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.
