: do undesirable factors modify the efficiency levels of cement industry? Is it reasonable to omit CO2 emissions in evaluating the performances of the cement sector in different countries? In order to answer to these questions, alternative formulations of standard data envelopment analysis model and directional distance function are compared both in presence and in absence of undesirable factors. This analysis shows that the presence of undesirable factors greatly affects efficiency levels. Efficiency levels are influenced by investments in best available technologies and by the utilization of alternative fuels and raw materials in cement and clinker production processes. The original results of this Ph.D. thesis have been collected in the following research papers: • Riccardi R. and R. Toninelli. Data Envelopment Analysis with outputs uncertainty. Journal of Information & Optimization Sciences, to appear. • Riccardi R., Oggioni G. and R. Toninelli. The cement industry: eco-efficiency country comparison using Data Envelopment Analysis. Journal of Statistics & Management Systems, accepted for publication. • Riccardi R., Oggioni G. and R. Toninelli. Eco-efficiency of the world cement industry: A Data Envelopment Analysis. Energy Policy, Vol. 39, Issue 5, p. 2842-2854, 2011, available online at: http://dx.doi.org/10.1016/j.enpol.2011.02.057 • Riccardi R., Oggioni G. and R. Toninelli. Evaluating the efficiency of the cement sector in presence of undesirable output: a world based Data Envelopment Analysis. Technical Report n. 344, Department of Statistics and Applied Mathematics, University of Pisa, 2011, submitted to Resource and Energy Economics. The research topic considered in this thesis shows many different lines for future developments. In particular, from a theoretical point of view, starting from the models proposed in Riccardi and Toninelli (2011), we are studying for a bi-objective like DEA formulation where both uncertainty desirable and undesirable factor are taken into account. As regards the applicative aspects, we are also studying and applying bootstrap techniques to manage uncertainty and generate empirical distributions of efficiency scores, in order to capture and analyze the sensitivity of samples with respect to changes in the estimated frontier.

Das erste Kapitel widmet sich einer Familie von Netzerzeugungsspielen. Netzerzeugungsspiele dienen der grobkörnigen Analyse von Netzwerken für die detailierte Daten nicht vorliegen, oder grundsätzlich nicht gewonnen werden können. Ein typisches Beispiel sind Soziale Netze. In den hier untersuchten Spielen wird das Netzwerk von den Knoten als den Spielern selbst gebildet. Paare von Knoten können Kanten zwischen sich einrichten oder abbrechen. Jedem Knoten entstehen feste Kosten für jede zum ihm inzidente Kante. Ein Knoten profitiert jedoch global vom Netzwerk. Jeder Knoten zieht einen Nutzen daraus, zu jedem anderen Knoten direkt oder indirekt verbunden zu sein. Dieser Nutzen ist abhängig von der Distanz der beiden Knoten im Netzwerk. Bisher konnten nur Spiele analysiert werden, in denen sich diese Distanz linear auf den Nutzen auswirkt. Wir können u.a. den Preis der Anarchie bei exponentiellen Nutzenfunktionen exakt auf 2 bestimmen. Dieses Ergebnis beruht auf einer graphentheoretischen Charakterisierung stabiler Zustände. Hiermit gelingt es auch zu zeigen, dass diese Prozesse zykeln können. Im zweiten Kapitel beschreiben wir einen approximativen Zulässigkeitstest für das sporadische Echtzeit-Ablaufplanungsproblem auf mehreren identischen Prozessoren. Eine Instanz diese Problems besteht aus einer potenziell unendlichen Folge von Aufträgen, die von einer festen Anzahl Prozessoren bearbeitet werden. Die Zuweisung der Aufträge auf die Prozessoren muss auf Grund des Echtzeitcharakters der Anwendung gemäß einer einfachen Politik erfolgen. Da die Anwendungen zudem sicherheits-kritisch sind, muss im Voraus sichergestellt werden, dass jeder Auftrag fristgerecht abgeschlossen wird. Angesichts der enormen Anzahl an Aufträgen (die häufig sogar als unendlich modelliert wird) muss ein Zulässigkeitstest dies anhand einer sehr kompakten, insbesondere endlichen, im sporadischen Fall unvollständigen Beschreibung der Instanz beurteilen. Unser Test entscheidet entweder, dass für eine Instanz kein zulässiger Ablaufplan existiert, oder dass die Instanz durch einen Ablaufplan, bei dem stets der Auftrag mit der nächstliegenden Frist als erster bearbeitet wird, auf annähernd doppelt so schnellen Prozessoren zuverlässig gelöst wird. Die Existenz eines solchen approximativen Zulässigkeitstest wurde seit den neunziger Jahren vermutet. Seither ist ebenfalls bekannt, dass die hier in Anspruch genommene Geschwindigkeitserhöhung die kleinst mögliche ist, da bereits die einfache Ablaufregel diese Erhöhung erforderlich macht. Der vorliegende Test ist damit der erste bestmögliche Test seiner Art für mehr als einen Prozessor. Im letzten Kapitel beschäftigen wir uns mit Robuster Optimierung. Robuste Optimierung ist ein spezieller Ansatz im Bereich der Optimierung unter Informationsdefizit. Anstelle verlässlicher Daten ist eine (im Allgemeinen unendliche) Menge von Szenarien gegeben. Ein leitendes Beispiel sind verspätungsresistente Fahrpläne. Die klassische robuste Optimierung sucht nach Lösungen, die in jedem dieser Szenarien zulässig sind. Dies führt zu unzumutbar konservative Lösungen. Klassische robuste Fahrpläne etwa weisen unverhältnismäßig große Pufferzeiten auf. Wir entwickeln dagegen den Begriff der Wiederherstellungfreundlichkeit (Recoverable Robustness). Eine wiederherstellungsfreundliche Lösung lässt sich mit geringem Aufwand in jedem anzunehmenden Szenario in eine zulässige Lösung verwandeln. Wir zeigen, dass dieser Begriff die Vorteile der klassischen Robustheit, insbesondere die Gütegarantie und die Lösbarkeit erhält, und dennoch weit weniger konservative Lösungen als der klassische Ansatz ermöglicht. Die Wiederherstellungsfreundlichkeit erlaubt zudem die Anwendung auf Praxisprobleme, die der klassischen robusten Optimierung vorenthalten sind. Eine wichtige Spezialisierung dieses Begriffs ist die robuste Netzpufferung. Dies ist ein verhältnismäßig einfach zu implementierendes, exaktes Verfahren, das in der Zwischenzeit bereits Anwendung in der Gleiszuweisung gefunden hat. Dort hat die Methode eine Reduzierung der Verspätung um ein Viertel bewirkt. Für ganzzahlige Varianten der robusten Netzpufferung beweisen wir u.a. Nicht-Approximierbarkeit. Neben den Anwendungs- und Modellierungsvorteilen eröffnet die Wiederherstellungsfreundlichkeit eine neue, polyedrische Sichtweise auf die lineare Optimierung unter Informationsdefizit. Diese Perspektive erlaubt es im Vorhinein den Bereich aussergewöhnlich großer Störungen zu bestimmen, in dem wiederherstellungsfreundliche Lösung immer noch beherrschbar bleiben. Wir führen an einer Reihe von Beispielen die Anwendung der Wiederherstellungsfreundlichkeit insbesondere bei der Planung von Verkehrssystemen vor. Im Zuge dessen bestimmen wir auch die Komplexität des Verspätungsmanagements, und modellieren einen durch die Anwendung motivierten alternativen Ansatz mit geringerer Komplexität. ; We discuss three different, classical concepts of reliability: the analysis of network creation games, feasibility tests for real-time scheduling, and robust optimization. In each case a substantial extension of the concept yields a significant advance both with respect to applicability and to mathematical insight. All concepts belong to the broader field of combinatorial and linear optimization and discrete mathematics. Still, each topic belongs to a separate special research area, and requires a different method. In the first chapter we analyze a family of network creation games. Network creation games are a high-level tool to understand the structure of networks which cannot be apprehended explicitly and in full detail. Typical examples of such a networks are social network. We consider settings in which the network is created by selfish, myopic players corresponding to the vertices of the network. Each pair of vertices can establish an edge among them. A vertex has to cover a fixed cost for each of its incident edges. In contrast to this local cost the vertices have a global benefit from the network. Each vertex has an income from any other vertex to which it is directly or indirectly connected. The value of this income is given by some function of the distance between the two vertices in the network. An edge is stable when both incident vertices have a better payoff in the current network with the edge than without it. Hitherto, such a setting could only be analyzed for an income function that depends linearly on the distance. We consider exponentially decaying income functions. This process has originally been proposed in economics to analyze social networks of cooperation or information exchange. We show that the process has a positive probability to cycle. We reduce the creation rule functions to graph theoretic criteria. Moreover, these criteria can be evaluated locally. This allows us to reveal the structure of all stable states. In addition, the question for the price of anarchy can be reduced to counting the maximum number of edges of a stable graph. This allows to determine the price of anarchy exactly. It equals 2. In the second chapter we propose an approximate feasibility test for sporadic, multiprocessor real-time scheduling. An instance of this problem consists of a potentially infinite set of jobs that have to be executed on fixed set of processors. The real-time character of the applications requires simple scheduling policies. Moreover, the applications are typically safety-critical. Therefore, it must be guaranteed in advance that every job meets its deadline. Due to the enormous number of jobs this test must be based on a compact, in the sporadic case even vague description of the job sequence. For any sporadic real-time scheduling instance our feasibility test either returns that the given instance cannot be scheduled at all on the given platform of processors, or that it is scheduled feasibly by the Earliest-Deadline-First policy (EDF) on processors with almost double-speed. Also it has been apparent since then that almost double speed is the least possible speed-up factor, because EDF already requires this speed-up to feasibly schedule every feasible instance. Robust Optimization is the subject of the last chapter. It is a specific concept for optimization under imperfect information. Instead of reliable data we are given a (usually infinite set) of scenarios. A guiding example for our work are delay resistant timetables. Classical Robustness seeks solutions that are feasible in each scenario. This yields unacceptably conservative solutions. Classical robust timetables for example make excessive use of buffer times. In contrast we develop the notion of Recoverable Robustness. A recovery robust solution can be recovered in every likely scenario by a limited effort. We show that this notion maintains the advantages of classical robustness, in particular tractability and a quality guarantee, while it produces substantially less conservative solutions. Recoverable Robustness is applicable to real-world problems that cannot be modeled convincingly in the classical terms. An important special case of this notion is robust network buffering. It has been used for train platforming in the mean-time, reducing delay by a forth. For the integer variants of network buffering we prove inapproximability. Besides, we unfold a polyhedral perspective to recoverable robustness. This yields a new approach to linear programming under uncertainty. It allows to a priori determine the region of large disturbances in which a recovery robust solutions stays recoverable. We exemplify Recoverable Robustness with a number of applications mostly from public transport design. In this context we prove PSPACE-hardness for the delay management problem. We also model delay resistant timetabling with respect to a simpler strategy for delay management which is motivated by the application.

Ali E. Abbas (" From the Editors: Brainstorming, Multiplicative Utilities, Partial Information on Probabilities or Outcomes, and Regulatory Focus ") is an associate professor in the Department of Industrial and Enterprise Systems Engineering at the University of Illinois at Urbana–Champaign. He received an M.S. in electrical engineering (1998), an M.S. in engineering economic systems and operations research (2001), a Ph.D. in management science and engineering (2003), and a Ph.D. (minor) in electrical engineering, all from Stanford University. He worked as a lecturer in the Department of Management Science and Engineering at Stanford and worked in Schlumberger Oilfield Services, where he held several international positions in wireline logging, operations management, and international training. He has also worked on several consulting projects for mergers and acquisitions in California, and cotaught several executive seminars on decision analysis at Strategic Decisions Group in Menlo Park, California. His research interests include utility theory, decision making with incomplete information and preferences, dynamic programming, and information theory. Dr. Abbas is a senior member of the IEEE and a member of the Institute for Operations Research and the Management Sciences (INFORMS). He is also an associate editor for the Decision Analysis and Operations Research journals of INFORMS. Email: aliabbas@illinois.edu . Kash Barker (" Decision Trees with Single and Multiple Interval-Valued Objectives ") is an assistant professor in the School of Industrial and Systems Engineering at the University of Oklahoma. He holds a Ph.D. in systems engineering from the University of Virginia, where he was a research assistant in the Center for Risk Management of Engineering Systems, and B.S. and M.S. degrees in industrial engineering from the University of Oklahoma. His primary research interests lie in modeling interdependent economic systems and decision making for large-scale system sustainment, with research funded by the National Science Foundation, the Federal Highway Administration, and the Army Research Office. Email: kashbarker@ou.edu . J. Eric Bickel (" From the Editors: Brainstorming, Multiplicative Utilities, Partial Information on Probabilities or Outcomes, and Regulatory Focus " and " A Simulation-Based Approach to Decision Making with Partial Information ") is an assistant professor in the Graduate Program in Operations Research (Department of Mechanical Engineering) at the University of Texas at Austin. In addition, Professor Bickel is a fellow in both the Center for International Energy and Environmental Policy (CIEEP) and the Center for Petroleum Asset Risk Management (CPARM). His research interests include the theory and practice of decision analysis and its application in the energy and climate-change arenas. His work has addressed the modeling of probabilistic dependence, value of information, scoring rules, calibration, risk preference, decision education, decision making in sports, and climate engineering as a response to climate change. Prior to returning to academia, Professor Bickel was a senior engagement manager for Strategic Decisions Group. He has consulted around the world in a range of industries, including oil and gas, electricity generation/transmission/delivery, energy trading and marketing, commodity and specialty chemicals, life sciences, financial services, and metals and mining. Professor Bickel is the vice president/president-elect of the Decision Analysis Society and an associate editor for Decision Analysis. He holds an M.S. and Ph.D. from the Department of Engineering–Economic Systems at Stanford University. Email: ebickel@mail.utexas.edu . Vicki M. Bier (" From the Editors: Brainstorming, Multiplicative Utilities, Partial Information on Probabilities or Outcomes, and Regulatory Focus ") is a full professor in the Department of Industrial and Systems Engineering at the University of Wisconsin–Madison, where she is currently department chair and also directs the Center for Human Performance and Risk Analysis. She is also the past president of the Decision Analysis Society and is an associate editor for Decision Analysis. Her research interests include applications of operations research, risk analysis, and decision analysis to problems of homeland security and critical infrastructure protection. Email: bier@engr.wisc.edu . Samuel E. Bodily (" Multiplicative Utilities for Health and Consumption ") is the John Tyler Professor of Business Administration at the University of Virginia's Darden School. He has published textbooks and more than 40 articles in journals ranging from Harvard Business Review to Management Science. His publications relate to decision and risk analysis, multiattribute utility, forecasting, strategy modeling, revenue management, and eStrategy. Along with Casey Lichtendahl, he was runner-up for the 2012 Decision Analysis Publication Award. He has edited special issues of Interfaces on Decision and Risk Analysis, and Strategy Modeling and Analysis. Professor Bodily has published well over 120 cases, including a couple of the 10 best-selling cases at Darden. He received the Distinguished Casewriter Wachovia award from Darden in 2005 and three other best case or research Wachovia awards. Professor Bodily is faculty leader for the executive program Strategic Thinking and Action. He is the course-head of and teaches in a highly valued first-year M.B.A. course in decision analysis; has a successful second-year elective, Management Decision Models; and has taught eStrategy and Strategy. He is a past winner of the Decision Sciences International Instructional Award. He has served as chair of the INFORMS Decision Analysis Society. He has taught numerous executive education programs for Darden and private companies, has consulted widely for business and government entities, and has served as an expert witness. Professor Bodily was on the faculties of MIT Sloan School of Management and Boston University and has been a visiting professor at INSEAD Singapore, Stanford University, and the University of Washington. He has Ph.D. and S.M. degrees from Massachusetts Institute of Technology and a B.S. in physics from Brigham Young University. Email: bodilys@virginia.edu . David V. Budescu (" From the Editors: Brainstorming, Multiplicative Utilities, Partial Information on Probabilities or Outcomes, and Regulatory Focus ") is the Anne Anastasi Professor of Psychometrics and Quantitative Psychology at Fordham University. He held positions at the University of Illinois at Urbana–Champaign and the University of Haifa, and visiting positions at Carnegie Mellon University, University of Gothenburg, the Kellogg School at Northwestern University, the Hebrew University, and the Israel Institute of Technology (Technion). His research is in the areas of human judgment, individual and group decision making under uncertainty and with incomplete and vague information, and statistics for the behavioral and social sciences. He is on the editorial boards of Applied Psychological Measurement, Decision Analysis (associate editor), Journal of Behavioral Decision Making, Journal of Mathematical Psychology, Journal of Experimental Psychology: Learning, Memory & Cognition (2000–2003), Multivariate Behavioral Research, Organizational Behavior and Human Decision Processes (1992–2002), and Psychological Methods (1996–2000). He is past president of the Society for Judgment and Decision Making (2000–2001), fellow of the Association for Psychological Science, and an elected member of the Society of Multivariate Experimental Psychologists. Email: budescu@fordham.edu . John C. Butler (" From the Editors: Brainstorming, Multiplicative Utilities, Partial Information on Probabilities or Outcomes, and Regulatory Focus ") is a clinical associate professor of finance and the academic director of the Energy Management and Innovation Center in the McCombs School of Business at the University of Texas at Austin, the outgoing secretary/treasurer of the INFORMS Decision Analysis Society, and an associate editor for Decision Analysis. Professor Butler received his Ph.D. in management science and information systems from the University of Texas at Austin in 1998. His research interests involve the use of decision science models to support decision making, with a particular emphasis on decision and risk analysis models with multiple-performance criteria. Professor Butler has consulted with a number of organizations regarding the application of decision analysis tools to a variety of practical problems. Most of his consulting projects involve use of Visual Basic for Applications and Excel to implement complex decision science models in a user-friendly format. Email: john.butler2@mccombs.utexas.edu . Enrico Diecidue (" From the Editors: Brainstorming, Multiplicative Utilities, Partial Information on Probabilities or Outcomes, and Regulatory Focus ") is an associate professor of Decision Sciences at INSEAD (France) and holds a Ph.D. from the CentER (Center for Economic Research), Tilburg University, the Netherlands. Since 2001 he has been a resident faculty member at INSEAD, except for 2008–2009 when he was a visiting professor at Wharton and 2010–2011 when he was on sabbatical at the Erasmus School of Economics (the Netherlands). His main research interests are in individual decision making under uncertainty, health decisions, and experimental economics. He is interested in the role of regret, aspiration levels, and time in individual decisions. His current research is also addressing the role of groups in complex and ambiguous decisions. Professor Diecidue's research has appeared in leading journals in economics and management. He is on the editorial board of Journal of Risk and Uncertainty and is an associate editor for Decision Analysis. He teaches M.B.A., executive M.B.A., and executive participants on topics such as uncertainty, data and judgment, decision making under uncertainty, risk management, and management decision making. He has won teaching awards at INSEAD and Wharton. Email: enrico.diecidue@insead.edu . Robin L. Dillon-Merrill (" From the Editors: Brainstorming, Multiplicative Utilities, Partial Information on Probabilities or Outcomes, and Regulatory Focus ") is an associate professor in the McDonough School of Business at Georgetown University. Professor Dillon-Merrill seeks to understand and explain how and why people make the decisions that they do under conditions of uncertainty and risk. This research specifically examines critical decisions that people have made following near-miss events in situations with severe outcomes (i.e., hurricane evacuation, terrorism, NASA mission management, etc.). She has received research funding from the National Science Foundation, NASA, the Department of Defense, and the Department of Homeland Security through the University of Southern California's National Center for Risk and Economic Analysis for Terrorism Events. She has served as a risk analysis and project management expert on several National Academies Committees, including the review of the New Orleans regional hurricane protection projects and the application of risk-analysis techniques to securing the Department of Energy's special nuclear materials. She is an associate editor for Decision Analysis. Email: rld9@georgetown.edu . Raimo P. Hämäläinen (" From the Editors: Brainstorming, Multiplicative Utilities, Partial Information on Probabilities or Outcomes, and Regulatory Focus ") is a professor of operations research and director of the Systems Analysis Laboratory at Aalto University, Finland. He received his M.Sc. and Dr. Tech. degrees in systems theory and applied mathematics from the Helsinki University of Technology. His research interests include decision analysis, risk and game theory, and experimental economics, as well as dynamic optimization with aerospace applications. He is widely known for his work in environmental decision making and energy policy. He is the designer of widely used decision analysis software, including the first Web-based value tree software, Web-HIPRE; Smart-Swaps; and the Joint Gains negotiation support system. He has recently introduced the concept of Systems Intelligence, which opens a new perspective to organizational learning and personal growth. He is on the editorial board of a number of journals, including Decision Analysis (associate editor), European Journal of Operational Research, Journal of Group Decision and Negotiation, and EURO Journal on Decision Processes. Dr. Hämäläinen has received the Edgeworth-Pareto Award of the International Society for Multiple Criteria Decision Making. He is also the honorary president of the Finnish Operations Research Society. Email: raimo.hamalainen@aalto.fi . Ralph L. Keeney (" Value-Focused Brainstorming ") is a research professor emeritus at the Fuqua School of Business at Duke University. His education includes a B.S. in engineering from the University of California, Los Angeles, and a Ph.D. in operations research from Massachusetts Institute of Technology. His research interests are in the areas of decision making and risk analysis. He has applied such work to important personal decisions and as a consultant for private and public organizations addressing corporate management problems, environmental and risk studies, and decisions involving life-threatening risks. Prior to joining the Duke faculty, Professor Keeney was a faculty member in Management and Engineering at MIT and at the University of Southern California, a research scholar at the International Institute for Applied Systems Analysis in Austria, and the founder of the decision and risk analysis group of a large geotechnical and environmental consulting firm. Professor Keeney is the author of many books and articles, including Value-Focused Thinking, Decisions with Multiple Objectives, coauthored with Howard Raiffa, and Smart Choices, coauthored with John S. Hammond and Howard Raiffa, which has been translated into 15 languages. Dr. Keeney was awarded the Ramsey Medal for distinguished contributions in decision analysis by the Decision Analysis Society and is a member of the U.S. National Academy of Engineering. Email: keeney@duke.edu . L. Robin Keller (" From the Editors: Brainstorming, Multiplicative Utilities, Partial Information on Probabilities or Outcomes, and Regulatory Focus ") is a professor of operations and decision technologies in the Merage School of Business at the University of California, Irvine. She received her Ph.D. and M.B.A. in management science and her B.A. in mathematics from the University of California, Los Angeles. She has served as a program director for the Decision, Risk, and Management Science Program of the U.S. National Science Foundation (NSF). Her research is on decision analysis and risk analysis for business and policy decisions and has been funded by NSF and the U.S. Environmental Protection Agency. Her research interests cover multiple attribute decision making, riskiness, fairness, probability judgments, ambiguity of probabilities or outcomes, risk analysis (for terrorism, environmental, health, and safety risks), time preferences, problem structuring, cross-cultural decisions, and medical decision making. She is the outgoing Editor-in-Chief of Decision Analysis, published by the Institute for Operations Research and the Management Sciences (INFORMS). She is a Fellow of INFORMS and has held numerous roles in INFORMS, including board member and chair of the INFORMS Decision Analysis Society. She is a recipient of the George F. Kimball Medal from INFORMS. She has served as the decision analyst on three National Academy of Sciences committees. She has been appointed to the editorial board of the new EURO Journal on Decision Processes. Email: lrkeller@uci.edu . Anton Kühberger (" Explaining Risk Attitude in Framing Tasks by Regulatory Focus: A Verbal Protocol Analysis and a Simulation Using Fuzzy Logic ") is a professor of psychology at the University of Salzburg, Department of Cognition and Development, and a member of the Center of Neurocognitive Research at the University of Salzburg. His research interests include the following areas: judgment and decision making, in particular framing, and the role of risk, ambiguity, and uncertainty in decision making; thinking and reasoning, in particular counterfactual thinking and the notion of rationality; and social cognition, in particular the foundation of the understanding of oneself and others either by a theory of mind or by simulation. In addition, he is interested in the development of scientific methods such as verbal protocol analysis and in understanding the role statistics for the accumulation of knowledge. He is a member of the editorial board of several scholarly journals and currently is vice-dean of the Faculty of Natural Sciences at the University of Salzburg. Email: anton.kuehberger@sbg.ac.at . Kenneth C. Lichtendahl Jr. (" From the Editors: Brainstorming, Multiplicative Utilities, Partial Information on Probabilities or Outcomes, and Regulatory Focus " and " Multiplicative Utilities for Health and Consumption ") is an assistant professor of business administration at the Darden School of Business at the University of Virginia. He is an associate editor for Decision Analysis. He received his Ph.D. in decision sciences from the Fuqua School of Business at Duke University. His research focuses on decision analysis, Bayesian statistics, game theory, and dynamic programming. Email: lichtendahlc@darden.virginia.edu . Jason R. W. Merrick (" From the Editors: Brainstorming, Multiplicative Utilities, Partial Information on Probabilities or Outcomes, and Regulatory Focus ") is a professor in the Department of Statistical Sciences and Operations Research at Virginia Commonwealth University. He has a D.Sc. in operations research from George Washington University. He teaches courses in decision analysis, risk analysis, and simulation. His research is primarily in the area of decision analysis and Bayesian statistics. He has worked on projects ranging from assessing maritime oil transportation and ferry system safety, the environmental health of watersheds, and optimal replacement policies for rail tracks and machine tools. He has received grants from the National Science Foundation, the Federal Aviation Administration, the U.S. Coast Guard, the American Bureau of Shipping, British Petroleum, and Booz Allen Hamilton, among others. He has also performed training for Infineon Technologies, Wyeth Pharmaceuticals, and Capital One Services. He is an associate editor for Decision Analysis and the EURO Journal on Decision Processes. Email: jrmerric@vcu.edu . Luis Vicente Montiel (" A Simulation-Based Approach to Decision Making with Partial Information ") is a postdoctoral researcher at the University of Texas at Austin. His main research interest is mathematical modeling for optimization under uncertainty, with a special interest in decision analysis and simulation learning for optimization. His current research is dedicated to providing a theoretical and practical framework for approximating joint distributions under partial information. Luis has a Ph.D. in operations research from the University of Texas, an M.S. in financial engineering from Columbia University, and an M.S. in management science and engineering from Stanford University. Email: lvmontiel@utexas.edu . Jay R. Simon (" From the Editors: Brainstorming, Multiplicative Utilities, Partial Information on Probabilities or Outcomes, and Regulatory Focus ") is an assistant professor in the Defense Resources Management Institute of the Naval Postgraduate School. He holds a Ph.D. in operations and decision technologies from the Merage School of Business at the University of California, Irvine, an M.S. in management science and engineering, and a B.S. in mathematical and computational science from Stanford University. His primary research interest is multiattribute preference modeling, particularly involving outcomes that occur over time, space, or groups of people. His current and recent work includes a prostate cancer decision model, preference models for health decisions, preferences over geographical data, altruistic utility modeling, and multiattribute procurement. He is an associate editor for Decision Analysis and is the webmaster and social media officer for the Decision Analysis Society of INFORMS. Email: jrsimon@nps.edu . Christian Wiener (" Explaining Risk Attitude in Framing Tasks by Regulatory Focus: A Verbal Protocol Analysis and a Simulation Using Fuzzy Logic ") received his doctorate from the University of Salzburg, Austria. His first research area was the application of the EEG in connection with dyslexia. Later he moved to the area of social cognition, where his research was focused on framing and especially the simulation of decision-making behavior using a fuzzy-logic expert system. Since the completion of his dissertation, he has been working as a clinical psychologist with developmentally delayed children. Email: christian.wiener@gmx.at . Kaycee J. Wilson (" Decision Trees with Single and Multiple Interval-Valued Objectives ") completed M.S. and B.S. degrees in the School of Industrial and Systems Engineering at the University of Oklahoma in 2012 and 2010, respectively. Her primary interests lie in healthcare operations and reliability-based decision making, and she holds a National Science Foundation Graduate Research Fellowship. Email: kaycee.j.wilson-1@ou.edu . George Wu (" From the Editors: Brainstorming, Multiplicative Utilities, Partial Information on Probabilities or Outcomes, and Regulatory Focus ") has been on the faculty of the University of Chicago's Booth School of Business since September 1997. His degrees include an A.B. (applied mathematics, 1985), an S.M. (applied mathematics, 1987), and a Ph.D. (decision sciences, 1991), all from Harvard University. Prior to joining the faculty at the University of Chicago, Professor Wu was on the faculty at Harvard Business School. Professor Wu worked as a decision analyst at Procter & Gamble prior to starting graduate school. His research interests include descriptive and prescriptive aspects of decision making, in particular decision making involving risk; cognitive biases in bargaining and negotiation; and managerial and organizational decision making. Professor Wu is a coordinating editor for Theory and Decision, an advisory editor for Journal of Risk and Uncertainty, on the editorial boards of Decision Analysis (associate editor) and the Journal of Behavioral Decision Making, and a former department editor of Management Science. Email: wu@chicagobooth.edu .

Ali E. Abbas (" Decomposing the Cross Derivatives of a Multiattribute Utility Function into Risk Attitude and Value ") received the M.S. degree in electrical engineering, the M.S. degree in engineering economic systems and operations research, the Ph.D. degree in management science and engineering, and the Ph.D. (minor) degree in electrical engineering, all from Stanford University, Stanford, California. He was a lecturer in the Department of Management Science and Engineering at Stanford. He previously worked for Schlumberger Oilfield Services, where he held several international positions in wireline logging, operations management, and international training. He was also involved with several consulting projects for mergers and acquisitions in California, and was a co-teacher of several executive seminars on decision analysis at Strategic Decisions Group, Menlo Park, California. He is currently an associate professor in the Department of Industrial and Enterprise Systems Engineering, University of Illinois at Urbana–Champaign, Champaign. His research interests include utility theory, decision making with incomplete information and preferences, dynamic programming, and information theory. Dr. Abbas is a member of INFORMS, a senior member of the IEEE, an associate editor for Decision Analysis and Operations Research, and an editor of the DA column in education for Decision Analysis Today. Address: Department of Industrial and Enterprise Systems Engineering, College of Engineering, University of Illinois at Urbana–Champaign, 117 Transportation Building, MC-238, 104 South Mathews Avenue, Urbana, IL 61801; e-mail: aliabbas@uiuc.edu . Vicki M. Bier (" Deterring the Smuggling of Nuclear Weapons in Container Freight Through Detection and Retaliation ") holds a joint appointment as professor in the Department of Industrial and Systems Engineering and the Department of Engineering Physics at the University of Wisconsin–Madison, where she chairs the Department of Industrial and Systems Engineering. She has directed the Center for Human Performance and Risk Analysis (formerly the Center for Human Performance in Complex Systems) since 1995. She has more than 20 years of experience in risk analysis for the nuclear power, chemical, petrochemical, and aerospace industries. Before returning to academia, she spent seven years as a consultant at Pickard, Lowe and Garrick, Inc. While there, her clients included the U.S. Nuclear Regulatory Commission, the U.S. Department of Energy, and a number of nuclear utilities, and she prepared testimony for Atomic Safety and Licensing Board hearings on the safety of the Indian Point nuclear power plants. Dr. Bier's current research focuses on applications of risk analysis and related methods to problems of security and critical infrastructure protection, under support from the Department of Homeland Security. She is also currently serving as a special term appointee for the Infrastructure Assurance Center at Argonne National Laboratory. Address: Department of Industrial and Systems Engineering, 1513 University Avenue, University of Wisconsin–Madison, Madison, WI 53706; e-mail: bier@engr.wisc.edu . Robert F. Bordley (" Using Bayes' Rule to Update an Event's Probabilities Based on the Outcomes of Partially Similar Events ") is an INFORMS Fellow and a winner of the best publication award from the Decision Analysis Society as well as five major application awards from General Motors. He is a General Motors Technical Fellow with experience in research, planning, quality, marketing, corporate strategy, and procurement. He is also an adjunct professor at the University of Michigan, Ann Arbor, and was formerly program director of Decision, Risk and Management Sciences at the National Science Foundation. Dr. Bordley has published 75 papers in decision analysis, marketing, and operations management. He has also served as chair of the American Statistical Association's Risk Analysis Section (which now has 1000 members), vice president of the Production and Operations Management Society, and a member of the INFORMS Board and the Decision Analysis Society Council. He earned a Ph.D. and M.S. in operations research and an M.B.A. in finance from the University of California, Berkeley. His primary interests have been in theoretical developments enabling high-impact application of decision analysis in a wide variety of corporate contexts (e.g., engineering design, corporate strategy, procurement, program management, etc.). Address: General Motors, Pontiac Centerpoint Campus North, 585 South Boulevard, Pontiac, MI 48341; e-mail: robert.bordley@gm.com , rbordley@umich.edu . Heidi M. Crane (" Whether to Retest the Lipids of HIV-Infected Patients: How Much Does Fasting Bias Matter? ") is an assistant professor of medicine at the University of Washington (UW) School of Medicine and the associate director of Clinical Epidemiology and Health Services Research at the UW Center for AIDS Research (CFAR), which promotes research comparing the effectiveness of management strategies for HIV-infected patients in routine clinical practice. She is co–principal investigator (PI) of a PROMIS (Patient-Reported Outcomes Measurement Information Systems) National Institutes of Health Roadmap initiative U01 on measuring patient reported outcomes in clinical care for HIV-infected patients and PI of a National Institute of Mental Health R01 project on measuring and improving adherence for HIV-infected patients in clinical care. She is also medical director of the Madison HIV Metabolic clinic, PI of an American Heart Association grant on myocardial infarction and metabolic complications among patients with HIV, and PI of an Agency for Healthcare Research and Quality grant on comparative effectiveness of antihypertensive and lipid-lowering medication among HIV-infected patients. She provides care and training in the clinical care of HIV-infected individuals, and she also mentors junior investigators in HIV research in the UW Division of Infectious Diseases. Dr. Crane is a member of the Data Management Centers for the National Institute of Allergy and Infectious Diseases–funded CFAR Network of Integrated Clinical Systems (CNICS) research platform of real-time electronic health record data for 22,000 patients from eight CFARs across the United States, and the International Epidemiological Databases to Evaluate AIDS project's North American AIDS Cohort Collaboration on Research and Design (NA-ACCORD), which merges data on 110,000 HIV-infected individuals in care at 60 sites across the United States and Canada. Dr. Crane leads the CNICS Patient Reported Outcomes Committee and the CNICS and NA-ACCORD myocardial infarction event adjudication teams. Dr. Crane's research focuses on methods to improve clinical care for HIV-infected individuals as well as metabolic and other chronic comorbidities of HIV. She received her internal medicine residency training from Barnes and Jewish Hospitals, and her B.A., B.S., M.D., M.P.H. and Infectious Disease Fellowship training from the UW. Address: Harborview Medical Center, 325 9th Avenue, Box 359931, Seattle, WA 98104; e-mail: hcrane@u.washington.edu . Naraphorn Haphuriwat (" Deterring the Smuggling of Nuclear Weapons in Container Freight Through Detection and Retaliation ") is a researcher at the National Metal and Materials Technology Center in Thailand. She applies tools including optimization, decision analysis, and process simulation to improve production processes and operations for small and medium enterprises. She earned her Ph.D. from the University of Wisconsin–Madison in the Department of Industrial and Systems Engineering in August 2010. During her doctoral study, she was supported by the Center for Risk and Economic Analysis of Terrorism Events (CREATE) at the University of Southern California, where she conducted game-theoretic studies in the applications of security. She also received an honorable mention in the 2004–2005 University Book Store Academic Excellence Award Competition for a project related to computer security. Address: 114 Thailand Science Park, Paholyothin Road, Klong 1, Klong Luang, Pathumthani 12120, Thailand; e-mail: naraphoh@mtec.or.th . Joseph B. Kadane (" Whether to Retest the Lipids of HIV-Infected Patients: How Much Does Fasting Bias Matter? ") is Leonard J. Savage University Professor of Statistics and Social Sciences, Emeritus, at Carnegie Mellon University. His research focus is on both foundational issues of Bayesian analysis and applications in many settings. These currently include physics, phylogenetics, air pollution, Internet security, law, and medicine, as well as Internet auctions. He also serves as an expert witness in legal matters. Address: Tepper School of Business, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213; e-mail: kadane@andrew.cmu.edu . L. Robin Keller (" From the Editors: Deterrence, Multiattribute Utility, and Probability and Bayes' Updating ") is a professor of operations and decision technologies in the Merage School of Business at the University of California, Irvine. She received her Ph.D. and M.B.A. in management science and her B.A. in mathematics from the University of California, Los Angeles. She has served as a program director for the Decision, Risk, and Management Science Program of the U.S. National Science Foundation (NSF). Her research is on decision analysis and risk analysis for business and policy decisions and has been funded by NSF and the U.S. Environmental Protection Agency. Her research interests cover multiple-attribute decision making, riskiness, fairness, probability judgments, ambiguity of probabilities or outcomes, risk analysis (for terrorism, environmental, health, and safety risks), time preferences, problem structuring, cross-cultural decisions, and medical decision making. She is currently Editor-in-Chief of Decision Analysis, published by the Institute for Operations Research and the Management Sciences (INFORMS). She is a Fellow of INFORMS and has held numerous roles in INFORMS, including board member and chair of the INFORMS Decision Analysis Society. She is a recipient of the George F. Kimball Medal from INFORMS. She has served as the decision analyst on three National Academy of Sciences committees. Address: The Paul Merage School of Business, University of California, Irvine, Irvine, CA 92697-3125; e-mail: lrkeller@uci.edu . Mari M. Kitahata (" Whether to Retest the Lipids of HIV-Infected Patients: How Much Does Fasting Bias Matter? ") is professor of medicine at the University of Washington (UW) School of Medicine, director of Clinical Epidemiology and Health Services Research at the Center for AIDS Research (CFAR), and principal investigator of the UW HIV Cohort. She has provided care and training in the clinical management of HIV-infected individuals for two decades, and she mentors investigators in HIV research in the UW Division of Infectious Diseases. Dr. Kitahata studies the outcomes of care for persons with HIV infection, and her research has elucidated key determinants of increased survival, including care managed by physicians with HIV expertise and earlier initiation of antiretroviral treatment. The need for observational research to complement the invaluable information provided by randomized controlled trials has grown tremendously, which is why she established the CFAR Clinical Epidemiology and Health Services Research program at UW in 1995 and was among the first CFARs in the United States to do so. Dr. Kitahata developed the structure and methods to merge comprehensive HIV patient data and biological specimens from multiple settings into a powerful resource for researchers conducting basic, translational, clinical outcomes/comparative effectiveness, and behavioral/prevention research. She has led efforts to establish networks of national and international HIV research collaborations to address the most pressing questions regarding treatment and outcomes for HIV-infected individuals that cannot be answered through smaller cohort studies. Dr. Kitahata directs the Data Management Centers for the National Institute of Allergy and Infectious Diseases–funded CFAR Network of Integrated Clinical Systems (CNICS) research platform of real-time electronic health record (EHR) data for 22,000 patients from eight CFARs across the United States, and the International Epidemiological Databases to Evaluate AIDS project's North American AIDS Cohort Collaboration on Research and Design (NA-ACCORD), which merges data on 110,000 HIV-infected individuals in care at 60 sites across the United States and Canada. Dr. Kitahata serves on the Board of Directors for the Infectious Diseases Society of America (IDSA) HIV Medicine Association, the U.S. Public Health Service/IDSA Guidelines Committee for Prevention of Opportunistic Infections, and the International Training and Education Center on HIV (I-TECH), where she developed a national EHR system for the Haitian Ministry of Health. Dr. Kitahata received her B.S. from Yale University, M.D. from the University of Pennsylvania, internal medicine residency training at the University of California, San Francisco, and M.P.H. and Fellowship training at the University of Washington, where she was a Robert Wood Johnson Clinical Scholar. Address: University of Washington Medical Center, 1959 NE Pacific Street, UW Box 356423, Seattle, WA 98195-6423; e-mail: kitahata@u.washington.edu . Sanjeev R. Kulkarni (" Aggregating Large Sets of Probabilistic Forecasts by Weighted Coherent Adjustment ") is a professor in the Department of Electrical Engineering at Princeton University. He is also an affiliated faculty member in the Department of Operations Research and Financial Engineering and the Department of Philosophy. Prior to joining Princeton, he was a member of the technical staff at MIT Lincoln Laboratory. During his time at Princeton, he has held visiting or consulting positions with Australian National University, Susquehanna International Group, and Flarion Technologies. Professor Kulkarni has served as an associate editor for the IEEE Transactions on Information Theory, and he is a Fellow of the IEEE. His research interests include statistical pattern recognition, nonparametric statistics, learning and adaptive systems, information theory, wireless networks, and image/video processing. Address: School of Engineering and Applied Science, Princeton University, Princeton, NJ 08544; e-mail: kulkarni@princeton.edu . Daniel N. Osherson (" Aggregating Large Sets of Probabilistic Forecasts by Weighted Coherent Adjustment ") earned his Ph.D. in psychology at the University of Pennsylvania in 1973. Since then he has taught at Stanford University, the University of Pennsylvania, Massachusetts Institute of Technology, Università San Raffael, Rice University, and Princeton University. His work centers on probability judgment and learning. Address: Department of Psychology, Princeton University, Princeton, NJ 08544; e-mail: osherson@princeton.edu . H. Vincent Poor (" Aggregating Large Sets of Probabilistic Forecasts by Weighted Coherent Adjustment ") is dean of the School of Engineering and Applied Science at Princeton University, where he is also the Michael Henry Strater University Professor of Electrical Engineering. He holds a Ph.D. from Princeton. His research interests are in the areas of statistical signal processing, stochastic analysis, and information theory, and their applications to wireless networks and related fields. Among his publications in these areas are the recent books Quickest Detection (Cambridge University Press, 2009) and Information Theoretic Security (NOW Publishers, 2009). Dean Poor is a member of the U.S. National Academy of Engineering and the U.S. National Academy of Sciences, and he is a Fellow of the IEEE, the Institute of Mathematical Statistics, the American Academy of Arts and Sciences, and the Royal Academy of Engineering of the United Kingdom. A former Guggenheim Fellow, recent recognition of his work included the Institution of Engineering and Technology Ambrose Fleming Medal, the IEEE Eric E. Sumner Award, and an honorary D.Sc. from the University of Edinburgh. Address: School of Engineering and Applied Science, Princeton University, Princeton, NJ 08544; e-mail: poor@princeton.edu . Guanchun Wang (" Aggregating Large Sets of Probabilistic Forecasts by Weighted Coherent Adjustment ") received an undergraduate degree in electrical engineering at Shanghai Jiao Tong University. He is currently a Ph.D. student in the Department of Electrical Engineering at Princeton University. His research interests include statistical learning, information retrieval, and judgment aggregation. He also worked as a summer associate for McKinsey's technology practice. Address: School of Engineering and Applied Science, Princeton University, Princeton, NJ 08544; e-mail: guanchun@princeton.edu . Henry H. Willis (" Deterring the Smuggling of Nuclear Weapons in Container Freight Through Detection and Retaliation ") is a professor of policy analysis at the Pardee RAND Graduate School and the associate director of the RAND Homeland Security and Defense Center. His research has applied risk analysis tools to resource allocation and risk management decisions in the areas of public health and emergency preparedness, terrorism and national security policy, energy and environmental policy, and transportation planning. Dr. Willis serves on the editorial board of the journal Risk Analysis and served on the National Academies of Science Committee on Evaluating Testing, Costs, and Benefits of Advanced Spectroscopic Portals. He earned his Ph.D. from the Department of Engineering and Public Policy at Carnegie Mellon University and holds degrees in chemistry and environmental studies from the University of Pennsylvania (B.A.) and in environmental science from the University of Cincinnati (M.A.). Address: RAND Corporation, 4570 Fifth Avenue, Suite 600, Pittsburgh, PA 15213; e-mail: hwillis@rand.org . Xiting (Cindy) Yang (" Whether to Retest the Lipids of HIV-Infected Patients: How Much Does Fasting Bias Matter? ") completed her Ph.D. from Carnegie Mellon University in the area of elicitation, specifically focusing on elicitation of expert knowledge on phylogenies in the format of rooted trees. She is currently a statistical reviewer at the Center for Devices and Radiological Health, U.S. Food and Drug Administration. Her current research focuses on clinical trials and elicitation. Address: U.S. Food and Drug Administration, 10903 New Hampshire Avenue, Building 66, Room 2223, Silver Spring, MD 20993-0002; e-mail: xiting.yang@fda.hhs.gov .

Ali Abbas (" From the Editors… ") is an associate professor in the Department of Industrial and Enterprise Systems Engineering at the University of Illinois at Urbana–Champaign. He received an M.S. in electrical engineering (1998), an M.S. in engineering economic systems and operations research (2001), a Ph.D. in management science and engineering (2003), and a Ph.D. (minor) in electrical engineering, all from Stanford University. He worked as a lecturer in the Department of Management Science and Engineering at Stanford and worked in Schlumberger Oilfield Services from 1991 to 1997, where he held several international positions in wireline logging, operations management, and international training. He has also worked on several consulting projects for mergers and acquisitions in California, and cotaught several executive seminars on decision analysis at Strategic Decisions Group in Menlo Park, California. His research interests include utility theory, decision making with incomplete information and preferences, dynamic programming, and information theory. Dr. Abbas is a senior member of the Institute of Electrical and Electronic Engineers (IEEE) and a member of the Institute for Operations Research and the Management Sciences (INFORMS). He is also an associate editor for Decision Analysis and Operations Research and coeditor of the DA column in education for Decision Analysis Today. Address: Department of Industrial and Enterprise Systems Engineering, College of Engineering, University of Illinois at Urbana–Champaign, 117 Transportation Building, MC-238, 104 South Mathews Avenue, Urbana, IL 61801; e-mail: aliabbas@uiuc.edu . Matthew D. Bailey (" Eliciting Patients' Revealed Preferences: An Inverse Markov Decision Process Approach ") is an assistant professor of business analytics and operations in the School of Management at Bucknell University, and he is an adjunct research investigator with Geisinger Health System. He received his Ph.D. in industrial and operations engineering from the University of Michigan. His primary research interest is in sequential decision making under uncertainty with applications to health-care operations and medical decision making. He is a member of the Institute for Operations Research and the Management Sciences (INFORMS) and the Institute of Industrial Engineers (IIE). Address: School of Management, Bucknell University, 308 Taylor Hall, Lewisburg, PA 17837; e-mail: matt.bailey@bucknell.edu . Anthony M. Barrett (" Cost Effectiveness of On-Site Chlorine Generation for Chlorine Truck Attack Prevention ") is a risk analyst at ABS Consulting in Arlington, Virginia. He holds a Ph.D. in engineering and public policy from Carnegie Mellon University, and he also was a postdoctoral research associate at the Homeland Security Center for Risk and Economic Analysis of Terrorism Events (CREATE) at the University of Southern California. His research interests include risk analysis, risk management, and public policies in a wide variety of areas, including terrorism, hazardous materials, energy and the environment, and natural hazards. Address: ABS Consulting, 1525 Wilson Boulevard, Suite 625, Arlington, VA 22209; e-mail: abarrett@absconsulting.com . Manel Baucells (" From the Editors… ") is a full professor at the Department of Economics and Business of Universitat Pompeu Fabra, Barcelona. He was an associate professor and head of the Managerial Decision Sciences Department at IESE Business School. He earned his Ph.D. in management from the University of California, Los Angeles (UCLA) and holds a degree in mechanical engineering from Polytechnic University of Catalonia (UPC). His research and consulting activities cover multiple areas of decision making including group decisions, consumer decisions, uncertainty, complexity, and psychology. He acts as associate editor for the top journals Management Science, Operations Research, and Decision Analysis. He has received various prizes and grants for his research. In 2001, he won the student paper competition of the Decision Analysis Society. He is the only IESE professor having won both the Excellence Research Award and the Excellence Teaching Award. He has been visiting professor at Duke University, UCLA, London Business School, and Erasmus University. Address: Universitat Pompeu Fabra, Ramon Trias Fargas 25-27, 08005 Barcelona, Spain; e-mail: manel.baucells@upf.edu . J. Eric Bickel (" Scoring Rules and Decision Analysis Education ") is an assistant professor in both the Operations Research/Industrial Engineering Group (Department of Mechanical Engineering) and the Department of Petroleum and Geosystems Engineering at the University of Texas at Austin. In addition, Professor Bickel is a fellow in both the Center for International Energy and Environmental Policy and the Center for Petroleum Asset Risk Management. He holds an M.S. and Ph.D. from the Department of Engineering-Economic Systems at Stanford University and a B.S. in mechanical engineering with a minor in economics from New Mexico State University. His research interests include the theory and practice of decision analysis and its application in the energy and climate-change arenas. His research has addressed the modeling of probabilistic dependence, value of information, scoring rules, calibration, risk preference, education, decision making in sports, and climate engineering as a response to climate change. Prior to joining the University of Texas at Austin, Professor Bickel was an assistant professor at Texas A&M University and a senior engagement manager for Strategic Decisions Group. He has consulted around the world in a range of industries, including oil and gas, electricity generation/transmission/delivery, energy trading and marketing, commodity and specialty chemicals, life sciences, financial services, and metals and mining. Address: Graduate Program in Operations Research, The University of Texas at Austin, 1 University Station, C2200, Austin, TX 78712-0292; e-mail: ebickel@mail.utexas.edu . Vicki M. Bier (" From the Editors… ") holds a joint appointment as a professor in the Department of Industrial and Systems Engineering and the Department of Engineering Physics at the University of Wisconsin–Madison, where she has directed the Center for Human Performance and Risk Analysis (formerly the Center for Human Performance in Complex Systems) since 1995. She has more than 20 years of experience in risk analysis for the nuclear power, chemical, petrochemical, and aerospace industries. Before returning to academia, she spent seven years as a consultant at Pickard, Lowe and Garrick, Inc. While there, her clients included the U.S. Nuclear Regulatory Commission, the U.S. Department of Energy, and a number of nuclear utilities, and she prepared testimony for Atomic Safety and Licensing Board hearings on the safety of the Indian Point nuclear power plants. Dr. Bier's current research focuses on applications of risk analysis and related methods to problems of security and critical infrastructure protection, under support from the Department of Homeland Security. Dr. Bier received the Women's Achievement Award from the American Nuclear Society in 1993, and was elected a Fellow of the Society for Risk Analysis in 1996, from which she received the Distinguished Achievement Award in 2007. She has written a number of papers and book chapters related to uncertainty analysis and decision making under uncertainty, and is the author of two scholarly review articles on risk communication. She served as the engineering editor for Risk Analysis from 1997 through 2001, and has served as a councilor of both the Society for Risk Analysis and the Decision Analysis Society, for which she is currently vice president and president elect. Dr. Bier has also served as a member of both the Radiation Advisory Committee and the Homeland Security Advisory Committee of the U.S. Environmental Protection Agency's Science Advisory Board. Address: Department of Industrial and Systems Engineering, University of Wisconsin–Madison, 1513 University Avenue, Madison, WI 53706; e-mail: bier@engr.wisc.edu . Samuel E. Bodily (" Darden's Luckiest Student: Lessons from a High-Stakes Risk Experiment ") is the John Tyler Professor of Business Administration at the University of Virginia's Darden School of Business and has published textbooks and more than 40 articles in journals ranging from Harvard Business Review to Management Science. His publications relate to decision and risk analysis, forecasting, strategy modeling, revenue management, and eStrategy. He has edited special issues of Interfaces on decision and risk analysis and strategy modeling and analysis. Professor Bodily has published well over 100 cases, including a couple of the 10 best-selling cases at Darden. He received the Distinguished Casewriter Wachovia Award from Darden in 2005 and three other best case or research Wachovia awards. He is faculty leader for an executive program on Strategic Thinking and Action. He is the course head of, and teaches in, a highly valued first-year MBA course in decision analysis, has a successful second-year elective on Management Decision Models, and has taught eStrategy and Strategy. He is a past winner of the Decision Sciences International Instructional Award and has served as chair of the INFORMS Decision Analysis Society. He has taught numerous executive education programs for Darden and private companies, has consulted widely for business and government entities, and has served as an expert witness. Professor Bodily was on the faculties of MIT Sloan School of Management and Boston University and has been a visiting professor at INSEAD Singapore, Stanford University, and the University of Washington. He has a Ph.D. degree and an S.M. degree from the Massachusetts Institute of Technology and a B.S. degree in physics from Brigham Young University. Address: Darden School of Business, 100 Darden Boulevard, Charlottesville, VA 22903; e-mail: bodilys@virginia.edu . David Budescu (" From the Editors… ") is the Anne Anastasi Professor of Psychometrics and Quantitative Psychology at Fordham University. He held positions at the University of Illinois and the University of Haifa, and visiting positions at Carnegie Mellon University, University of Gotheborg, the Kellog School at Northwestern University, the Hebrew University, and the Israel Institute of Technology (Technion). His research is in the areas of human judgment, individual and group decision making under uncertainty and with incomplete and vague information, and statistics for the behavioral and social sciences. He is or was on the editorial boards of Applied Psychological Measurement; Decision Analysis; Journal of Behavioral Decision Making; Journal of Mathematical Psychology; Journal of Experimental Psychology: Learning, Memory and Cognition (2000–2003); Multivariate Behavioral Research; Organizational Behavior and Human Decision Processes (1992–2002); and Psychological Methods (1996–2000). He is past president of the Society for Judgment and Decision Making (2000–2001), fellow of the Association for Psychological Science, and an elected member of the Society of Multivariate Experimental Psychologists. Address: Department of Psychology, Fordham University, Bronx, New York, NY 10458; e-mail: budescu@fordham.edu . John C. Butler (" From the Editors… ") is a clinical associate professor of finance and the academic director of the Energy Management and Innovation Center in the McCombs School of Business at the University of Texas at Austin, and the secretary/treasurer of the INFORMS Decision Analysis Society. Butler received his Ph.D. in management science and information systems from the University of Texas in 1998. His research interests involve the use of decision science models to support decision making, with a particular emphasis on decision and risk analysis models with multiple performance criteria. Butler has consulted with a number of organizations regarding the application of decision analysis tools to a variety of practical problems. Most of his consulting projects involve use of Visual Basic for Applications and Excel to implement complex decision science models in a user-friendly format. Address: Center for Energy Management and Innovation, McCombs School of Business, The University of Texas at Austin, Austin, TX 78712-1178; e-mail: john.butler2@mccombs.utexas.edu . Philippe Delquié (" From the Editors… ") is an associate professor of decision sciences at the George Washington University and holds a Ph.D. from the Massachusetts Institute of Technology. Professor Delquié's teaching and research are in decision, risk, and multicriteria analysis. His work focuses on the interplay of behavioral and normative theories of choice, with the aim of improving managerial decision making and risk taking. His research addresses issues in preference assessment, value of information, nonexpected utility models of choice under risk, and risk measures. Prior to joining the George Washington University, he held academic appointments at INSEAD, the University of Texas at Austin, and École Normale Supérieure, France, and visiting appointments at Duke University's Fuqua School of Business. Address: Department of Decision Sciences, George Washington University, Funger Hall, Suite 415, Washington, DC 20052; e-mail: delquie@gwu.edu . Zeynep Erkin (" Eliciting Patients' Revealed Preferences: An Inverse Markov Decision Process Approach ") is a Ph.D. candidate in the Department of Industrial Engineering at the University of Pittsburgh. She received her M.S. and B.S. degrees in industrial engineering from the University of Pittsburgh and Middle East Technical University, Turkey, in 2008 and 2006, respectively. Her research interests include maintenance optimization and medical decision making. Address: Department of Industrial Engineering, University of Pittsburgh, 3600 O'Hara Street, Pittsburgh, PA 15261; e-mail: zee2@pitt.edu . Peter I. Frazier (" Paradoxes in Learning and the Marginal Value of Information ") is an assistant professor in the School of Operations Research and Information Engineering at Cornell University. He received a Ph.D. in operations research and financial engineering from Princeton University in 2009. His research interest is in the optimal acquisition of information, with applications in simulation, medicine, operations management, neuroscience, and information retrieval. He teaches courses in simulation and statistics. Address: School of Operations Research and Information Engineering, Cornell University, Ithaca, NY 14853; e-mail: pf98@cornell.edu . L. Robin Keller (" From the Editors… ") is a professor of operations and decision technologies in the Merage School of Business at the University of California, Irvine. She received her Ph.D. and M.B.A. in management science and her B.A. in mathematics from the University of California, Los Angeles. She has served as a program director for the Decision, Risk, and Management Science Program of the U.S. National Science Foundation (NSF). Her research is on decision analysis and risk analysis for business and policy decisions and has been funded by NSF and the U.S. Environmental Protection Agency. Her research interests cover multiple-attribute decision making, riskiness, fairness, probability judgments, ambiguity of probabilities or outcomes, risk analysis (for terrorism, environmental, health, and safety risks), time preferences, problem structuring, cross-cultural decisions, and medical decision making. She is currently Editor-in-Chief of Decision Analysis, published by the Institute for Operations Research and the Management Sciences (INFORMS). She is a Fellow of INFORMS and has held numerous roles in INFORMS, including board member and chair of the INFORMS Decision Analysis Society. She is a recipient of the George F. Kimball Medal from INFORMS. She has served as the decision analyst on three National Academy of Sciences committees. Address: The Paul Merage School of Business, University of California, Irvine, Irvine, CA 92697-3125; e-mail: lrkeller@uci.edu . Lisa M. Maillart (" Eliciting Patients' Revealed Preferences: An Inverse Markov Decision Process Approach ") is an associate professor in the Industrial Engineering Department at the University of Pittsburgh. Prior to joining the faculty at the University of Pittsburgh, she served on the faculty of the Department of Operations in the Weatherhead School of Management at Case Western Reserve University. She received her M.S. and B.S. in industrial and systems engineering from Virginia Tech, and her Ph.D. in industrial and operations engineering from the University of Michigan. Her primary research interest is in sequential decision making under uncertainty, with applications in medical decision making and maintenance optimization. She is a member of the Institute for Operations Research and the Management Sciences (INFORMS), the Society of Medical Decision Making (SMDM), and the Institute of Industrial Engineers (IIE). Address: Department of Industrial Engineering, University of Pittsburgh, 3600 O'Hara Street, Pittsburgh, PA 15261; e-mail: maillart@pitt.edu . Jason R. W. Merrick (" From the Editors… ") is an associate professor in the Department of Statistical Sciences and Operations Research at Virginia Commonwealth University. He has a D.Sc. in operations research from the George Washington University. He teaches courses in decision analysis, risk analysis, and simulation. His research is primarily in the area of decision analysis and Bayesian statistics. He has worked on projects ranging from assessing maritime oil transportation and ferry system safety, the environmental health of watersheds, and optimal replacement policies for rail tracks and machine tools, and he has received grants from the National Science Foundation, the Federal Aviation Administration, the United States Coast Guard, the American Bureau of Shipping, British Petroleum, and Booz Allen Hamilton, among others. He has also performed training for Infineon Technologies, Wyeth Pharmaceuticals, and Capital One Services. He is an associate editor for Decision Analysis and Operations Research. He is the information officer for the Decision Analysis Society. Address: Department of Statistical Sciences and Operations Research, Virginia Commonwealth University, Richmond, VA 23284; e-mail: jrmerric@vcu.edu . Phillip E. Pfeifer (" Darden's Luckiest Student: Lessons from a High-Stakes Risk Experiment ") is the Richard S. Reynolds Professor of Business at the University of Virginia's Darden School of Business, where he teaches courses in decision analysis and direct marketing. A graduate of Lehigh University and the Georgia Institute of Technology, his teaching has won student awards and has been recognized in Business Week's Guide to the Best Business Schools. He is an active researcher in the areas of decision making and direct marketing, and he currently serves on the editorial review board of the Journal of Interactive Marketing, which named him their best reviewer of 2008. In 2004 he was recognized as the Darden School's faculty leader in terms of external case sales, and in 2006 he coauthored a managerial book, Marketing Metrics: 50+ Metrics Every Executive Should Master, published by Wharton School Publishing, which was named best marketing book of the year by Strategy + Business. Address: Darden School of Business; 100 Darden Boulevard; Charlottesville, VA 22903; e-mail: pfeiferp@virginia.edu . Warren B. Powell (" Paradoxes in Learning and the Marginal Value of Information ") is a professor in the Department of Operations Research and Financial Engineering at Princeton University, where he has taught since 1981. He is the director of CASTLE Laboratory (Princeton University), which specializes in the development of stochastic optimization models and algorithms with applications in transportation and logistics, energy, health, and finance. The author or coauthor of more than 160 refereed publications, he is an INFORMS Fellow, and the author of Approximate Dynamic Programming: Solving the Curses of Dimensionality, published by John Wiley and Sons. His primary research interests are in approximate dynamic programming for high-dimensional applications and optimal learning (the efficient collection of information), and their application in energy systems analysis and transportation. He is a recipient of the Wagner prize and has twice been a finalist in the Edelman competition. He has also served in a variety of editorial and administrative positions for INFORMS, including INFORMS Board of Directors, area editor for Operations Research, president of the Transportation Science Section, and numerous prize and administrative committees. Address: Department of Operations Research and Financial Engineering, Princeton University, Princeton, NJ 08544; e-mail: powell@princeton.edu . Mark S. Roberts (" Eliciting Patients' Revealed Preferences: An Inverse Markov Decision Process Approach "), M.D., M.P.P., is professor and chair of health policy and management, and he holds secondary appointments in medicine, industrial engineering, and clinical and translational science. A practicing general internist, he has conducted research in decision analysis and the mathematical modeling of disease for more than 25 years, and he has expertise in cost effectiveness analysis, mathematical optimization and simulation, and the measurement and inclusion of patient preferences into decision problems. He has used decision analysis to examine clinical, costs, policy and allocation questions in liver transplantation, vaccination strategies, operative interventions, and the use of many medications. His recent research has concentrated in the use of mathematical methods from operations research and management science, including Markov decision processes, discrete-event simulation, and integer programming, to problems in health care. Address: Department of Health Policy and Management, University of Pittsburgh, Graduate School of Public Health, 130 De Soto Street, Pittsburgh, PA 15261; e-mail: robertsm@upmc.edu . Ahti Salo (" From the Editors… ") is a professor of systems analysis at the Systems Analysis Laboratory of Aalto University. His research interests include topics in portfolio decision analysis, multicriteria decision making, risk management, efficiency analysis, and technology foresight. He is currently president of the Finnish Operations Research Society (FORS) and represents Europe and the Middle East in the INFORMS International Activities Committee. Professor Salo has been responsible for the methodological design and implementation of numerous high-impact decision and policy processes, including FinnSight 2015, the national foresight exercise of the Academy of Finland and the National Funding Agency for Technology and Innovations (Tekes). Address: Aalto University, Systems Analysis Laboratory, P.O. Box 11100, 00076 Aalto, Finland; e-mail: ahti.salo@tkk.fi . Andrew J. Schaefer (" Eliciting Patients' Revealed Preferences: An Inverse Markov Decision Process Approach ") is an associate professor of industrial engineering and Wellington C. Carl Fellow at the University of Pittsburgh. He has courtesy appointments in bioengineering, medicine, and clinical and translational science. He received his Ph.D. in industrial and systems engineering from Georgia Tech in 2000. His research interests include the application of stochastic optimization methods to health-care problems, as well as stochastic optimization techniques, in particular, stochastic integer programming. He is interested in patient-oriented decision making in contexts such as end-stage liver disease, HIV/AIDS, sepsis, and diabetes. He also models health-care systems, including operating rooms and intensive-care units. He is an associate editor for INFORMS Journal on Computing and IIE Transactions. Address: Department of Industrial Engineering, University of Pittsburgh, 3600 O'Hara Street, Pittsburgh, PA 15261; e-mail: Schaefer@pitt.edu . George Wu (" From the Editors… ") has been on the faculty of the University of Chicago Booth School of Business since September 1997. His degrees include A.B. (applied mathematics, 1985), S.M. (applied mathematics, 1987), and Ph.D. (decision sciences, 1991), all from Harvard University. Prior to joining the faculty at the University of Chicago, Professor Wu was on the faculty at Harvard Business School. Wu worked as a decision analyst at Procter & Gamble prior to starting graduate school. His research interests include descriptive and prescriptive aspects of decision making, in particular, decision making involving risk, cognitive biases in bargaining and negotiation, and managerial and organizational decision making. Professor Wu is a coordinating editor for Theory and Decision, an advisory editor for Journal of Risk and Uncertainty, on the editorial boards of Decision Analysis and Journal of Behavioral Decision Making, and a former department editor of Management Science. Address: Booth School of Business, University of Chicago, 5807 South Woodlawn Avenue, Chicago, IL 60637; e-mail: wu@chicagobooth.edu .