Decision Making: Uncertainty, Imperfection, Deliberation and Scalability

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Publisher : Springer
ISBN 13 : 3319151444
Total Pages : 184 pages
Book Rating : 4.41/5 ( download)

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Book Synopsis Decision Making: Uncertainty, Imperfection, Deliberation and Scalability by : Tatiana V. Guy

Download or read book Decision Making: Uncertainty, Imperfection, Deliberation and Scalability written by Tatiana V. Guy and published by Springer. This book was released on 2015-02-09 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume focuses on uncovering the fundamental forces underlying dynamic decision making among multiple interacting, imperfect and selfish decision makers. The chapters are written by leading experts from different disciplines, all considering the many sources of imperfection in decision making, and always with an eye to decreasing the myriad discrepancies between theory and real world human decision making. Topics addressed include uncertainty, deliberation cost and the complexity arising from the inherent large computational scale of decision making in these systems. In particular, analyses and experiments are presented which concern: • task allocation to maximize “the wisdom of the crowd”; • design of a society of “edutainment” robots who account for one anothers’ emotional states; • recognizing and counteracting seemingly non-rational human decision making; • coping with extreme scale when learning causality in networks; • efficiently incorporating expert knowledge in personalized medicine; • the effects of personality on risky decision making. The volume is a valuable source for researchers, graduate students and practitioners in machine learning, stochastic control, robotics, and economics, among other fields.

Computational Linguistics and Intelligent Text Processing

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Publisher : Springer
ISBN 13 : 3319771132
Total Pages : 608 pages
Book Rating : 4.37/5 ( download)

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Book Synopsis Computational Linguistics and Intelligent Text Processing by : Alexander Gelbukh

Download or read book Computational Linguistics and Intelligent Text Processing written by Alexander Gelbukh and published by Springer. This book was released on 2018-10-09 with total page 608 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two-volume set LNCS 10761 + 10762 constitutes revised selected papers from the CICLing 2017 conference which took place in Budapest, Hungary, in April 2017. The total of 90 papers presented in the two volumes was carefully reviewed and selected from numerous submissions. In addition, the proceedings contain 4 invited papers. The papers are organized in the following topical sections: Part I: general; morphology and text segmentation; syntax and parsing; word sense disambiguation; reference and coreference resolution; named entity recognition; semantics and text similarity; information extraction; speech recognition; applications to linguistics and the humanities. Part II: sentiment analysis; opinion mining; author profiling and authorship attribution; social network analysis; machine translation; text summarization; information retrieval and text classification; practical applications.

Statistics and Causality

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Publisher : John Wiley & Sons
ISBN 13 : 1118947045
Total Pages : 478 pages
Book Rating : 4.43/5 ( download)

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Book Synopsis Statistics and Causality by : Wolfgang Wiedermann

Download or read book Statistics and Causality written by Wolfgang Wiedermann and published by John Wiley & Sons. This book was released on 2016-06-07 with total page 478 pages. Available in PDF, EPUB and Kindle. Book excerpt: b”STATISTICS AND CAUSALITYA one-of-a-kind guide to identifying and dealing with modern statistical developments in causality Written by a group of well-known experts, Statistics and Causality: Methods for Applied Empirical Research focuses on the most up-to-date developments in statistical methods in respect to causality. Illustrating the properties of statistical methods to theories of causality, the book features a summary of the latest developments in methods for statistical analysis of causality hypotheses. The book is divided into five accessible and independent parts. The first part introduces the foundations of causal structures and discusses issues associated with standard mechanistic and difference-making theories of causality. The second part features novel generalizations of methods designed to make statements concerning the direction of effects. The third part illustrates advances in Granger-causality testing and related issues. The fourth part focuses on counterfactual approaches and propensity score analysis. Finally, the fifth part presents designs for causal inference with an overview of the research designs commonly used in epidemiology. Statistics and Causality: Methods for Applied Empirical Research also includes: New statistical methodologies and approaches to causal analysis in the context of the continuing development of philosophical theories End-of-chapter bibliographies that provide references for further discussions and additional research topics Discussions on the use and applicability of software when appropriate Statistics and Causality: Methods for Applied Empirical Research is an ideal reference for practicing statisticians, applied mathematicians, psychologists, sociologists, logicians, medical professionals, epidemiologists, and educators who want to learn more about new methodologies in causal analysis. The book is also an excellent textbook for graduate-level courses in causality and qualitative logic.

Novel Approaches in Microbiome Analyses and Data Visualization

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Publisher : Frontiers Media SA
ISBN 13 : 2889456536
Total Pages : 186 pages
Book Rating : 4.36/5 ( download)

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Book Synopsis Novel Approaches in Microbiome Analyses and Data Visualization by : Jessica Galloway-Peña

Download or read book Novel Approaches in Microbiome Analyses and Data Visualization written by Jessica Galloway-Peña and published by Frontiers Media SA. This book was released on 2019-02-06 with total page 186 pages. Available in PDF, EPUB and Kindle. Book excerpt: High-throughput sequencing technologies are widely used to study microbial ecology across species and habitats in order to understand the impacts of microbial communities on host health, metabolism, and the environment. Due to the dynamic nature of microbial communities, longitudinal microbiome analyses play an essential role in these types of investigations. Key questions in microbiome studies aim at identifying specific microbial taxa, enterotypes, genes, or metabolites associated with specific outcomes, as well as potential factors that influence microbial communities. However, the characteristics of microbiome data, such as sparsity and skewedness, combined with the nature of data collection, reflected often as uneven sampling or missing data, make commonly employed statistical approaches to handle repeated measures in longitudinal studies inadequate. Therefore, many researchers have begun to investigate methods that could improve incorporating these features when studying clinical, host, metabolic, or environmental associations with longitudinal microbiome data. In addition to the inferential aspect, it is also becoming apparent that visualization of high dimensional data in a way which is both intelligible and comprehensive is another difficult challenge that microbiome researchers face. Visualization is crucial in both the analysis and understanding of metagenomic data. Researchers must create clear graphic representations that give biological insight without being overly complicated. Thus, this Research Topic seeks to both review and provide novels approaches that are being developed to integrate microbiome data and complex metadata into meaningful mathematical, statistical and computational models. We believe this topic is fundamental to understanding the importance of microbial communities and provides a useful reference for other investigators approaching the field.

Bounded Rationality in Decision Making Under Uncertainty: Towards Optimal Granularity

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Publisher : Springer
ISBN 13 : 3319622145
Total Pages : 164 pages
Book Rating : 4.49/5 ( download)

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Book Synopsis Bounded Rationality in Decision Making Under Uncertainty: Towards Optimal Granularity by : Joe Lorkowski

Download or read book Bounded Rationality in Decision Making Under Uncertainty: Towards Optimal Granularity written by Joe Lorkowski and published by Springer. This book was released on 2017-07-01 with total page 164 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book addresses an intriguing question: are our decisions rational? It explains seemingly irrational human decision-making behavior by taking into account our limited ability to process information. It also shows with several examples that optimization under granularity restriction leads to observed human decision-making. Drawing on the Nobel-prize-winning studies by Kahneman and Tversky, researchers have found many examples of seemingly irrational decisions: e.g., we overestimate the probability of rare events. Our explanation is that since human abilities to process information are limited, we operate not with the exact values of relevant quantities, but with “granules” that contain these values. We show that optimization under such granularity indeed leads to observed human behavior. In particular, for the first time, we explain the mysterious empirical dependence of betting odds on actual probabilities. This book can be recommended to all students interested in human decision-making, to researchers whose work involves human decisions, and to practitioners who design and employ systems involving human decision-making —so that they can better utilize our ability to make decisions under uncertainty.

Decision Making Under Uncertainty

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Publisher : MIT Press
ISBN 13 : 0262331713
Total Pages : 350 pages
Book Rating : 4.15/5 ( download)

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Book Synopsis Decision Making Under Uncertainty by : Mykel J. Kochenderfer

Download or read book Decision Making Under Uncertainty written by Mykel J. Kochenderfer and published by MIT Press. This book was released on 2015-07-24 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to decision making under uncertainty from a computational perspective, covering both theory and applications ranging from speech recognition to airborne collision avoidance. Many important problems involve decision making under uncertainty—that is, choosing actions based on often imperfect observations, with unknown outcomes. Designers of automated decision support systems must take into account the various sources of uncertainty while balancing the multiple objectives of the system. This book provides an introduction to the challenges of decision making under uncertainty from a computational perspective. It presents both the theory behind decision making models and algorithms and a collection of example applications that range from speech recognition to aircraft collision avoidance. Focusing on two methods for designing decision agents, planning and reinforcement learning, the book covers probabilistic models, introducing Bayesian networks as a graphical model that captures probabilistic relationships between variables; utility theory as a framework for understanding optimal decision making under uncertainty; Markov decision processes as a method for modeling sequential problems; model uncertainty; state uncertainty; and cooperative decision making involving multiple interacting agents. A series of applications shows how the theoretical concepts can be applied to systems for attribute-based person search, speech applications, collision avoidance, and unmanned aircraft persistent surveillance. Decision Making Under Uncertainty unifies research from different communities using consistent notation, and is accessible to students and researchers across engineering disciplines who have some prior exposure to probability theory and calculus. It can be used as a text for advanced undergraduate and graduate students in fields including computer science, aerospace and electrical engineering, and management science. It will also be a valuable professional reference for researchers in a variety of disciplines.

Decision Making Under Uncertainty

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Publisher : Thomson South-Western
ISBN 13 :
Total Pages : 228 pages
Book Rating : 4.15/5 ( download)

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Book Synopsis Decision Making Under Uncertainty by : David E. Bell

Download or read book Decision Making Under Uncertainty written by David E. Bell and published by Thomson South-Western. This book was released on 1995 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: These authors draw on nearly 50 years of combined teaching and consulting experience to give readers a straightforward yet systematic approach for making estimates about the likelihood and consequences of future events -- and then using those assessments to arrive at sound decisions. The book's real-world cases, supplemented with expository text and spreadsheets, help readers master such techniques as decision trees and simulation, such concepts as probability, the value of information, and strategic gaming; and such applications as inventory stocking problems, bidding situations, and negotiating.

Planning on Uncertainty

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Publisher : John Wiley & Sons
ISBN 13 :
Total Pages : 264 pages
Book Rating : 4.83/5 ( download)

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Book Synopsis Planning on Uncertainty by : Ruth Prince Mack

Download or read book Planning on Uncertainty written by Ruth Prince Mack and published by John Wiley & Sons. This book was released on 1971 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Decision Making with Imperfect Decision Makers

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Publisher : Springer Science & Business Media
ISBN 13 : 3642246478
Total Pages : 207 pages
Book Rating : 4.70/5 ( download)

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Book Synopsis Decision Making with Imperfect Decision Makers by : Tatiana Valentine Guy

Download or read book Decision Making with Imperfect Decision Makers written by Tatiana Valentine Guy and published by Springer Science & Business Media. This book was released on 2011-11-13 with total page 207 pages. Available in PDF, EPUB and Kindle. Book excerpt: Prescriptive Bayesian decision making has reached a high level of maturity and is well-supported algorithmically. However, experimental data shows that real decision makers choose such Bayes-optimal decisions surprisingly infrequently, often making decisions that are badly sub-optimal. So prevalent is such imperfect decision-making that it should be accepted as an inherent feature of real decision makers living within interacting societies. To date such societies have been investigated from an economic and gametheoretic perspective, and even to a degree from a physics perspective. However, little research has been done from the perspective of computer science and associated disciplines like machine learning, information theory and neuroscience. This book is a major contribution to such research. Some of the particular topics addressed include: How should we formalise rational decision making of a single imperfect decision maker? Does the answer change for a system of imperfect decision makers? Can we extend existing prescriptive theories for perfect decision makers to make them useful for imperfect ones? How can we exploit the relation of these problems to the control under varying and uncertain resources constraints as well as to the problem of the computational decision making? What can we learn from natural, engineered, and social systems to help us address these issues?

Advances in Decision Making Under Risk and Uncertainty

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Publisher : Springer
ISBN 13 : 9783642088001
Total Pages : 0 pages
Book Rating : 4.07/5 ( download)

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Book Synopsis Advances in Decision Making Under Risk and Uncertainty by : Mohammed Abdellaoui

Download or read book Advances in Decision Making Under Risk and Uncertainty written by Mohammed Abdellaoui and published by Springer. This book was released on 2010-11-25 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Whether we like it or not we all feel that the world is uncertain. From choosing a new technology to selecting a job, we rarely know in advance what outcome will result from our decisions. Unfortunately, the standard theory of choice under uncertainty developed in the early forties and fifties turns out to be too rigid to take many tricky issues of choice under uncertainty into account. The good news is that we have now moved away from the early descriptively inadequate modeling of behavior. This book brings the reader into contact with the accomplished progress in individual decision making through the most recent contributions to uncertainty modeling and behavioral decision making. It also introduces the reader into the many subtle issues to be resolved for rational choice under uncertainty.