Since the beginning of the 1980's, many new 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. These approaches include: neural networks, multilayer perceptrons, genetic algorithms, cellular Automates, and self-organizing maps. They can be summarized by the word "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. However, it rapidly 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 and their basic background. The main notions used by these new techniques were not familiar to the social and human sciences researchers.The purpose of the book is to put these new techniques at the disposal of researchers coming from different horizons, to assess the state of the art, to identify the capability of these new algorithms, to evidence the contribution of these methods to economics and management sciences. The contributions in this book bring new confirmations of the interest of connectionist approaches for researchers in economics and management sciences. The first part is dedicated to theoretical advances; the second to a wide range of applications. All papers contain interesting results on each subject, which would have been very difficult to show with classical techniques but which has been proven by using these connectionist non-linear methods.
