Evaluation and decision models : a critical perspective /

Evaluation and decision models : a critical perspective /

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Bibliographic Details
Imprint:Boston : Kluwer Academic Publishers, c2000.
Description:viii, 274 p. : ill. ; 25 cm.
Language:English
Series:International series in operations research & management science
Subject:
Decision making.
Operations research.
Decision making.
Operations research.
Format: Print Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/4480297
Hidden Bibliographic Details
Other authors / contributors:Bouyssou, D. (Denis)
ISBN:0792372506 (alk. paper)
Notes:Includes bibliographical references (p. [253]-269) and index.
Table of Contents:
  • 1. Introduction
  • 1.1. Motivations
  • 1.2. Audience
  • 1.3. Structure
  • 1.4. Outline
  • 1.5. Who are the authors?
  • 1.6. Conventions
  • 1.7. Acknowledgements
  • 2. Choosing on the basis of several opinions
  • 2.1. Analysis of some voting systems
  • 2.1.1. Uninominal election
  • 2.1.2. Election by rankings
  • 2.1.3. Some theoretical results
  • 2.2. Modelling the preferences of a voter
  • 2.2.1. Rankings
  • 2.2.2. Fuzzy relations
  • 2.2.3. Other models
  • 2.3. The voting process
  • 2.3.1. Definition of the set of candidates
  • 2.3.2. Definition of the set of the voters
  • 2.3.3. Choice of the aggregation method
  • 2.4. Social choice and multiple criteria decision support
  • 2.4.1. Analogies
  • 2.5. Conclusions
  • 3. Building and aggregating evaluations
  • 3.1. Introduction
  • 3.1.1. Motivation
  • 3.1.2. Evaluating students in Universities
  • 3.2. Grading students in a given course
  • 3.2.1. What is a grade?
  • 3.2.2. The grading process
  • 3.2.3. Interpreting grades
  • 3.2.4. Why use grades?
  • 3.3. Aggregating grades
  • 3.3.1. Rules for aggregating grades
  • 3.3.2. Aggregating grades using a weighted average
  • 3.4. Conclusions
  • 4. Constructing measures
  • 4.1. The human development index
  • 4.1.1. Scale Normalisation
  • 4.1.2. Compensation
  • 4.1.3. Dimension independence
  • 4.1.4. Scale construction
  • 4.1.5. Statistical aspects
  • 4.2. Air quality index
  • 4.2.1. Monotonicity
  • 4.2.2. Non compensation
  • 4.2.3. Meaningfulness
  • 4.3. The decathlon score
  • 4.3.1. Role of the decathlon score
  • 4.4. Indicators and multiple criteria decision support
  • 4.5. Conclusions
  • 5. Assessing competing projects
  • 5.1. Introduction
  • 5.2. The principles of CBA
  • 5.2.1. Choosing between investment projects in private firms
  • 5.2.2. From Corporate Finance to CBA
  • 5.2.3. Theoretical foundations
  • 5.3. Some examples in transportation studies
  • 5.3.1. Prevision of traffic
  • 5.3.2. Time gains
  • 5.3.3. Security gains
  • 5.3.4. Other effects and remarks
  • 5.4. Conclusions
  • 6. Comparing on several attributes
  • 6.1. Thierry's choice
  • 6.1.1. Description of the case
  • 6.1.2. Reasoning with preferences
  • 6.2. The weighted sum
  • 6.2.1. Transforming the evaluations
  • 6.2.2. Using the weighted sum on the case
  • 6.2.3. Is the resulting ranking reliable?
  • 6.2.4. The difficulties of a proper usage of the weighted sum
  • 6.2.5. Conclusion
  • 6.3. The additive value model
  • 6.3.1. Direct methods for determining single-attribute value functions
  • 6.3.2. AHP and Saaty's eigenvalue method
  • 6.3.3. An indirect method for assessing single-attribute value functions and trade-offs
  • 6.3.4. Conclusion
  • 6.4. Outranking methods
  • 6.4.1. Condorcet-like procedures in decision analysis
  • 6.4.2. A simple outranking method
  • 6.4.3. Using ELECTRE I on the case
  • 6.4.4. Main features and problems of elementary outranking approaches
  • 6.4.5. Advanced outranking methods: from thresholding towards valued relations
  • 6.5. General conclusion
  • 7. Deciding automatically
  • 7.1. Introduction
  • 7.2. A System with Explicit Decision Rules
  • 7.2.1. Designing a decision system for automatic watering
  • 7.2.2. Linking symbolic and numerical representations
  • 7.2.3. Interpreting input labels as scalars
  • 7.2.4. Interpreting input labels as intervals
  • 7.2.5. Interpreting input labels as fuzzy intervals
  • 7.2.6. Interpreting output labels as (fuzzy) intervals
  • 7.3. A System with Implicit Decision Rules
  • 7.3.1. Controlling the quality of biscuits during baking
  • 7.3.2. Automatising human decisions by learning from examples
  • 7.4. An hybrid approach for automatic decision-making
  • 7.5. Conclusion
  • 8. Dealing with uncertainty
  • 8.1. Introduction
  • 8.2. The context
  • 8.3. The model
  • 8.3.1. The set of actions
  • 8.3.2. The set of criteria
  • 8.3.3. Uncertainties and scenarios
  • 8.3.4. The temporal dimension
  • 8.3.5. Summary of the model
  • 8.4. A didactic example
  • 8.4.1. The expected value approach
  • 8.4.2. Some comments on the previous approach
  • 8.4.3. The expected utility approach
  • 8.4.4. Some comments on the expected utility approach
  • 8.4.5. The approach applied in this case: first step
  • 8.4.6. Comment on the first step
  • 8.4.7. The approach applied in this case: second step
  • 8.5. Conclusions
  • 9. Supporting decisions
  • 9.1. Preliminaries
  • 9.2. The Decision Process
  • 9.3. Decision Support
  • 9.3.1. Problem Formulation
  • 9.3.2. The Evaluation Model
  • 9.3.3. The final recommendation
  • 9.4. Conclusions
  • Appendix A
  • Appendix B
  • 10. Conclusion
  • 10.1. Formal methods are all around us
  • 10.2. What have we learned?
  • 10.3. What can be expected?
  • Bibliography
  • Index

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