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Bio-Mimetic Approaches in Management Science

  • Book
  • © 1998

Overview

Editors:
  1. Jacques-Marie Aurifeille

    1. University of Poitiers, France

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    You can also search for this editor in PubMed   Google Scholar

  2. Christophe Deissenberg

    1. University of Nantes, France

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    You can also search for this editor in PubMed   Google Scholar

Part of the book series: Advances in Computational Management Science (AICM, volume 1)

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Table of contents (13 chapters)

  1. Front Matter

    Pages i-x
    Download chapter PDF

  2. The Potential of Neural Networks Evaluated Within a Taxonomy of Marketing Applications

    • Stefania Pandelidaki, Andrew N. Burgess
    Pages 1-12

  3. Complexity Control and Generalization in Multilayer Perceptrons

    • Patrick Gallinari, Tautvydas Cibas
    Pages 13-25

  4. Application of Neural Networks to Bond Rating and House Pricing

    • Hennie Daniels, Bart Kamp, William Verkooijen
    Pages 27-45

  5. Connexionist Approach and Corporate Distress Diagnosis

    • Jean-François Casta, Bernard Prat
    Pages 47-59

  6. 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

    • Pierre Desmet
    Pages 61-75

  7. Comparison of Discriminant Analysis and Neural Networks Application for the Detection of Company Failures

    • Mireille Bardos, Wenhua Zhu
    Pages 77-100

  8. Using Artificial Neural Nets to Specify and Estimate Aggregate Reference Price Models

    • Martin Natter, Harald Hruschka
    Pages 101-118

  9. Towards Connectionist Merchandising: A Conceptual and Operational Definition

    • Ph. Robert Demontrond, D. Thiel
    Pages 119-137

  10. A Statistical Methodology for Specifying Neural Network Models: Application to the Identification of Cross-Selling Opportunities

    • Andrew N. Burgess, Stefania Pandelidaki
    Pages 139-152

  11. Optimization by a Genetic Algorithm of Stochastic Linear Models of Time Series

    • R. Boné, R. Thillier, F. Yvon, J. P. Asselin de Beauville
    Pages 153-162

  12. A Bio-Mimetic Clusterwise Regression Algorithm for Consumer Segmentation

    • Jacques-Marie Aurifeille
    Pages 163-178

  13. Hybrid Genetic Learning of Hidden Markov Models for Time Series Prediction

    • Mohamed Slimane, Gilles Venturini, Jean-Pierre Asselin de Beauville, Thierry Brouard
    Pages 179-196

  14. Learning how to regulate a polluter with unknown characteristics: An application of genetic algorithms to a game of dynamic pollution control

    • Thomas Vallée, Christophe Deissenberg
    Pages 197-208
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Keywords

About this book

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.

Editors and Affiliations

  • University of Poitiers, France

    Jacques-Marie Aurifeille

  • University of Nantes, France

    Christophe Deissenberg

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