Bio-Mimetic Approaches in Management Science / Edition 1by Jacques-Marie Aurifeille
Pub. Date: 04/30/1998
Publisher: Springer US
Management Science is often confronted with optimization problems characterised by weak underlying theoretical models and complex constraints. Among them, one finds data analysis, pattern recognition (classification, multidimensional analysis, discriminant analysis) as well as modelling (forecasting, confirmatory analysis, expert system design). In recent years,
Management Science is often confronted with optimization problems characterised by weak underlying theoretical models and complex constraints. Among them, one finds data analysis, pattern recognition (classification, multidimensional analysis, discriminant analysis) as well as modelling (forecasting, confirmatory analysis, expert system design). In recent years, biomimetic approaches have received growing attention from Marketing, Finance and Human Resource researchers and executives as effective tools for practically handling such problems. Biomimetic approaches include a variety of heuristic methods - such as neural networks, genetic algorithms, immunitary nets, cellular automata - that simulate nature's way of solving complex problems and, thus, can be considered as numerical transpositions of true life problem solving.
Bio-Mimetic Approaches in Management Science presents a selection of recent papers on biomimetic approaches and their application to Management Science. Most of these papers were presented at the last ACSEG/CAEMS International Congresses (Approches Connexionnistes en Sciences Economiques et de Gestion/Connectionnist Approaches in Economics and Management Science). All papers combine the discussion of conceptual issues with illustrative empirical applications, and contain detailed information on the way heuristics are practically implemented. The advantages and limits of the biomimetic approaches are discussed in several of the papers, either by comparing these approaches with more classical methods (logit models, clustering), or by investigating specific issues like overfitting and robustness. Synthesizing overviews are provided, as well as new tools for coping with some of the limitations of biomimetic methods.
- Springer US
- Publication date:
- Advances in Computational Management Science Series, #1
- Edition description:
- Product dimensions:
- 6.10(w) x 9.25(h) x 0.02(d)
Table of Contents
Foreword. 1. The Potential of Neural Networks Evaluated within a Taxonomy of Marketing Applications; S. Pandelidaki, A.N. Burgess. 2. Complexity Control and Generalization in Multilayer Perceptrons; P. Gallinari, T. Cibas. 3. Application of Neural Networks to Bond Rating and House Pricing; H. Daniels, et al. 4. Connexionist Approach and Corporate Distress Diagnosis: A Contribution to the Processing of Incomplete Information; J.-F. Casta, B. Prat. 5. Comparison of the Predictivity of a Neural Network with Backpropagation with Those Using Linear Regression, Logistic and A.I.D. Methods for Direct Marketing Scoring; P. Desmet. 6. Comparison of Discriminant Analysis and Neural Networks Application for the Detection of Company Failures; M. Bardos, Wenhua Zhu. 7. Using Artificial Neural Nets to Specify and Estimate Aggregate Reference Price Models; M. Natter, H. Hruschka. 8. Towards Connectionist Merchandising: A Conceptual and Operational Definition; Ph.R. Demontrond, D. Thiel. 9. A Statistical Methodology for Specifying Neural Network Models: Application to the Identification of Cross-Selling Opportunities; A.N. Burgess, S. Pandelidaki. 10. Optimization by a Genetic Algorithm of Shastic Linear Models of Time Series; R. Boné, et al. 11. A Bio-Mimetic Clusterwise Regression Algorithm for Consumer Segmentation; J.-M. Aurifeille. 12. Hybrid Genetic Learning of Hidden Markov Models for Time Series Prediction; M. Slimane, et al. 13. Learning How to Regulate a Polluter with Unknown Characteristics: An Application of Genetic Algorithms to a Game of Dynamic Pollution Control; T. Vallée, C.Deissenberg.
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