A Loser Can Be a Winner: Comparison of Two Instance-based Learning Models in a Market Entry Competition

Games

Cleotilde Gonzalez¹  Varun Dutt² 

[1] Dynamic Decision Making Laboratory, Carnegie Mellon University, Pittsburgh, PA 15213, USA;

关键词: instance-based learning theory;    model comparison;    generalization;    parsimony;   

DOI  :  10.3390/g2010136

来源: mdpi

【 摘 要 】

This paper presents a case of parsimony and generalization in model comparisons. We submitted two versions of the same cognitive model to the Market Entry Competition (MEC), which involved four-person and two-alternative (enter or stay out) games. Our model was designed according to the Instance-Based Learning Theory (IBLT). The two versions of the model assumed the same cognitive principles of decision making and learning in the MEC. The only difference between the two models was the assumption of homogeneity among the four participants: one model assumed homogeneous participants (IBL-same) while the other model assumed heterogeneous participants (IBL-different). The IBL-same model involved three free parameters in total while the IBL-different involved 12 free parameters, i.e., three free parameters for each of the four participants. The IBL-different model outperformed the IBL-same model in the competition, but after exposing the models to a more challenging generalization test (the Technion Prediction Tournament), the IBL-same model outperformed the IBL-different model. Thus, a loser can be a winner depending on the generalization conditions used to compare models. We describe the models and the process by which we reach these conclusions.

【 授权许可】

CC BY   
© 2011 by the Cleotilde Gonzalez, Varun Dutt, and Tomas Lejarraga; licensee MDPI, Basel, Switzerland.