A Loser Can Be a Winner: Comparison of Two Instance-based Learning Models in a Market Entry Competition
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.
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- Pavlo Blavatsky, 2003. "Note on "Small Feedback-based Decisions and Their Limited Correspondence to Description-based Decisions"," CERGE-EI Working Papers wp218, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
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- Ido Erev & Eyal Ert & Alvin E. Roth, 2010. "Erev, I. et al . A Choice Prediction Competition for Market Entry Games: An Introduction. Games 2010, 1 , 117-136," Games, MDPI, Open Access Journal, vol. 1(3), pages 221, July.
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