The Analytics of Uncertainty and Information PDF Download

Author: Jack Hirshleifer
Publisher: Cambridge University Press
ISBN: 9780521283694
Category : Business & Economics
Languages : en
Pages : 482

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Book Description
Economists have always recognised that human endeavours are constrained by our limited and uncertain knowledge, but only recently has an accepted theory of uncertainty and information evolved. This theory has turned out to have surprisingly practical applications: for example in analysing stock market returns, in evaluating accident prevention measures, and in assessing patent and copyright laws. This book presents these intellectual advances in readable form for the first time. It unifies many important but partial results into a satisfying single picture, making it clear how the economics of uncertainty and information generalises and extends standard economic analysis. Part One of the volume covers the economics of uncertainty: how each person adapts to a given fixed state of knowledge by making an optimal choice among the immediate 'terminal' actions available. These choices in turn determine the overall market equilibrium reflecting the social distribution of risk bearing. In Part Two, covering the economics of information, the state of knowledge is no longer held fixed. Instead, individuals can to a greater or lesser extent overcome their ignorance by 'informational' actions. The text also addresses at appropriate points many specific topics such as insurance, the Capital Asset Pricing model, auctions, deterrence of entry, and research and invention.

Author: Sushil Bikhchandani
Publisher: Cambridge University Press
ISBN: 1107433762
Category : Business & Economics
Languages : en
Pages : 536

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Book Description
There has been explosive progress in the economic theory of uncertainty and information in the past few decades. This subject is now taught not only in departments of economics but also in professional schools and programs oriented toward business, government and administration, and public policy. This book attempts to unify the subject matter in a simple, accessible manner. Part I of the book focuses on the economics of uncertainty; Part II examines the economics of information. This revised and updated second edition places a greater focus on game theory. New topics include posted-price markets, mechanism design, common-value auctions, and the one-shot deviation principle for repeated games.

Author: Jack Hirshleifer
Publisher:
ISBN:
Category :
Languages : en
Pages : 0

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Author: Jean-Jacques Laffont
Publisher: MIT Press
ISBN: 9780262121361
Category : Business & Economics
Languages : en
Pages : 312

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Book Description
The Economics of Uncertainty and Information may be used in conjunction with Loffont's Fundamentals of Economics in an advanced course in microeconomics.

Author: Luis Tenorio
Publisher: SIAM
ISBN: 1611974917
Category : Mathematics
Languages : en
Pages : 275

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Book Description
Inverse problems are found in many applications, such as medical imaging, engineering, astronomy, and geophysics, among others. To solve an inverse problem is to recover an object from noisy, usually indirect observations. Solutions to inverse problems are subject to many potential sources of error introduced by approximate mathematical models, regularization methods, numerical approximations for efficient computations, noisy data, and limitations in the number of observations; thus it is important to include an assessment of the uncertainties as part of the solution. Such assessment is interdisciplinary by nature, as it requires, in addition to knowledge of the particular application, methods from applied mathematics, probability, and statistics. This book bridges applied mathematics and statistics by providing a basic introduction to probability and statistics for uncertainty quantification in the context of inverse problems, as well as an introduction to statistical regularization of inverse problems. The author covers basic statistical inference, introduces the framework of ill-posed inverse problems, and explains statistical questions that arise in their applications. An Introduction to Data Analysis and Uncertainty Quantification for Inverse Problems?includes many examples that explain techniques which are useful to address general problems arising in uncertainty quantification, Bayesian and non-Bayesian statistical methods and discussions of their complementary roles, and analysis of a real data set to illustrate the methodology covered throughout the book.

Author: Tamara Afifi
Publisher: Routledge
ISBN: 1135890560
Category : Language Arts & Disciplines
Languages : en
Pages : 449

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Book Description
This volume integrates scholarly work on disclosure and uncertainty with the most up-to-date, cutting edge research, theories, and applications. Uncertainty is an ever-present part of human relationships, and the ways in which people reduce and/or manage uncertainty involves regulating their communication with others through revealing and concealing information. This collection is devoted to collating knowledge in these areas, advancing theory and presenting work that is socially meaningful. This work includes contributions from renowned scholars in interpersonal uncertainty and information regulation, focusing on processes that bridge boundaries within and across disciplines, while maintaining emphasis on interpersonal contexts. Disciplines represented here include interpersonal, family, and health communication, as well as relational and social psychology. Key features of the volume include: comprehensive coverage integrating the latest research on disclosure, information seeking, and uncertainty a highly theoretical content, socially meaningful in nature (applied to real-world contexts) an interdisciplinary approach that crosses sub-fields within communication. This volume is a unique and timely resource for advanced study in interpersonal, health, or family communication. With its emphasis on theory, the book is an excellent resource for graduate courses addressing theory and/or theory construction, and it will also appeal to scholars interested in applied research.

Author: Ivo D. Dinov
Publisher: Walter de Gruyter GmbH & Co KG
ISBN: 3110697823
Category : Computers
Languages : en
Pages : 489

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Book Description
The amount of new information is constantly increasing, faster than our ability to fully interpret and utilize it to improve human experiences. Addressing this asymmetry requires novel and revolutionary scientific methods and effective human and artificial intelligence interfaces. By lifting the concept of time from a positive real number to a 2D complex time (kime), this book uncovers a connection between artificial intelligence (AI), data science, and quantum mechanics. It proposes a new mathematical foundation for data science based on raising the 4D spacetime to a higher dimension where longitudinal data (e.g., time-series) are represented as manifolds (e.g., kime-surfaces). This new framework enables the development of innovative data science analytical methods for model-based and model-free scientific inference, derived computed phenotyping, and statistical forecasting. The book provides a transdisciplinary bridge and a pragmatic mechanism to translate quantum mechanical principles, such as particles and wavefunctions, into data science concepts, such as datum and inference-functions. It includes many open mathematical problems that still need to be solved, technological challenges that need to be tackled, and computational statistics algorithms that have to be fully developed and validated. Spacekime analytics provide mechanisms to effectively handle, process, and interpret large, heterogeneous, and continuously-tracked digital information from multiple sources. The authors propose computational methods, probability model-based techniques, and analytical strategies to estimate, approximate, or simulate the complex time phases (kime directions). This allows transforming time-varying data, such as time-series observations, into higher-dimensional manifolds representing complex-valued and kime-indexed surfaces (kime-surfaces). The book includes many illustrations of model-based and model-free spacekime analytic techniques applied to economic forecasting, identification of functional brain activation, and high-dimensional cohort phenotyping. Specific case-study examples include unsupervised clustering using the Michigan Consumer Sentiment Index (MCSI), model-based inference using functional magnetic resonance imaging (fMRI) data, and model-free inference using the UK Biobank data archive. The material includes mathematical, inferential, computational, and philosophical topics such as Heisenberg uncertainty principle and alternative approaches to large sample theory, where a few spacetime observations can be amplified by a series of derived, estimated, or simulated kime-phases. The authors extend Newton-Leibniz calculus of integration and differentiation to the spacekime manifold and discuss possible solutions to some of the "problems of time". The coverage also includes 5D spacekime formulations of classical 4D spacetime mathematical equations describing natural laws of physics, as well as, statistical articulation of spacekime analytics in a Bayesian inference framework. The steady increase of the volume and complexity of observed and recorded digital information drives the urgent need to develop novel data analytical strategies. Spacekime analytics represents one new data-analytic approach, which provides a mechanism to understand compound phenomena that are observed as multiplex longitudinal processes and computationally tracked by proxy measures. This book may be of interest to academic scholars, graduate students, postdoctoral fellows, artificial intelligence and machine learning engineers, biostatisticians, econometricians, and data analysts. Some of the material may also resonate with philosophers, futurists, astrophysicists, space industry technicians, biomedical researchers, health practitioners, and the general public.

Author: Ryan G. McClarren
Publisher: Springer
ISBN: 3319995251
Category : Science
Languages : en
Pages : 345

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Book Description
This textbook teaches the essential background and skills for understanding and quantifying uncertainties in a computational simulation, and for predicting the behavior of a system under those uncertainties. It addresses a critical knowledge gap in the widespread adoption of simulation in high-consequence decision-making throughout the engineering and physical sciences. Constructing sophisticated techniques for prediction from basic building blocks, the book first reviews the fundamentals that underpin later topics of the book including probability, sampling, and Bayesian statistics. Part II focuses on applying Local Sensitivity Analysis to apportion uncertainty in the model outputs to sources of uncertainty in its inputs. Part III demonstrates techniques for quantifying the impact of parametric uncertainties on a problem, specifically how input uncertainties affect outputs. The final section covers techniques for applying uncertainty quantification to make predictions under uncertainty, including treatment of epistemic uncertainties. It presents the theory and practice of predicting the behavior of a system based on the aggregation of data from simulation, theory, and experiment. The text focuses on simulations based on the solution of systems of partial differential equations and includes in-depth coverage of Monte Carlo methods, basic design of computer experiments, as well as regularized statistical techniques. Code references, in python, appear throughout the text and online as executable code, enabling readers to perform the analysis under discussion. Worked examples from realistic, model problems help readers understand the mechanics of applying the methods. Each chapter ends with several assignable problems. Uncertainty Quantification and Predictive Computational Science fills the growing need for a classroom text for senior undergraduate and early-career graduate students in the engineering and physical sciences and supports independent study by researchers and professionals who must include uncertainty quantification and predictive science in the simulations they develop and/or perform.

Author: Jesús Medina
Publisher: Springer
ISBN: 3319914731
Category : Computers
Languages : en
Pages : 835

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Book Description
This three volume set (CCIS 853-855) constitutes the proceedings of the 17th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2017, held in Cádiz, Spain, in June 2018. The 193 revised full papers were carefully reviewed and selected from 383 submissions. The papers are organized in topical sections on advances on explainable artificial intelligence; aggregation operators, fuzzy metrics and applications; belief function theory and its applications; current techniques to model, process and describe time series; discrete models and computational intelligence; formal concept analysis and uncertainty; fuzzy implication functions; fuzzy logic and artificial intelligence problems; fuzzy mathematical analysis and applications; fuzzy methods in data mining and knowledge discovery; fuzzy transforms: theory and applications to data analysis and image processing; imprecise probabilities: foundations and applications; mathematical fuzzy logic, mathematical morphology; measures of comparison and entropies for fuzzy sets and their extensions; new trends in data aggregation; pre-aggregation functions and generalized forms of monotonicity; rough and fuzzy similarity modelling tools; soft computing for decision making in uncertainty; soft computing in information retrieval and sentiment analysis; tri-partitions and uncertainty; decision making modeling and applications; logical methods in mining knowledge from big data; metaheuristics and machine learning; optimization models for modern analytics; uncertainty in medicine; uncertainty in Video/Image Processing (UVIP).

Author: Don Grady
Publisher: Routledge
ISBN: 1000721728
Category : Language Arts & Disciplines
Languages : en
Pages : 241

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Book Description
Audience and media analytics is more important now than ever, and this latest volume in the cutting-edge BEA Electronic Media Research Series collects some of the top scholars working with big data and analytics today. These chapters describe the development and help define media analytics as an academic discipline and professional practice. Understanding audiences is integral to creating and distributing media messages and the study of media analytics requires knowing a range of skills including research methods, the necessary tools available, familiarity with statistical procedures, and a mindset to provide insights and apply findings. This book summarizes the insights of analytics practitioners regarding the current state of legacy media analysis and social media analytics. Topics covered include the evolution of media technologies, the teaching of media measurement and analytics, the transition taking place in media research, and the use of media analytics to answer meaningful questions, drive content creation, and engage with audiences.