Artikel

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.

Sprache
Englisch

Erschienen in
Journal: Games ; ISSN: 2073-4336 ; Volume: 2 ; Year: 2011 ; Issue: 1 ; Pages: 136-162 ; Basel: MDPI

Klassifikation
Wirtschaft
Thema
instance-based learning theory
model comparison
generalization
parsimony

Ereignis
Geistige Schöpfung
(wer)
Gonzalez, Cleotilde
Dutt, Varun
Lejarraga, Tomás
Ereignis
Veröffentlichung
(wer)
MDPI
(wo)
Basel
(wann)
2011

DOI
doi:10.3390/g2010136
Handle
Letzte Aktualisierung
10.03.2025, 11:42 MEZ

Datenpartner

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ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Artikel

Beteiligte

  • Gonzalez, Cleotilde
  • Dutt, Varun
  • Lejarraga, Tomás
  • MDPI

Entstanden

  • 2011

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