- freely available
A Choice Prediction Competition for Market Entry Games: An Introduction
AbstractA choice prediction competition is organized that focuses on decisions from experience in market entry games (http://sites.google.com/site/gpredcomp/ and http://www.mdpi.com/si/games/predict-behavior/). The competition is based on two experiments: An estimation experiment, and a competition experiment. The two experiments use the same methods and subject pool, and examine games randomly selected from the same distribution. The current introductory paper presents the results of the estimation experiment, and clarifies the descriptive value of several baseline models. The experimental results reveal the robustness of eight behavioral tendencies that were documented in previous studies of market entry games and individual decisions from experience. The best baseline model (I-SAW) assumes reliance on small samples of experiences, and strong inertia when the recent results are not surprising. The competition experiment will be run in May 2010 (after the completion of this introduction), but they will not be revealed until September. To participate in the competition, researchers are asked to E-mail the organizers models (implemented in computer programs) that read the incentive structure as input, and derive the predicted behavior as an output. The submitted models will be ranked based on their prediction error. The winners of the competition will be invited to publish a paper that describes their model.
Share & Cite This Article
Erev, I.; Ert, E.; Roth, A.E. A Choice Prediction Competition for Market Entry Games: An Introduction. Games 2010, 1, 117-136.View more citation formats
Erev I, Ert E, Roth AE. A Choice Prediction Competition for Market Entry Games: An Introduction. Games. 2010; 1(2):117-136.Chicago/Turabian Style
Erev, Ido; Ert, Eyal; Roth, Alvin E. 2010. "A Choice Prediction Competition for Market Entry Games: An Introduction." Games 1, no. 2: 117-136.
Notes: Multiple requests from the same IP address are counted as one view.