{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T13:20:56Z","timestamp":1765545656430,"version":"build-2065373602"},"reference-count":8,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2011,3,21]],"date-time":"2011-03-21T00:00:00Z","timestamp":1300665600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Games"],"abstract":"<jats:p>This paper describes the \u201cBounded Memory, Inertia, Sampling and Weighting\u201d (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\u2019s performance is the best among all the participants.<\/jats:p>","DOI":"10.3390\/g2010187","type":"journal-article","created":{"date-parts":[[2011,3,25]],"date-time":"2011-03-25T11:14:25Z","timestamp":1301051665000},"page":"187-199","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Bounded Memory, Inertia, Sampling and Weighting Model for Market Entry Games"],"prefix":"10.3390","volume":"2","author":[{"given":"Wei","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Economics, National Taiwan University, No.21 Hsu-Chow Rd., Taipei, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shu-Yu","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Economics, National Taiwan University, No.21 Hsu-Chow Rd., Taipei, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chih-Han","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Economics, National Taiwan University, No.21 Hsu-Chow Rd., Taipei, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi-Shan","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Economics, National Taiwan University, No.21 Hsu-Chow Rd., Taipei, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2011,3,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"117","DOI":"10.3390\/g1020117","article-title":"A choice prediction competition for market entry games: An introduction","volume":"1","author":"Erev","year":"2010","journal-title":"Games"},{"key":"ref_2","unstructured":"Camerer, C.F. (2003). Behavioral Game Theory: Experiments on Strategic Interaction, Princeton University Press."},{"key":"ref_3","first-page":"848","article-title":"Predicting how people play games: Reinforcement learning in experimental games with unique, mixed strategy equilibria","volume":"88","author":"Erev","year":"1998","journal-title":"Amer. Econ. Rev."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"827","DOI":"10.1111\/1468-0262.00054","article-title":"Experience-weighted attraction learning in normal form games","volume":"67","author":"Camerer","year":"1999","journal-title":"Econometrica"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1597","DOI":"10.1111\/1468-0262.00258","article-title":"An evaluation of econometric models of adaptive learning","volume":"69","author":"Salmon","year":"2001","journal-title":"Econometrica"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1002\/bdm.683","article-title":"A choice prediction competition: Choices from experience and from description","volume":"23","author":"Erev","year":"2010","journal-title":"J. Behav. Decis. Making"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"99","DOI":"10.2307\/1884852","article-title":"A behavioral model of rational choice","volume":"69","author":"Simon","year":"1955","journal-title":"Quart. J. Econ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1037\/h0043158","article-title":"The magical number seven, plus or minus two: Some limits on our capacity for processing information","volume":"63","author":"Miller","year":"1956","journal-title":"Psychol. Rev."}],"container-title":["Games"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-4336\/2\/1\/187\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T21:55:35Z","timestamp":1760219735000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-4336\/2\/1\/187"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2011,3,21]]},"references-count":8,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2011,3]]}},"alternative-id":["g2010187"],"URL":"https:\/\/doi.org\/10.3390\/g2010187","relation":{},"ISSN":["2073-4336"],"issn-type":[{"type":"electronic","value":"2073-4336"}],"subject":[],"published":{"date-parts":[[2011,3,21]]}}}