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Herausgeber: 
  • Jacques-Marie Aurifeille
  • Christopher Deissenberg
  • Bio-Mimetic Approaches in Management Science 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 2 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   i.d.R. innert 7-14 Tagen versandfertig
    Veröffentlichung:  April 1998  
    Genre:  Wirtschaft / Recht 
    ISBN:  9780792349938 
    EAN-Code: 
    9780792349938 
    Verlag:  Springer Us 
    Einband:  Gebunden  
    Sprache:  English  
    Dimensionen:  H 241 mm / B 160 mm / D 17 mm 
    Gewicht:  512 gr 
    Seiten:  228 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    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.

      



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