Table Of ContentBIO-MIMETIC APPROACHES IN MANAGEMENT SCIENCE
Advances in Computational Management Science
VOLUMEl
SERIES EDITORS
Hans Amman, University ofA msterdam, Amsterdam, The Netherlands
Berc Rustem, Imperial College, UK
EDITORIAL BOARD
Christophe Deissenberg, University ofNantes
Artbur Farley, University of Oregon
Manfred Gilli, University ofGeneva
David Kendrick, University ofA msterdam
David Luenberger, Stanford University
Rik Maes, University ofA msterdam
Istvan Maros, Imperial College
John Mulvey, Princeton University
Anna Nagurney, University ofM assachusetts
Soren Nielsen, University ofTexas
Louis Pau, Ericsson, Alsjo, Sweden
Edison Tse, Stanford University
Andy Whinston, University ofTexas
Bio-Mimetic Approaches
I• n
Management Science
edited by
Jacques-Marie Aurifeille
University ofP oitiers
and
Christophe Deissenberg
University ofNantes
SPRINGER-SCIENCE+BUSINESS MEDIA, B.V.
A C.I.P. Catalogue record for this book is available from the Library of Congress.
ISBN 978-1-4419-4791-8 ISBN 978-1-4757-2821-7 (eBook)
DOI 10.1007/978-1-4757-2821-7
Printed on acid-free paper
All Rildtts Reserved
© 1998 Springer Science+Business Media Dordrecht
Originally published by Kluwer Academic Publishers in 1998
Softcover reprint of the bardeover Ist edition 1998
No part of the material protected by this copyright notice may be reproduced or
utilized in any form or by any means, electronic or mechanical,
including photocopying, recording or by any information storage and
retrieval system, without written permission from the copyright owner
TADLE OF CONTENTS
Foreword...................................................................................... vii
1. The Potential of Neural Networks Evaluated within a Taxonomy of
Marketing Applications
Stefania Pandelidaki, Andrew N.Burgess .............................................. 1
2. Complexity Control and Generalization in Multilayer Perceptrons
Patrick Gallinari and Tautvydas Cibas .. ... ......... .. ... ..... .. . . .. . . . .... ... .... ... .. 13
3. Application of Neural Networks to Bond Rating and House Pricing
Rennie Daniels, Bart Kamp and William Verkooijen .............................. 27
4. Connexionist Approach and Corporate Distress Diagnosis:
a Contribution to the Processing of lncomplete Information
Jean-Fmn~ois Casta and Bernard Pmt ................................................... 47
5. Comparison ofthe Predictivity of a Neural Network with
Backpropagation with those using Linear Regression, Logistic and
A.LD. Methods for Direct Marketing Scoring
Pierre Desmet .................................................................................. 61
6. Comparison of Discriminant Analysis and Neural Networlu
Application for the Detection of Company Failures
Mireille Bardos and Wenbua Zhu ....................................................... 77
7. Using Artificial Neural Nets to Specify and Estimate Aggregate
Reference Price Models
Martin Natter and Hamld Hruschka..................................................... 101
8. Towards Connectionist Merchandising:
a Conceptual and Operational Definition
Ph. Robert Demontrond and D. Thiel................................................. 119
9. A Statistical Methodology for Specifying Neural Network Models:
Application to the Identitication of Cross-selling Oi>portunities
Andrew N. Burgess and Stefania Pandelidaki ....................................... 139
10. Optimization by a Genetic Algorithm of Stochastic Linear Models
of Time Series
R. Bone, R. Thillier, F. Yvon and J.P. Asselin de Beauville ................. 153
11. A Bio-mimetic Clustenvise Regression Algorithm for Consumer
Segmentation
Jacques-Marie Aurifeille ................................ ............... .. ... . .. . .. ......... 163
12. Hybrid Genetic Leaming of Hidden Markov Models For Time Series
Prediction
Mohamed Slimane, GillesVenturini, Jean-Pierre Asselin de Beauville
and Thierry Brouard .. .. .. .... .... . ..... .. .. ............................................. ... 179
13. Leaming How to Regulate a Polluter with Unknown Characteristics:
An Application of Genetic Algorithms to a Game of Dynamic
Pollution Control
Thomas Vallee and Christophe Deissenberg . . . . . . . . . . . . . . . . . . . . . . . 197
FOREWORD
Management Science is often confronted with optimization problems characterized
by weak underlying theoretical models and complex constraints. Among them, one
might mention data analysis (c lassification, multidimensional analysis, discriminant
analysis) as weH as modeling (forecasting, confrrmatory analysis, expert system
design).
The classical way to solve such complex problems is to develop formal, analytical
models and to solve them on the basis of statistical assumptions on the data
distribution and on the type of relationship between the variables. In some cases,
however, this approach is not adequate, either because no efficient model can be
developed (as is frequently the case when socio-psychological phenomena are
studied) or because of heterogeneity and discontinuity among the data.
However, nature is often confronted with complex problern and apparently solves
them without botherlog about formal models. This fact has drawn growing attention
from researchers and motivated "biomimetic" paradigms in which less weight is
placed on formal modeling and more on heuristic problern solving than was
traditionally the case. The corresponding theoretical advances as well as the
increasing availability of appropriate software has led to the widespread use of
biomimetic approaches in practical problem-solving. This is most evident in
Management Sciences: Biomimetic methods are now routinely used by Marketing,
Finance and Human Resource researchers or executives for handling everyday
management problems.
The biomimetic paradigms include a variety of heuristic methods, such as neural
networks, genetic algorithms, immunitary nets, or cellular automata. Basic to all
these approaches is that they are based on the interconnection of many elementary
cells (thus, one often speaks of "connectionist" approaches). The behavior of each
cell follows a few elementary rules that can easily be translated into computer
programs and resemble those underlying social or individual learning: selection,
crossover, mutation, memorization. Each cell contributes locally to the solution of a
global problem. Thus, complex functions can easily be formed by connecting several
cells. The cooperation between cells can take a variety of forms. Basically, the
information is shared and transmitted through market mechanisms or genetic
crossovers. In some cases, a specialization may occur: some cells get in charge of the
routine tasks while others are responding to transitory changes in the environment. In
other cases, competition arises among the cells, simulating a natural selection
mechanism.
The shared processing and the simplicity of the basic rules make it possible to find
solutions independently of the nature of the data or constraints, and without having a
precise model of the general problem. In particular, neural networks provide a
theoretical ("model free") simulators that require little or no statistical assumptions
and offer an attractive alternative whenever inductive theory and formal modeling
proved disappointing. Other biomimetic approaches like genetic algorithms offer
vii
"universal optimizers" which can be used to solve complex optimization problems
such as those involving several non-parametric constraints. Being based on numerous
individual explorers, these approaches ensure better coverage of the error surface
than heuristics based on a single explorer.
Thus, at frrst glance, combining neural networks and genetic algorithms into a single
hybrid algorithm may appear as the perfect solution to any type of problem.
However, the biomimetic approaches arenot without serious limitations of their own.
Therefore, it is necessary to carefully compare the performance of the available
analytical and bio-mimetic methods within specific classes of problems.
Another characteristic of biomimetic approaches, the importance of random choice,
may justify associating different methods for concrete problern solving. Unlike most
stochastic methods, in which a zest of randomness is added to logical searches,
connectionist methods rely on much random choice slightly conditioned by logical
considerations. As a consequence, biomimetic methods can need a great amount of
computer time before arriving at a satisfactory solution -a fact that is often critically
mentioned by the supporters of analytical methods. In such cases, hybrid methods
combining analytical and biomimetic approaches (e.g. parametric regression and
neural networks) can be extremely powerful.
A common criticism about bio-mimetic approaches is that they are " black boxes",
more concemed with problern solving than with formal modeling. Bio-mimetic
methods are not parsimonious. Whenever formal modeling and semantic
considerations are not specifically quantified as objectives, the profusion of cells
may impede the understanding of the underlying model. However, the "black box"
vs. "white box" dilemma may be simply a question of problern formulation.
Choosing the right network structure and leaming rules can lead to clear-cut
interpretations. This is typically the case with neural networks when competitive
leaming is used for pattern recognition or clustering. Nevertheless, to some extent,
the objectives of symbolic interpretation and fitting are contradictory. The analyst
has to find a satisfactory balance between the two. In such cases, the generalization
capacities of each method has to be considered. As a consequence of their flexibility,
biomimetic methods have a tendency to overfitting: excellent results with one data
set may be less satisfactory when the resulting simulator is applied to different data.
This is one of the most important questions to be addressed by neural network
designers.
Indeed, biomimetic approaches do not offer an universal solution to any type of
question. Their limitations, as well as their broad development and application
perspectives, must be known in order to choose the right method and to customize it
in an appropriate way. What makes them particularly attractive is the possibility to
look at problems differently in order to find new solution as well as better solutions
on the basis of " co-agitation", that is, of a phenomenon not unknown to managers
and at the etymological roots of "cogitation".
This book is a selection of recent papers on biomimetic approaches and their
application to Management Science. Most of these papers were presented at the last
viii
ACSEG/CAEMS International Congresses (Connectionist Approaches in Econornics
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 Iimits of the biornimetic
approaches are discussed, 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 weil as new tools for coping
with some limitations of the biomimetic methods.
The book is structured in 5 different parts.
The first part provides an overview of the different problems which may be solved
using biomimetic approaches. In this part, S. Pandelidaki and A.N. Burgess position
neural networks relative to other modeling techniques in the context of marketing
applications and review different types of networks.
The second part of the book deals with the fundamental questions of controlling
overfitting and choosing a relevant network architecture. P. Gallinari and T. Cibas
present practical approaches for optirnizing the generalization ability of neural
networks. H. Daniels, B. Kamp and W. Verkooijen provide a method for choosing a
relevant network architecture. The approach is illustrated in the case of bond rating
and house pricing. Finally, J-F. Casta and B. Prat show how the architectural
versatility of neural networks may help solving the problern of rnissing data and
diagnosing corporate distress.
Once the architectural and generalization questions have been analyzed, classical
data modeling methods and biornimetic approaches can be confronted. In the third
part of the book, P. Desmet compares a back-propagation neural network with the
logistic regression and A.I.D. methods. Empirical data are used to contrast the
predictive power of these methods for direct marketing scoring. Next, M. Bardos
and W. Zhu compare linear discrirninant analysis and neural networks when applied
to the detection of company failures. The article by M. Natter and H. Hruschka
makes a double comparison. Based on panel data, static vs. dynarnic models of
price effects are confronted using linear and non linear regression functions. By
contrast, the last paper by P. Robert-Demontrond and D. Thiel is not concerned with
neural networks. In this paper, the authors compare the ability of two stochastic
methods - the simulated annealing and a non-standard genetic algorithm using bi
dimensional chromosomes -to optirnize a given objective function in the context of a
merchandising problem.
Beyond comparisons, several methods may be associated in the same algorithm and
combine their specific advantages. Such hybrid approaches are presented in the
fourth part of the book. In the paper of A. Burgess and S. Pandelidaki, a two-step
procedure chaining a logit analysis with a neural network is applied to the
identification of cross-selling opportunities. The paper of R. Bone, R. Thillier, F.
Yvon and J-P. Asselin is based on a different kind of hybridization. It confronts in
the same genetic algorithrnic procedure three forecasting models: AR, MA and
ARMA. Next, J-M. Aurifeille proposes a clusterwise regression technique based on
ix
the hybridization of two biomimetic methods: the leaming vector quantification and
a genetic algorithm. The algorithm is compared with the original learning vector
quantification in the case of consumer segmentation. The paper by M. Slimane, G.
Venturini, J-P. Asselin de Beauville and T. Brouard combines genetic algorithm
heuristics with a gradient search for learning hidden Markov models within times
series. The approach is applied to well known data bases on air traffic and on British
obligations.
In the fifth part, an article by Th. Vallee and Ch. Deissenberg introduces an
economist's perspective in an otherwise purely management-related volume. The
authors address the problern of governmental regulation of a polluting monopolist
when the regulatory agency does not know the monopolist's characteristics. Contrary
to the usual practice, the genetic algorithm is not used off-line to find an optimal
solution on the basis of the a priori description of the problern exclusively. Instead, it
is used on-line, each chromosome corresponding to an action (the fixation of a
taxation level) that will be used to extract real-time inforrnation on the monopolist's
best reaction. The numerical results indicate that the approach may indeed prove
useful to help solving a crucial but yet largely unsolved problem.
Last, but not least, the editors would like to thank Dagmar Bergmann, Jacques-Marie
Aurifeille's wife, for spending many hours ensuring the graphic quality of the book.
Jacques-Marie AURIFEILLE Christophe OBISSENBERG
Director of the GREGEOI Director of the LEN-C3E
Universire de la Reunion Universire de Nantes
aurifeil @univ-reunion.fr [email protected]
X
1
T11E POTENTIAL OF NEURAL NETWORKS
EVA LUATED WITHIN A TAXONOMY OF
MARKETING APPLICATIONS
Stefania Pandelidaki and Andrew N.Burgess
Department ofD ecision Seiences
London Business School
United Kingdom
1. INTRODUCTION
Neural Networks techniques have developed rapidly during the last decade and are
now used in vecy diverse fields such as speech and character recognition, medical
diagnosis, time series analysis, operations research, and management science. Over
recent years they have attracted a great deal of interest in the area of forecasting in the
fmancial industty (see Refenes 1995, Trippi and Turban 1993). The reasons for these
developments are the reducing costs of computing and the relative progress cf
theoretical and applied research in neural networks. Their use in marketing is now
ernerging due mainly to the the increasing availability of marketing data, and the
sometimes limited effectiveness of traditional statistical models as analytical tools
for decision-making.
In fact, neural networks have been used in marketing as an alternative to statistical
models such as linear or logistic regression, factor analysis, discriminant analysis,
and CHAID (Chi-Squared Automatie Interaction Detector), (see Coyne 1993,
Gregoty 1993, Hruschka 1994, Murray 1993, Ripley 1994). Their performance is
encouraging in the sense that in most cases they perform better or at least as weil as
statistical models, though equivocal studies do exist. However, most of the current
research on the subject is either in the form of case study, only exploring the
potential of the new technology, or of comparative nature, but without much
consideration ofthe underlying methodological issues. Therefore, there is a need to
critically review the use of neural networks in the marketing field, rigorously
evaluate their contribution, and point out directions for future research.
We suspect that there are certain classes ofproblems and certain conditions under
which one methodology performs better than the other, and others for which the two
are equivalent. These classes of problems and conditions need to be identified. This
paper contributes towards this line ofinquity.
I.-M. Aurifeille and C. Deissenberg (eds.), Bio-Mimetic Approaches in Manogement Science, 1-12.
© 1998 Kluwer Academic Publishers.
