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1 edition of Mathematical Models for Decision Support found in the catalog.

Mathematical Models for Decision Support

by Gautam Mitra

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Published by Springer Berlin Heidelberg in Berlin, Heidelberg .
Written in English


About the Edition

This volume is based on the lectures presented by well-known experts at the NATO Advanced Study Institute on Mathematical Models for Decision Support held in Val d"Isère, France, July 26 - August 6, 1987. At the beginning the abstracts appear in a section of their own. This is followed by an extensive review paper which provides an overview of the major modeling issues in decision making. Thus, by first reading these two sections the readers may gain a fair glimpse of the contents of the remaining six sections of the volume. Researchers and practitioners interested in current Decision Support Systems issues and the directions in progress are the intended audience of this book. There is a particular emphasis on the adaptation of Artificial Intelligence techniques for use in decision making. Readers of this volume will find its structure and contents valuable and germane to their own field of study and research.

Edition Notes

Other titlesProceedings of the NATO Advanced Study Institute on Mathematical Models for Decision Support held in Val d"Isere France, July 26 - August 6, 1987
Statementedited by Gautam Mitra, Harvey J. Greenberg, Freerk A. Lootsma, Marcel J. Rijkaert, Hans J. Zimmermann
SeriesNATO ASI Series, Series F: Computer and Systems Sciences -- 48, NATO ASI series -- 48.
ContributionsGreenberg, Harvey J., Lootsma, Freerk A., Rijkaert, Marcel J., Zimmermann, Hans J.
The Physical Object
Format[electronic resource] /
Pagination1 online resource (IX, 765 pages 150 illustrations).
Number of Pages765
ID Numbers
Open LibraryOL27073268M
ISBN 103642835554
ISBN 109783642835551
OCLC/WorldCa840301355

4. title and subtitle developing integrated decision support systems from mathematical models 6. author(s) downs, michael s. 5. funding numbers 7. performing organization name(s) and addressees) naval postgraduate school monterey ca performing organization report number 9. sponsoring/monitoring agency name(s) and address(es)   Bringing together the disciplines of agriculture, animal science, plant science and ecology, this book explores how mathematics can be used to understand and explain agricultural processes. It starts by providing a review of the mathematical models currently available to agriculturalists, and the philosophy behind, and objectives of, modeling. The book then applies these techniques to real.

Mathematical Programming: An Overview 1 Management science is characterized by a scientific approach to managerial decision making. It attempts to apply mathematical methods and the capabilities of modern computers to the difficult and unstructured problems confronting modern managers. It is a young and novel discipline. Although its roots can beFile Size: KB. Mathematical Modelling on Decision-making: Probabilistic Model Key Stage: 4 Decision-making processes occur in all aspects of life and natural sciences. For example, the migration patterns of some species of animals could be analysed as the mathematical models so as to refine the models to better fit the real-life scenarios 3. File Size: KB.

The new edition of Mathematical Modeling, the survey text of choice for mathematical modeling courses, adds ample instructor support and online delivery for solutions manuals and software ancillaries.. From genetic engineering to hurricane prediction, mathematical models guide much of the decision making in our society. If the assumptions and methods underlying the modeling are flawed, the. Shareable Link. Use the link below to share a full-text version of this article with your friends and colleagues. Learn more.


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Mathematical Models for Decision Support by Gautam Mitra Download PDF EPUB FB2

Use of Optimisation Models as Decision Support Tools. Role of Information Systems in Decision Making: Database and Model Management Issues. Methods of Artificial Intelligence in Decision Making: Intelligent Knowledge Based Systems.

Representation of Uncertainty in Mathematical Models and Knowledge Based Systems. Mathematical Basis for Constructing Models and Model Validation. Keywords artificial intelligence control decision problem decision support expert system intelligence knowledge base knowledge-based systems modeling natural language operations research optimization uncertainty.

Mathematical models for decision support. Applied computing. Operations research. Decision analysis. Computing methodologies. Artificial intelligence.

Control methods. Knowledge representation and reasoning. Search methodologies. Modeling and simulation. Model development and analysis. Because the USGS has been historically active in the development of mathematical models for ground water flow and transport, and because similar activity for surface water quality models has been (at least in recent years) limited to the integration of existing models in decision support systems, the following discussion of predictive models.

Hsu-Shih Shih, An interactive approach for integrated multilevel systems in a fuzzy environment, Mathematical and Computer Modelling: An International Journal, v n, p, September, Patrick Valente, Gautam Mitra, The evolution of web-based optimisation: From ASP to e-Services, Decision Support Systems, v n.4, p Cited by: 6.

NATO Advanced Study Institute on Mathematical Models for Decision Support ( Val d'Isère, France). Mathematical models for decision support. Berlin ; New York: Springer-Verlag, © (OCoLC) Material Type: Conference publication, Internet resource: Document Type: Book, Internet Resource: All Authors / Contributors.

For a book solely focused on building polyhedral models, I'd recommend the reprinted "Shapes, Space, and Symmetry" by Alan Holden; I've had it for years and have used it to build several beautiful models.

But Cundy's book offers the possibility of enjoying a broader range of mathematical ideas in the same concrete way as building polyhedra/5(6).

Get this from a library. Mathematical Models for Decision Support. [Gautam Mitra; Harvey J Greenberg; Freerk A Lootsma; Marcel J Rijkaert; Hans J Zimmermann] -- This volume is based on the lectures presented by well-known experts at the NATO Advanced Study Institute on Mathematical Models for Decision Support held in Val d'Isère, France, July 26 - August 6.

The book explores a range of approaches including optimization models, dynamic models and probability models. Show less The new edition of Mathematical Modeling, the survey text of choice for mathematical modeling courses, adds ample instructor support and online delivery for.

Mathematical Models Generators of Decision Support Systems for Help in Case of Catastrophes: An Experience from Venezuela: /ch Immediately after the catastrophes that affected Venezuela at the end ofespecially the flood of the State of Vargas, a group of investigators of aCited by: 5.

Mathematical models and simulators provide an understanding of the fundamentals of the relevant processes causing formation damage and reducing the flow efficiency in petroleum reservoirs during the various phases of oil and gas production.

Modeling and simulation of formation damage are important tools for model-assisted analysis and. decision support facilitation and, in general, the complete decision support process. By its very nature, models and modelling play an essential role in OR/MS, as do the methodological issues underlying the very concept of Size: KB.

Decision support systems (DSSs) are a sub-set of information systems that support human decision-making through computerized systems that provide contextual information.

DSSs allow decision-makers to improve their strategic planning and management control; from a business standpoint these systems have a wide-range of application (Yong & Taib Cited by: 1. Open Library is an initiative of the Internet Archive, a (c)(3) non-profit, building a digital library of Internet sites and other cultural artifacts in digital projects include the Wayback Machine, and Mathematical models are used to forecast new product sales.

Forecasting using mathematical methods have been criticized by many researchers, as the forecasts are often not sufficiently accurate to Author: Carlo Vercellis. Combines detailed coverage with a practical guide to the mathematical models and analysis methodologies of business intelligence.

Covers all the hot topics such as data warehousing, data mining and its applications, machine learning, classification, supply optimization models, decision support systems, and analytical methods for performance.

A mathematical model is a description of a system using mathematical concepts and process of developing a mathematical model is termed mathematical atical models are used in the natural sciences (such as physics, biology, earth science, chemistry) and engineering disciplines (such as computer science, electrical engineering), as well as in the social sciences (such.

The complexity of issues requiring rational decision making grows and thus such decisions are becoming more and more difficult, despite advances in methodology and tools for decision support and in other areas of research. Globalization, interlinks between environmental, industrial, social and political issues, and rapid speed of change all contribute to the increase of this complexity.

(source: Nielsen Book Data) Choice theory forms the basis for many scientific fields, such as decision making, mathematical models of micro-economics, voting theory, theory of control in socio-economic systems, and some branches of psychology. This volume is a first monograph summing up major scientific results obtained during two decades.

The new edition of Mathematical Modeling, the survey text of choice for mathematical modeling courses, adds ample instructor support and online delivery for solutions manuals and software ancillaries.

From genetic engineering to hurricane prediction, mathematical models guide much of the decision making in our society. If the assumptions and methods underlying the modeling are flawed. One division between models is based on the type of outcome they predict. Deterministic models ignore random variation, and so always predict the same outcome from a given starting point.

On the other hand, the model may be more statistical in nature and so may predict the distribution of possible outcomes. Such models are said to be stochastic. 1File Size: 1MB.Mathematical Models of Life Support Systems; Mathematical Models; Research personnel and Policy analysts, managers, and decision makers and NGOs.

He has co-authored (with K. Vrieze) a research level text book Competitive Markov Decision Processes (Springer, ). He also authored or co-authored approximately 70 refereed research papers.

The book stresses those fundamental concepts that the authors believe are most important for the practical analysis of management decisions. It focuses on modeling and evaluating uncertainty explicitly, understanding the dynamic nature of decision making, using historical data and limited information effectively, simulating complex systems, and Pages: