Outcome Uncertain

This casebook presents both classic and current cases in bioethics, as well as the biomedical and social framework needed to understand the moral and social issues they raise. The text draws its cases from the author's market leading text, INTERVENTION AND REFLECTION, 6th Edition, and provides up-to-date introductions and a strong theoretical foundation for the critical study of bioethics.

Decision Theory has considerably developed in the late 1970's and the 1980's. The evolution has been so fast and far-r2aching that it has become increasingly difficult to keep track of the new state of the art. After a decade of new contributions, there was a need for an overview' of the field. This book is intended to fill the gap. The reader will find here thirty~nine selected papers which were given at FUR-III, the third international confe rence on the Foundations and applications of Utility, Risk and decision theories, held in Aix-en-Provence in June 1986. An introductory chapter will provide an overview of the main questions raised on the subject since the 17th Century and more particularly so in the last thirty years, as well as some elementary information on the experimental and theoretical results obtained. It is thus hoped that any reader with some basic background in either Economics, Hanagement or Operations Research will be able to read profitably the thirty-nine other chapters. Psychologists, Sociologists, Social Philosophers and other specialists of the social sciences will also read this book with interest, as will high-level practitioners of decision~making and advanced students in one of the abovementioned fields. An expository survey of this volume will be found at the end of the introductory chapter, so that any of the seven parts of the book can be put by the reader in due perspective.

How ought you to evaluate your options if you're uncertain about what's fundamentally valuable? A prominent response is Expected Value Maximisation (EVM)—the view that under axiological uncertainty, an option is better than another if and only if it has the greater expected value across axiologies. But the expected value of an option depends on quantitative probability and value facts, and in particular on value comparisons across axiologies. We need to explain what it is for such facts to hold. Also, EVM is by no means self-evident. We need an argument to defend that it’s true. This book introduces an axiomatic approach to answer these worries. It provides an explication of what EVM means by use of representation theorems: intertheoretic comparisons can be understood in terms of facts about which options are better than which, and mutatis mutandis for intratheoretic comparisons and axiological probabilities. And it provides a systematic argument to the effect that EVM is true: the theory can be vindicated through simple axioms. The result is a formally cogent and philosophically compelling extension of standard decision theory, and original take on the problem of axiological or normative uncertainty.

S2Presented here is a method of analysis that makes use of multiple estimates of project profitability stemming from different assumptions about the true values of uncertain factors like yields and prices. The purpose of the analysis is to screen competing projects to eliminate those that can be proved to be unprofitable, or surely less profitable than some other, regardless of the assumptions made about the values of uncertain factors.S3.

This Handbook explores the cognitive, motivational, interpersonal, clinical, and applied aspects of personal uncertainty. It showcases both the diversity and the unity that defines contemporary perspectives on uncertainty in self within social and personality psychology. The contributions to the volume are all written by distinguished scholars in personality, social psychology, and clinical psychology united by their common focus on the causes and consequences of self-uncertainty. Chapters explore the similarities and differences between personal uncertainty and other psychological experiences in terms of their nature and relationship with human thought, emotion, motivation, and behavior. Specific challenges posed by personal uncertainty and the coping strategies people develop in their daily life are identified. There is an assessment of the potential negative and positive repercussions of coping with the specific experience of self-uncertainty, including academic, health, and relationship outcomes. Throughout, strategies specifically designed to assist others in confronting the unique challenges posed by self-uncertainty in ways that emphasize healthy psychological functioning and growth are promoted. In addition, the contributions to the Handbook touch on the psychological, social, and cultural context of the new millennium, including concepts such as Friedman’s "flat world," confidence, the absence of doubt in world leaders, the threat of terrorism since 9/11, the arts, doubt and religious belief, and views of doubt as the universal condition of humankind. The Handbook is an invaluable resource for researchers, practitioners, and senior undergraduate and graduate students in social and personality psychology, clinical and counseling psychology, educational psychology, and developmental psychology.

A hands-on guide to the use of quantitative methods and software for making successful business decisions The appropriate use of quantitative methods lies at the core of successful decisions made by managers, researchers, and students in the field of business. Providing a framework for the development of sound judgment and the ability to utilize quantitative and qualitative approaches, Data Driven Business Decisions introduces readers to the important role that data plays in understanding business outcomes, addressing four general areas that managers need to know about: data handling and Microsoft Excel®, uncertainty, the relationship between inputs and outputs, and complex decisions with trade-offs and uncertainty. Grounded in the author's own classroom approach to business statistics, the book reveals how to use data to understand the drivers of business outcomes, which in turn allows for data-driven business decisions. A basic, non-mathematical foundation in statistics is provided, outlining for readers the tools needed to link data with business decisions; account for uncertainty in the actions of others and in patterns revealed by data; handle data in Excel®; translate their analysis into simple business terms; and present results in simple tables and charts. The author discusses key data analytic frameworks, such as decision trees and multiple regression, and also explores additional topics, including: Use of the Excel® functions Solver and Goal Seek Partial correlation and auto-correlation Interactions and proportional variation in regression models Seasonal adjustment and what it reveals Basic portfolio theory as an introduction to correlations Chapters are introduced with case studies that integrate simple ideas into the larger business context, and are followed by further details, raw data, and motivating insights. Algebraic notation is used only when necessary, and throughout the book, the author utilizes real-world examples from diverse areas such as market surveys, finance, economics, and business ethics. Excel® add-ins StatproGo and TreePlan are showcased to demonstrate execution of the techniques, and a related website features extensive programming instructions as well as insights, data sets, and solutions to problems included in the material. Data Driven Business Decisions is an excellent book for MBA quantitative analysis courses or undergraduate general statistics courses. It also serves as a valuable reference for practicing MBAs and practitioners in the fields of statistics, business, and finance.

This text examines new research at the interface of operations research, behavioral and cognitive sciences, and decision analysis. From the cognitive behaviorist who collects empirical evidence as to how people make decisions to the engineer and economist who are the consumers of such understanding, the reader encounters the familiar Traveling Salesman Problem and Prisoner's dilemma, how agricultural decisions are made in Argentina's Pampas region, and some social goals that come into play as an element of rational decision-making. In these 14 self-contained chapters, broad topics covered include the integration of decision analysis and behavioral models, innovations in behavioral models, exploring descriptive behavior models, and experimental studies.

Traditionally, cognition and emotion are seen as separate domains that are independent at best and in competition at worst. The French scientist and philosopher Blaise Pascal (1623-1662) famously said “Le coeur a ses raisons que la raison ne connaît point” (The heart has its reasons that reason does not know). Over the last century, however, psychologists and neuroscientists have increasingly appreciated their very strong reciprocal connections and interactions. Initially this was demonstrated in cognitive functions such as attention, learning and memory, and decision making. For instance, an emotional stimulus captures attention (e.g., Anderson & Phelps, 2001). Likewise, emotional stimuli are better learned and remembered than neutral ones (e.g., McGaugh, 1990) and they can provide strong incentives to bias decision making (Bechara et al., 1997). In more recent years, cognitive control has also been found to be intimately intertwined with emotion. This is consistent with an approach that considers cognitive control as an adaptive learning process (Braver & Cohen, 1999), reinforcement learning in particular (Holroyd & Coles, 2002; Verguts & Notebaert, 2009). From this perspective, cognitive control is not a cool encapsulated executive function, but instead involves rapidly calculating the value of situational, contextual, and action cues (Rushworth & Behrens, 2008) for the purpose of adapting the cognitive system toward future optimal performance. A wide array of research has shed light on cognitive control and its interactions with affect or motivation. Behaviorally, important phenomena include how people respond to difficult stimuli (e.g., incongruent stimuli, task switches), negative feedback, or errors and how this influences subsequent task processing. Neurally, an important target structure has been the anterior cingulate cortex (ACC) and its connections to traditional “emotional” (e.g., amygdala) and “cognitive” areas (e.g., (pre)motor cortex, dorsolateral prefrontal cortex). ACC seems to play a predominant role in integrating distant effects from remote cognitive and emotion systems in order to guide and optimize behavior. The current special issue focuses on the bi-directional link between emotion and cognitive control. We invite studies that investigate the influence from emotion on cognitive control, or vice versa, the influence of cognitive control on emotion. Contributions can be of different types: We welcome empirical contributions (behavioral or neuroscientific) but also computational modeling, theory, or review papers. By bringing together researchers from the traditionally separated domains, we hope to further stimulate the crosstalk between emotion and cognitive control, and thus to deepen our understanding of both.