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Games 2011, 2(1), 187-199; doi:10.3390/g2010187

Bounded Memory, Inertia, Sampling and Weighting Model for Market Entry Games

Department of Economics, National Taiwan University, No.21 Hsu-Chow Rd., Taipei, Taiwan
Author to whom correspondence should be addressed.
Received: 3 January 2011 / Revised: 8 March 2011 / Accepted: 16 March 2011 / Published: 21 March 2011
(This article belongs to the Special Issue Predicting Behavior in Games)
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This paper describes the “Bounded Memory, Inertia, Sampling and Weighting” (BI-SAW) model, which won the Entry Prediction Competition in 2010. The BI-SAW model refines the I-SAW Model (Erev et al. [1]) by adding the assumption of limited memory span. In particular, we assume when players draw a small sample to weight against the average payoff of all past experience, they can only recall 6 trials of past experience. On the other hand, we keep all other key features of the I-SAW model: (1) Reliance on a small sample of past experiences, (2) Strong inertia and recency effects, and (3) Surprise triggers change. We estimate this model using the first set of experimental results run by the competition organizers, and use it to predict results of a second set of similar experiments later ran by the organizers. We find significant improvement in out-of-sample predictability (against the I-SAW model) in terms of smaller mean normalized MSD, and such result is robust to resampling the predicted game set and reversing the role of the sets of experimental results. Our model’s performance is the best among all the participants.
Keywords: learning; market entry game; prediction competition learning; market entry game; prediction competition
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Chen, W.; Liu, S.-Y.; Chen, C.-H.; Lee, Y.-S. Bounded Memory, Inertia, Sampling and Weighting Model for Market Entry Games. Games 2011, 2, 187-199.

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