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

  • Sidnei A. de AraújoEmail author
  • Daniel F. de BarrosJr.
  • Ernani M. da Silva
  • Marcos V. Cardoso
Methodologies and Application


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.


Business game Knowledge-based approach Computational intelligence Data mining Fuzzy logic Decision tree Decision making 



The authors would like to thank Nove de Julho University for providing the data extracted from the BMS database and for permitting us to use it in this study. In addition, one of the authors (S. A. Araújo) would like to thank CNPq–Brazilian National Research Council for his research scholarship (Process No. 311971/2015-6).


This study was partially funded by the CNPq–Brazilian National Research Council, by means of a scholarship granted to S. A. Araújo (Grant No. 311971/2015-6).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not refer to studies with human participants or animals performed by any of the authors. We would also like to provide the following clarifications: (i) all the data used in our experiments were extracted from the BMS database, with permission from Nove de Julho University, with a commitment from the authors not to divulge the names of the students (in the role of players) and professors (in the role of instructors) involved in the simulations performed during 2015 (the period in which the data were extracted); (ii) one of the developers of BMS is a co-author of this work, which was essential in conducting this research.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Sidnei A. de Araújo
    • 1
    Email author
  • Daniel F. de BarrosJr.
    • 2
  • Ernani M. da Silva
    • 1
  • Marcos V. Cardoso
    • 3
  1. 1.Informatics and Knowledge Management Graduate ProgramNove de Julho UniversitySão PauloBrazil
  2. 2.Departament of InformaticsNove de Julho UniversitySão PauloBrazil
  3. 3.Design of Games CourseAnhembi Morumbi UniversitySão PauloBrazil

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