https://scholars.lib.ntu.edu.tw/handle/123456789/556351
標題: | Bounded Memory, Inertia, Sampling and Weighting model for Market Entry Games | 作者: | WEI JAMES CHEN Liu, S.-Y. Chen, C.-H. Lee, Y.-S. |
關鍵字: | Learning; Market entry game; Prediction competition | 公開日期: | 2011 | 卷: | 2 | 期: | 1 | 起(迄)頁: | 187-199 | 來源出版物: | Games | 摘要: | This paper describes the "Bounded Memory, Inertia, Sampling and Weighting" (BI-SAW) model, which won the http://sites.google.com/site/gpredcomp/Market 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. ? 2011 by the authors; licensee MDPI, Basel, Switzerland. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84864299348&doi=10.3390%2fg2010187&partnerID=40&md5=3f5672134d22edd0cb8ef79c55ab9ac7 https://scholars.lib.ntu.edu.tw/handle/123456789/556351 |
ISSN: | 20734336 | DOI: | 10.3390/g2010187 |
顯示於: | 農業經濟學系 |
在 IR 系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。