a loser can be a winner comparison of two instance-based learning models in a market entry competition一个失败者可以成为赢家的比较两个基于实例的学习模型在市场准入竞争.pdfVIP

Games 2011, 2, 136-162; doi:10.3390/g2010136 OPEN ACCESS games ISSN 2073-4336 /journal/games Article A Loser Can Be a Winner: Comparison of Two Instance-based Learning Models in a Market Entry Competition Cleotilde Gonzalez *, Varun Dutt and Tomás Lejarraga Dynamic Decision Making Laboratory, Carnegie Mellon University, Pittsburgh, PA 15213, USA; E-Mails: vdutt@ (V.D.); tleja@ (T.L.) * Author to whom correspondence should be addressed; E-Mail: coty@. Received: 21 December 2010; in revised form: 1 March 2011 / Accepted: 14 March 2011 / Published: 16 March 2011 Abstract: 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-d