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Sensitivity Analysis in Multi-objective Decision Making

  • David Ríos Insua

Part of the Lecture Notes in Economics and Mathematical Systems book series (LNE, volume 347)

Table of contents

  1. Front Matter
    Pages I-XI
  2. David Ríos Insua
    Pages 127-163
  3. David Ríos Insua
    Pages 164-171
  4. Back Matter
    Pages 172-193

About this book

Introduction

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.

Keywords

Entscheidungen bei unvollständiger Information Entscheidungstheorie Multi-objective decision making Sensitivität Sensitivitätsanalyse algorithms decision making decision theory sensitivity analysis

Authors and affiliations

  • David Ríos Insua
    • 1
    • 2
  1. 1.School of Computer StudiesUniversity of LeedsLeedsUK
  2. 2.Dpt. de Inteligencia Artificial, Facultad de InformáticaUniversidad Politécnica de MadridMadridSpain

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-51656-6
  • Copyright Information Springer-Verlag Berlin Heidelberg 1990
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-540-52692-6
  • Online ISBN 978-3-642-51656-6
  • Series Print ISSN 0075-8442
  • Buy this book on publisher's site
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