Game-Theoretical and Evolutionary Simulation: A Toolbox for Complex Enterprise Problems

Abstract

Complex systems resist analysis and require experimenting or simulation. Many enterprise settings, for instance with cases of competition in an open market or “co-opetition” with partners, are complex and difficult to analyze, especially to accurately figure the behaviors of other companies. This paper describes an approach towards modeling a system of actors which is well suited to enterprise strategic simulation. This approach is based upon game theory and machine learning, applied to the behavior of a set of competing actors. Our intent is not to use simulation as forecasting - which is out of reach precisely because of the complexity of these problems - but rather as a tool to develop skills through what is commonly referred as “serious games”, in the tradition of military wargames. Our approach, dubbed GTES, is built upon the combination of three techniques: Monte-Carlo sampling, searching for equilibriums from game theory, and local search meta-heuristics for machine learning. We illustrate this approach with “Systemic Simulation of Smart Grids”, as well as a few examples drawn for the mobile telecommunication industry.

Keywords

simulation machine learning game theory evolutionary game theory evolutionary algorithms Monte-Carlo dynamic games serious games 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Bouygues TelecomIssy-les-MoulineauxFrance

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