Today’s business world is characterized by a complex non-linear environment, non-hierarchical organization structures, multi-country and de-centralized operations, etc. The prominent models of decision-making that were primarily developed with the industrial economy in mind, and that viewed decision-making as a couple of linear sequential steps and “decisions given-and-decisions followed” — might not work too well. Knowledge-based economies call for developing decision-making models that represent the complexity of the present world business. Under such context, we present an alternative approach to studying management decision-making — seeking inspiration from the natural/biological systems. Bees show similar behavior in their foraging activities, as a single objective management decision-making problem. The uniqueness of the developed model lies in its ability to explain the major properties of a complex system, and the value that emergence (of a decision) brings to a company.
Philippe De Wilde
Philippe De Wilde obtained the PhD degree in mathematical physics and the MSc degree in computer science in 1985. He was Lecturer and Senior Lecturer in the Department of Electrical Engineering, Imperial College London, between 1989 and 2005. He is currently a Professor in the Intelligent Systems Lab of the Department of Computer Science. He is also Head of the School of Mathematical and Computer Sciences. Associate Editor, IEEE Transactions on Systems, Man, and Cybernetics, Part B. Laureate, Royal Academy of Sciences, Letters and Fine Arts of Belgium, 1988. Research Fellow, British Telecom, 1994. Vloeberghs Chair, Free University Brussels, 2010. He published 44 journal papers and 44 conference papers, and published four books, including ``Neural Network Models’’, and ``Convergence and Knowledge-processing in Multi-agent Systems’’. He develops biological and sociological principles that improve the design of decision making and of networks. His research interests are in: interacting networks in the brain; decision making under uncertainty; networked populations; stability, scalability and evolution of multi-agent systems.