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Management science is often confronted with optimization problems characterized 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, bio-mimetic approaches have received growing attention from marketing, finance and human resource researchers and executives as effective tools for practically handling such problems. Bio-mimetic 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. This text presents a selection of papers on bio-mimetic approaches and their application to management science. Most of these papers were presented at an ACSEG/CAEMS International Congress (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 bio-mimetic 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 tools for coping with some of the limitations of bio-mimetic methods.
