Description: 1. Introduction.- 1. MCDM problems.- 2. Solutions of MCDM problems.- 3. Decision processes and the application of MCDM methods.- 4. Concepts of 'correct' decision making in MCDM methods.- 5. Summary and conclusions.- 2. The Meta Decision Problem in MCDM.- 1. Methodological criticism in MCDM.- 2. The met a decision problem in MCDM.- 3. Summary and conclusions.- 3. Neural Networks and Evolutionary Learning For MCDM.- 1. Neural networks and MCDM.- 2. Evolutionary learning.- 3. Summary and conclusions.- 4. On the Combination of MCDM Methods.- 1. Introduction.- 2. Properties of MCDM methods.- 3. Properties of specific MCDM methods.- 4. Properties of neurons and neural networks.- 5. The combination of algorithms.- 6. Neural MCDM networks.- 7. Termination and runtime of the algorithm.- 8. Summary and conclusions.- 5. Loops - An Object Oriented DSS for Solving Meta Decision Problems.- 1. Preliminary remarks.- 2. Method integration, openness, and object oriented implementation.- 3. A class concept for LOOPS.- 4. Problem solving and learning from an object oriented point of view.- 5. MADM methods in LOOPS.- 6. Neural networks in LOOPS.- 7. Neural MCDM networks in LOOPS.- 8. Evolutionary algorithms in LOOPS.- 9. An extended interactive framework.- 10. Summary and conclusions.- 6. Examples of the Application of Loops.- 1. Some remarks on the application of LOOPS.- 2. The learning of utility functions.- 3. Stock selection.- 4. Stock price prediction and the learning of time series.- 5. Stock analysis and long-term prediction.- 6. Method learning.- 7. Meta learning.- 8. An integrated proposal for the application of LOOPS.- 9. Summary and conclusions.- 7. Critical Resume and Outlook.- References.- Appendices.- A- Some basic concepts of MCDM theory.- 1. Relations.- 2. Efficiencyconcepts and scalarizing theorems.- 3. Utility concepts and other axiomatics 166 B- Some selected MCDM methods.- 1. Simple additive weighting.- 2. Achievement levels.- 3. Reference point approaches.- 4. The outranking method PROMETHEE 171 C- Neural networks.- 1. Introduction to neural networks.- 2. Neural networks for intelligent decision support 178 D- Evolutionary algorithms.- 1. Introduction to evolutionary algorithms.- 2. The generalization of evolutionary algorithms 186 E- List of symbols 189 F- List of abbreviations.

Please Wait