The MIT Beer Distribution Game Revisited: Genetic Machine Learning and Managerial Behavior in a Dynamic Decision Making Experiment

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@InProceedings{GeyerSchulz96a,

• crossref = "Herrera96",
• author = "Andreas Geyer--Schulz",
• title = "The {M}{I}{T} Beer Distribution Game Revisited: Genetic Machine Learning and Managerial Behavior in a Dynamic Decision Making Experiment",
• year = "1996",
• pages = "658--682",
• keywords = "genetic algorithms, genetic programming, Experimental economics, organizational learning, simulation, gaming, system dynamics, fuzzy genetic programming.",
• abstract = "The paper reports on the experiment of applying genetic machine learning methods to breeding heuristic for playing the MIT beer distribution game. In the MIT beer distribution game a team of four subjects acts as managers of a simulated industrial production and distribution system with the aim of minimising total inventory. The system consists of a chain of ofur coupled stock management systems with uncertain demand, tiem delays, feedbacks, multiple actors, non-linearities and restricted information availability. The complexity of the system - it is a 23rd order non-linear difference equation - renders calculation of the optimal behaviour intractable. In the experiment threee genetic machine learning methods (a simple genetic algorithm, genetic programming, and fuzzy genetic programming) are applied to the beer distribution game. The results of the methods are compared with the previously known best solution and with the performance of a group of subjects which actually played the game.",
• notes = "In \cite{Herrera96} http://decsai.ugr.es/~herrera/abstracts.html#c30",

}

Genetic Programming entries for Andreas Geyer-Schulz

Citations