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

Conference paper

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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