Applying computational intelligence techniques to improve the decision making of business game players

Business games have been widely used as differentiated pedagogical tools to provide experiential learning for business students. However, a critical problem with these tools is the issue of how to give feedback to students during the runtime of the simulation, especially in view of the high number of players involved in the game and the large amount of data generated in the simulations. In this scenario, intelligent mechanisms are desirable to make knowledge-based inferences, providing information which can assist both the players and the instructors facilitating the gaming process. In this work, we present an innovative knowledge-based approach focused on business games. Firstly, we apply data mining techniques to identify the behavioral patterns of players, based on their previous decisions stored in the database of a business game called business management simulator (BMS) that is used as a support tool for teaching concepts of production management, sales and business strategies. Secondly, based on these patterns, we develop a fuzzy inference system (FIS) to predict players’ performance based on their decisions in the game. Experimental results from a comparison of the real performance of players with the performance calculated by the proposed FIS show that this approach is very useful in the business game analyzed here, since it can help students during the simulation runtime, allowing them to improve their decisions. It is also clear that the proposed approach can be easily adapted to other business games, and particularly those with a similar structure to that of BMS.