Next Article in Journal
A Scent of Lemon—Seller Meets Buyer with a Noisy Quality Observation
Next Article in Special Issue
Bounded Memory, Inertia, Sampling and Weighting Model for Market Entry Games
Previous Article in Journal
Do I Really Want to Know? A Cognitive Dissonance-Based Explanation of Other-Regarding Behavior
Previous Article in Special Issue
Intergroup Prisoner’s Dilemma with Intragroup Power Dynamics
Article Menu

Export Article

Open AccessArticle
Games 2011, 2(1), 136-162;

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

Dynamic Decision Making Laboratory, Carnegie Mellon University, Pittsburgh, PA 15213, USA
Author to whom correspondence should be addressed.
Received: 21 December 2010 / Revised: 1 March 2011 / Accepted: 14 March 2011 / Published: 16 March 2011
(This article belongs to the Special Issue Predicting Behavior in Games)
Full-Text   |   PDF [163 KB, uploaded 16 March 2011]   |  


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. View Full-Text
Keywords: instance-based learning theory; model comparison; generalization; parsimony instance-based learning theory; model comparison; generalization; parsimony

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

Share & Cite This Article

MDPI and ACS Style

Gonzalez, C.; Dutt, V.; Lejarraga, T. A Loser Can Be a Winner: Comparison of Two Instance-based Learning Models in a Market Entry Competition. Games 2011, 2, 136-162.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Games EISSN 2073-4336 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top