Since the beginning of the 1980's, a lot of news approaches of biomimetic inspiration have been defined and developed for imitating the brain behavior, for modeling non linear phenomenon, for providing new hardware architectures, for solving hard problems. They are named Neural Networks, Multilayer Perceptrons, Genetic algorithms, Cellular Automates, Self-Organizing maps, Fuzzy Logic, etc. They can be summarized by the word of Connectionism, and consist of an interdisciplinary domain between neuroscience, cognitive science and engineering. First they were applied in computer sciences, engineering, biological models, pattern recognition, motor control, learning algorithms, etc. But rapidly, it appeared that these methods could be of great interest in the fields of Economics and Management Sciences. The main difficulty was the distance between researchers, the difference in the vocabulary used by the ones and the others, their basic background. The main notions used by these new techniques were not familiar to the Social and Human Sciences researchers. What are they ? Four of them are now very briefly introduced, but the reader will find more information in the following chapters.

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.

Some 20 years after the emergence of configurational theory as a key perspective in organization studies in the 1990s, this approach has yet to deliver on its promise. While we know that configurations the relative arrangement of parts and elements - matters, empirical research on configurations is just beginning to deliver on its promise.

The social sciences, especially economics, management, and organizational science, are experiencing a tremendous renewed interest for their epistemological and methodological statutes, as witnessed by the many books and specialized journals established during the last two decades. Relational Methodologies and Epistemology in the Economics and Management Sciences identifies and presents the four main network-based methodologies including network analysis, Boolean network simulation modeling, artificial neural network simulation modeling, and agent-based simulation modeling in addition to their conceptual-epistemological implications and concrete applications within the social and natural sciences. Featuring a critical assessment of relational methodologies and their practical applications, this timely publication is ideal for use by corporate R&D departments, researchers, theorists, and graduate-level students.

Research on forecasting methods has made important progress over recent years and these developments are brought together in the Handbook of Economic Forecasting. The handbook covers developments in how forecasts are constructed based on multivariate time-series models, dynamic factor models, nonlinear models and combination methods. The handbook also includes chapters on forecast evaluation, including evaluation of point forecasts and probability forecasts and contains chapters on survey forecasts and volatility forecasts. Areas of applications of forecasts covered in the handbook include economics, finance and marketing. *Addresses economic forecasting methodology, forecasting models, forecasting with different data structures, and the applications of forecasting methods *Insights within this volume can be applied to economics, finance and marketing disciplines

This volume is the first part of the two-volume proceedings of the International C- ference on Artificial Neural Networks (ICANN 2005), held on September 11–15, 2005 in Warsaw, Poland, with several accompanying workshops held on September 15, 2005 at the Nicolaus Copernicus University, Toru , Poland. The ICANN conference is an annual meeting organized by the European Neural Network Society in cooperation with the International Neural Network Society, the Japanese Neural Network Society, and the IEEE Computational Intelligence Society. It is the premier European event covering all topics concerned with neural networks and related areas. The ICANN series of conferences was initiated in 1991 and soon became the major European gathering for experts in those fields. In 2005 the ICANN conference was organized by the Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland, and the Nicolaus Copernicus Univ- sity, Toru , Poland. From over 600 papers submitted to the regular sessions and some 10 special c- ference sessions, the International Program Committee selected – after a thorough peer-review process – about 270 papers for publication. The large number of papers accepted is certainly a proof of the vitality and attractiveness of the field of artificial neural networks, but it also shows a strong interest in the ICANN conferences.

Some of the most challenging problems in science and engineering are being addressed by the integration of computation and science, a research ?eld known as computational science. Computational science plays a vital role in fundamental advances in biology, physics, chemistry, astronomy, and a host of other disciplines. This is through the coordination of computation, data management, access to instrumentation, knowledge synthesis, and the use of new devices. It has an impact on researchers and practitioners in the sciences and beyond. The sheer size of many challenges in computational science dictates the use of supercomputing, parallel and distri- ted processing, grid-based processing, advanced visualization and sophisticated algorithms. At the dawn of the 21st century the series of International Conferences on Computational Science (ICCS) was initiated with a ?rst meeting in May 2001 in San Francisco. The success of that meeting motivated the organization of the - cond meeting held in Amsterdam April 21–24, 2002, where over 500 participants pushed the research ?eld further. The International Conference on Computational Science 2003 (ICCS 2003) is the follow-up to these earlier conferences. ICCS 2003 is unique, in that it was a single event held at two di?erent sites almost opposite each other on the globe – Melbourne, Australia and St. Petersburg, Russian Federation. The conference ran on the same dates at both locations and all the presented work was published in a single set of proceedings, which you hold in your hands right now.