Uncertain Values

Uncertain Values

Author: Stefan Riedener

Publisher: Walter de Gruyter GmbH & Co KG

Published: 2021-10-25

Total Pages: 157

ISBN-13: 3110736225

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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.


Fuzzy Database Modeling of Imprecise and Uncertain Engineering Information

Fuzzy Database Modeling of Imprecise and Uncertain Engineering Information

Author: Zongmin Ma

Publisher: Springer

Published: 2008-09-12

Total Pages: 221

ISBN-13: 3540330135

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Computer-based information technologies have been extensively used to help industries manage their processes and information systems hereby - come their nervous center. More specially, databases are designed to s- port the data storage, processing, and retrieval activities related to data management in information systems. Database management systems p- vide efficient task support and database systems are the key to impleme- ing industrial data management. Industrial data management requires da- base technique support. Industrial applications, however, are typically data and knowledge intensive applications and have some unique character- tics that makes their management difficult. Besides, some new techniques such as Web, artificial intelligence, and etc. have been introduced into - dustrial applications. These unique characteristics and usage of new te- nologies have put many potential requirements on industrial data mana- ment, which challenge today’s database systems and promote their evolvement. Viewed from database technology, information modeling in databases can be identified at two levels: (conceptual) data modeling and (logical) database modeling. This results in conceptual (semantic) data model and logical database model. Generally a conceptual data model is designed and then the designed conceptual data model will be transformed into a chosen logical database schema. Database systems based on logical database model are used to build information systems for data mana- ment. Much attention has been directed at conceptual data modeling of - dustrial information systems. Product data models, for example, can be views as a class of semantic data models (i. e.


Optimization in Industry

Optimization in Industry

Author: Shubhabrata Datta

Publisher: Springer

Published: 2018-11-03

Total Pages: 355

ISBN-13: 3030016412

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This book describes different approaches for solving industrial problems like product design, process optimization, quality enhancement, productivity improvement and cost minimization. Several optimization techniques are described. The book covers case studies on the applications of classical as well as evolutionary and swarm optimization tools for solving industrial issues. The content is very helpful for industry personnel, particularly engineers from the Operation, R&D and Quality Assurance sectors, and also the academic researchers of different engineering and/or business administration background.


Fuzzy and Uncertain Object-oriented Databases

Fuzzy and Uncertain Object-oriented Databases

Author: Rita de Caluwe

Publisher: World Scientific

Published: 1997

Total Pages: 226

ISBN-13: 9789810228934

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Enriching database models to allow the user to deal with fuzzy and uncertain information has been of scientists' concern for years. This book presents the latest research results in dealing with fuzziness and uncertainty in object-oriented databases. The readership will be researchers and engineers interested in databases and software engineering programming.


The Uncertainty Analysis of Model Results

The Uncertainty Analysis of Model Results

Author: Eduard Hofer

Publisher: Springer

Published: 2018-05-02

Total Pages: 355

ISBN-13: 3319762974

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This book is a practical guide to the uncertainty analysis of computer model applications. Used in many areas, such as engineering, ecology and economics, computer models are subject to various uncertainties at the level of model formulations, parameter values and input data. Naturally, it would be advantageous to know the combined effect of these uncertainties on the model results as well as whether the state of knowledge should be improved in order to reduce the uncertainty of the results most effectively. The book supports decision-makers, model developers and users in their argumentation for an uncertainty analysis and assists them in the interpretation of the analysis results.


Uncertain Information Processing In Expert Systems

Uncertain Information Processing In Expert Systems

Author: Petr Hajek

Publisher: CRC Press

Published: 1992-06-29

Total Pages: 310

ISBN-13: 9780849363689

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Uncertain Information Processing in Expert Systems systematically and critically examines probabilistic and rule-based (compositional, MYCIN-like) systems, the two most important families of expert systems dealing with uncertainty. The book features a detailed introduction to probabilistic systems (including methods using graphical models and methods of knowledge integration), an analysis of compositional systems based on algebraic considerations, an application of graphical models, and the Dempster-Shafer theory of evidence and its use in expert systems. The book will be useful to anyone working in artificial intelligence, statistical computing, symbolic logic, and expert systems.


Risk Analysis of Complex and Uncertain Systems

Risk Analysis of Complex and Uncertain Systems

Author: Louis Anthony Cox Jr.

Publisher: Springer Science & Business Media

Published: 2009-06-12

Total Pages: 457

ISBN-13: 0387890149

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In Risk Analysis of Complex and Uncertain Systems acknowledged risk authority Tony Cox shows all risk practitioners how Quantitative Risk Assessment (QRA) can be used to improve risk management decisions and policies. It develops and illustrates QRA methods for complex and uncertain biological, engineering, and social systems – systems that have behaviors that are just too complex to be modeled accurately in detail with high confidence – and shows how they can be applied to applications including assessing and managing risks from chemical carcinogens, antibiotic resistance, mad cow disease, terrorist attacks, and accidental or deliberate failures in telecommunications network infrastructure. This book was written for a broad range of practitioners, including decision risk analysts, operations researchers and management scientists, quantitative policy analysts, economists, health and safety risk assessors, engineers, and modelers.


Quantitative Evaluation of Systems

Quantitative Evaluation of Systems

Author: David Parker

Publisher: Springer Nature

Published: 2019-09-04

Total Pages: 361

ISBN-13: 3030302814

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This book constitutes the proceedings of the 16th International Conference on Quantitative Evaluation Systems, QEST 2019, held in Glasgow, UK, in September 2019. The 17 full papers presented together with 2 short papers were carefully reviewed and selected from 40 submissions. The papers cover topics in the field of Probabilistic Verification; Learning and Verification; Hybrid Systems; Security; Probabilistic Modelling and Abstraction; and Applications and Tools.


Scalable Uncertainty Management

Scalable Uncertainty Management

Author: Sergio Greco

Publisher: Springer

Published: 2008-10-01

Total Pages: 411

ISBN-13: 3540879935

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This book constitutes the refereed proceedings of the Second International Conference on Scalable Uncertainty Management, SUM 2008, held in Naples, Italy, in Oktober 2008. The 27 revised full papers presented together with the extended abstracts of 3 invited talks/tutorials were carefully reviewed and selected from 42 submissions. The papers address artificial intelligence researchers, database researchers, and practitioners to demonstrate theoretical techniques required to manage the uncertainty that arises in large scale real world applications and to cope with large volumes of uncertainty and inconsistency in databases, the Web, the semantic Web, and artificial intelligence in general.


Symbolic and Quantitative Approaches to Reasoning with Uncertainty

Symbolic and Quantitative Approaches to Reasoning with Uncertainty

Author: Khaled Mellouli

Publisher: Springer Science & Business Media

Published: 2007-09-21

Total Pages: 926

ISBN-13: 3540752552

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This book constitutes the refereed proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, ECSQARU 2007, held in Hammammet, Tunisia, Oktober 31 - November 2, 2007. The 78 revised full papers presented together with 3 invited papers were carefully reviewed and selected from over hundret submissions for inclusion in the book. The papers are organized in topical sections on Bayesian networks, graphical models, learning causal networks, planning, causality and independence, preference modelling and decision, argumentation systems, inconsistency handling, belief revision and merging, belief functions, fuzzy models, many-valued logical systems, uncertainty logics, probabilistic reasoning, reasoning models under uncertainty, uncertainty measures, probabilistic classifiers, classification and clustering, and industrial applications.