[PDF] Partial Information And Sensitivity Analysis In Multi Objective Decision Making eBook

Author : David Rios Insua
Publisher : Springer Science & Business Media
Page : 204 pages
File Size : 39,77 MB
Release : 2013-06-29
Category : Business & Economics
ISBN : 3642516564

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The axiomatic foundations of the Bayesian approach to decision making assurne precision in the decision maker's judgements. In practicc, dccision makers often provide only partial and/or doubtful information. We unify and expand results to deal with those cases introducing a general framework for sensitivity analysis in multi-objective decision making. We study first decision making problems under partial information. We provide axioms leading to modelling preferences by families of value functions, in problems under certainty, and moJelling beliefs by families of probability distributions and preferences by familics of utility functions, in problems under uncertainty. Both problems are treated in parallel with the same parametric model. Alternatives are ordered in a Pareto sense, the solution of the problem being the set of non dominated alternatives. Potentially optimal solutions also seem acceptable, from an intuitive point of view and due to their relation with the nondominated ones. Algorithms are provided to compute these solutions in general problems and in cases typical in practice: linear and bilinear problems. Other solution concepts are criticised on the grounds of being ad hoc. In summary, we have a more ro bust theory of decision making based on a weaker set ofaxioms, but embodying coherence, since it essentially implies carrying out a family of coherent dccision anitlyses.

Author : David Rios Insua
Publisher :
Page : 26 pages
File Size : 27,66 MB
Release : 1988
Category : Decision making
ISBN :

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We introduce several solution concepts, and analytic ways of determining them, which allow us to identify the possible competitors of a current best solution. We analyse, then, distance-based tools for sensitivity analysis, according to some general lines. Finally, we describe some computational experience with a real example and suggest some ways of displaying the information to the decision-maker."

Author : Evangelos Triantaphyllou
Publisher : Springer Science & Business Media
Page : 307 pages
File Size : 42,57 MB
Release : 2013-03-09
Category : Business & Economics
ISBN : 1475731574

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Multi-Criteria Decision Making (MCDM) has been one of the fastest growing problem areas in many disciplines. The central problem is how to evaluate a set of alternatives in terms of a number of criteria. Although this problem is very relevant in practice, there are few methods available and their quality is hard to determine. Thus, the question `Which is the best method for a given problem?' has become one of the most important and challenging ones. This is exactly what this book has as its focus and why it is important. The author extensively compares, both theoretically and empirically, real-life MCDM issues and makes the reader aware of quite a number of surprising `abnormalities' with some of these methods. What makes this book so valuable and different is that even though the analyses are rigorous, the results can be understood even by the non-specialist. Audience: Researchers, practitioners, and students; it can be used as a textbook for senior undergraduate or graduate courses in business and engineering.

Author : Alan G. Lockett
Publisher : Springer Science & Business Media
Page : 620 pages
File Size : 33,55 MB
Release : 2012-12-06
Category : Business & Economics
ISBN : 3642492983

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McrM has been an active research area for over 20 years and the previous conferences clearly showed a tremendous growth of interest. A variety of successful applications and recent developments of interactive computer software to support decision making confinn a sustained progress. We therefore decided to make our theme "Inlproving Decision Making in Organisations". '!he aim was to take stock of the impact of multicriteria concepts in organisations and to involve management practitioners from a wide range of backgrounds. To this end the conference was organised round five broad themes and papers were solicited on the following topics:- Psychology - how do individuals in practice use and relate to the methodologies. Organisation - how do our models fit into the decision making framework of real organisations. Application - how have the models been used in practice and what is the users view. Methodology - what are the new areas in model development. Related Areas - is there complementary work eg. Expert Systems which may be attempting to solve very similar problems. '!he call for papers produced an overwhelming response of over 100 papers. '!hey were from a variety of disciplines and applications, and we decided to devote approximately one day to each of the five areas. We are very impressed by the results which can be seen in this edited proceedings.

Author : David Rios Insua
Publisher : Springer Science & Business Media
Page : 431 pages
File Size : 43,61 MB
Release : 2012-12-06
Category : Mathematics
ISBN : 1461213061

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Robust Bayesian analysis aims at overcoming the traditional objection to Bayesian analysis of its dependence on subjective inputs, mainly the prior and the loss. Its purpose is the determination of the impact of the inputs to a Bayesian analysis (the prior, the loss and the model) on its output when the inputs range in certain classes. If the impact is considerable, there is sensitivity and we should attempt to further refine the information the incumbent classes available, perhaps through additional constraints on and/ or obtaining additional data; if the impact is not important, robustness holds and no further analysis and refinement would be required. Robust Bayesian analysis has been widely accepted by Bayesian statisticians; for a while it was even a main research topic in the field. However, to a great extent, their impact is yet to be seen in applied settings. This volume, therefore, presents an overview of the current state of robust Bayesian methods and their applications and identifies topics of further in terest in the area. The papers in the volume are divided into nine parts covering the main aspects of the field. The first one provides an overview of Bayesian robustness at a non-technical level. The paper in Part II con cerns foundational aspects and describes decision-theoretical axiomatisa tions leading to the robust Bayesian paradigm, motivating reasons for which robust analysis is practically unavoidable within Bayesian analysis.

Author : Joseph B. Kadane
Publisher : Cambridge University Press
Page : 402 pages
File Size : 36,30 MB
Release : 1999-08-13
Category : Mathematics
ISBN : 9780521649759

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This important collection of essays is a synthesis of foundational studies in Bayesian decision theory and statistics. An overarching topic of the collection is understanding how the norms for Bayesian decision making should apply in settings with more than one rational decision maker and then tracing out some of the consequences of this turn for Bayesian statistics. There are four principal themes to the collection: cooperative, non-sequential decisions; the representation and measurement of 'partially ordered' preferences; non-cooperative, sequential decisions; and pooling rules and Bayesian dynamics for sets of probabilities. The volume will be particularly valuable to philosophers concerned with decision theory, probability, and statistics, statisticians, mathematicians, and economists.

Author : Yong Shi
Publisher : Springer Nature
Page : 733 pages
File Size : 50,37 MB
Release : 2022-01-13
Category : Computers
ISBN : 9811636079

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Today, big data affects countless aspects of our daily lives. This book provides a comprehensive and cutting-edge study on big data analytics, based on the research findings and applications developed by the author and his colleagues in related areas. It addresses the concepts of big data analytics and/or data science, multi-criteria optimization for learning, expert and rule-based data analysis, support vector machines for classification, feature selection, data stream analysis, learning analysis, sentiment analysis, link analysis, and evaluation analysis. The book also explores lessons learned in applying big data to business, engineering and healthcare. Lastly, it addresses the advanced topic of intelligence-quotient (IQ) tests for artificial intelligence. /divSince each aspect mentioned above concerns a specific domain of application, taken together, the algorithms, procedures, analysis and empirical studies presented here offer a general picture of big data developments. Accordingly, the book can not only serve as a textbook for graduates with a fundamental grasp of training in big data analytics, but can also show practitioners how to use the proposed techniques to deal with real-world big data problems.