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Brings together current theoretical insights and new empirical results to examine expert adjustment of model forecasts from an econometric perspective.
Greater data availability has been coupled with developments in statistical theory and economic theory to allow more elaborate and complicated models to be entertained. These include factor models, DSGE models, restricted vector autoregressions, and non-linear models.
Social science experiments often cannot be analyzed under controlled conditions, as many take place outside a laboratory. None-the-less, measurement provides scientists with a sound basis for collecting and analyzing the results of field research. Science Outside the Laboratory examines the relationship between measurement theory and field investigations through the philosophy of science.
This book highlights research on the behavioral biases affecting judgmental accuracy in judgmental forecasting and showcases the state-of-the-art in judgment-based predictive analytics. In recent years, technological advancements have made it possible to use predictive analytics to exploit highly complex (big) data resources. Consequently, modern forecasting methodologies are based on sophisticated algorithms from the domain of machine learning and deep learning. However, research shows that in the majority of industry contexts, human judgment remains an indispensable component of the managerial forecasting process. This book discusses ways in which decision-makers can address human behavioral issues in judgmental forecasting. The book begins by introducing readers to the notion of human-machine interactions. This includes a look at the necessity of managerial judgment in situations where organizations commonly have algorithmic decision support models at their disposal. The remainder of the book is divided into three parts, with Part I focusing on the role of individual-level judgment in the design and utilization of algorithmic models. The respective chapters cover individual-level biases such as algorithm aversion, model selection criteria, model-judgment aggregation issues and implications for behavioral change. In turn, Part II addresses the role of collective judgments in predictive analytics. The chapters focus on issues related to talent spotting, performance-weighted aggregation, and the wisdom of timely crowds. Part III concludes the book by shedding light on the importance of contextual factors as critical determinants of forecasting performance. Its chapters discuss the usefulness of scenario analysis, the role of external factors in time series forecasting and introduce the idea of mindful organizing as an approach to creating more sustainable forecasting practices in organizations.
Account for uncertainties and optimize decision-making with this thorough exposition Decision theory is a body of thought and research seeking to apply a mathematical-logical framework to assessing probability and optimizing decision-making. It has developed robust tools for addressing all major challenges to decision making. Yet the number of variables and uncertainties affecting each decision outcome, many of them beyond the decider's control, mean that decision-making is far from a “solved problem”. The tools created by decision theory remain to be refined and applied to decisions in which uncertainties are prominent. Probabilistic Forecasts and Optimal Decisions introduces a theoretically-grounded methodology for optimizing decision-making under conditions of uncertainty. Beginning with an overview of the basic elements of probability theory and methods for modeling continuous variates, it proceeds to survey the mathematics of both continuous and discrete models, supporting each with key examples. The result is a crucial window into the complex but enormously rewarding world of decision theory. Probabilistic Forecasts and Optimal Decisions readers will also find: Extended case studies supported with real-world data Mini-projects running through multiple chapters to illustrate different stages of the decision-making process End of chapter exercises designed to facilitate student learning Probabilistic Forecasts and Optimal Decisions is ideal for advanced undergraduate and graduate students in the sciences and engineering, as well as predictive analytics and decision analytics professionals.
Econometricians make choices on data, models, and estimation routines. Using various examples, this book shows the consequences of choices.
Since its original publication, Expert Political Judgment by New York Times bestselling author Philip Tetlock has established itself as a contemporary classic in the literature on evaluating expert opinion. Tetlock first discusses arguments about whether the world is too complex for people to find the tools to understand political phenomena, let alone predict the future. He evaluates predictions from experts in different fields, comparing them to predictions by well-informed laity or those based on simple extrapolation from current trends. He goes on to analyze which styles of thinking are more successful in forecasting. Classifying thinking styles using Isaiah Berlin's prototypes of the fox and the hedgehog, Tetlock contends that the fox--the thinker who knows many little things, draws from an eclectic array of traditions, and is better able to improvise in response to changing events--is more successful in predicting the future than the hedgehog, who knows one big thing, toils devotedly within one tradition, and imposes formulaic solutions on ill-defined problems. He notes a perversely inverse relationship between the best scientific indicators of good judgement and the qualities that the media most prizes in pundits--the single-minded determination required to prevail in ideological combat. Clearly written and impeccably researched, the book fills a huge void in the literature on evaluating expert opinion. It will appeal across many academic disciplines as well as to corporations seeking to develop standards for judging expert decision-making. Now with a new preface in which Tetlock discusses the latest research in the field, the book explores what constitutes good judgment in predicting future events and looks at why experts are often wrong in their forecasts.
The Spanish Conference of Industrial Engineering /Ingeniería de Organización Industrial (CIO) is an annual meeting promoted by Asociación para el Desarrollo de la Ingeniería de Organización/ Industrial Engineers Association (ADINGOR). The aim of CIO is to establish a forum for the open and free exchange of ideas, opinions and academic experiences about research, technology transfer or successful business experiences in the field of Industrial Engineering. The Scientific Committee is composed by 68 international referees and we foresee the attendance of some 200 people from more than 15 countries and following the rotation of venue and organization between various Spanish universities, the 2011 Conference will be the fifteenth National Conference and the fifth International Conference in Cartagena. During three days the 2011 Conference will include the participation of European and other foreign countries researchers and practitioners that will presenting communications, reproduced in this volume, on a range of topics including: Production and Operations Business Management Supply Chain Management Economic environment Technological and Organizational Innovation and Management and Innovation in Education The Conference on Industrial Engineering (CIO) and its proceedings are an excellent platform for the dissemination of the outputs of the scientific projects developed in the frame of the European, national or regional Research and Development plans.
This book brings together the research of philosophers, sociologists, and social scientists. It examines those areas of scientific practice where reliance on the subjective judgment of experts and practitioners is the main source of useful knowledge to address and possibly, bring solutions to social problems. A common phenomenon in applications of science is that objective evidence does not point to a single answer or solution, to a problem. Reliance on subjective judgment, then, becomes necessary, despite the known fact that hunches, even those of putative experts, often provide information that is not very accurate, and that experts are prone to fallacies and biases. The book looks at how experts reach consensus in the social sciences, and which experts are relevant to which problems. It aims to answer many questions, the main one being: Can we start building a normative theory of expertise on the basis of the evidence that social scientists, sociologists and philosophers have uncovered?