http://www.mdpi.com/journal/games/special_issues/predict-behavior/ {snippet name="submission_info"} http://www.mdpi.com/2073-4336/2/3/257/ Two independent, but related, choice prediction competitions are organized that focus on behavior in simple two-person extensive form games (http://sites.google.com/site/extformpredcomp/): one focuses on predicting the choices of the first mover and the other on predicting the choices of the second mover. The competitions are based on 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 some baseline models. The best baseline model assumes that each choice is made based on one of several rules. The rules include: rational choice, level-1 reasoning, an attempt to maximize joint payoff, and an attempt to increase fairness. The probability of using the different rules is assumed to be stable over games. The estimated parameters imply that the most popular rule is rational choice; it is used in about half the cases. To participate in the competitions, researchers are asked to email the organizers models (implemented in computer programs) that read the incentive structure as input, and derive the predicted behavior as an output. The submission deadline is 1 December 2011, the results of the competition experiment will not be revealed until that date. The submitted models will be ranked based on their prediction error. The winners of the competitions will be invited to write a paper that describes their model. http://www.mdpi.com/2073-4336/2/3/257/ Mon, 25 Jul 2011 00:00:00 CEST Games 2011-07-25 2 3 Article 257 276 2073-4336 A Choice Prediction Competition for Social Preferences in Simple Extensive Form Games: An Introduction 2011-07-25 doi: 10.3390/g2030257 Eyal Ert Ido Erev Alvin E. Roth http://www.mdpi.com/2073-4336/2/2/200/ We submitted three models to the competition which were based on the I-SAW model. The models introduced four new assumptions. In the first model an adjustment process was introduced through which the tendency for exploration was higher at the beginning and decreased over time in the exploration stage. Another new assumption was that surprise as a factor influencing the weight of a trial in the sampling procedure was added. In the second model we added the possibility of an exclusion of unreliable experiences gained in the early trials of a game and the possibility of a revision of a reasonable alternative which was responsible for a very bad outcome in the previous trial. Three of the four added assumptions were combined in the third model. Because each of our models contains at least two new assumptions, we estimated the relative effect of each assumption on the estimation and prediction scores and carried out a test of robustness. In this way, we were able to clarify the usefulness of each added assumption. http://www.mdpi.com/2073-4336/2/2/200/ Tue, 12 Apr 2011 00:00:00 CEST Games 2011-04-12 2 2 Commentary 200 208 2073-4336 Market Entry Prediction Competition 2010 2011-04-12 doi: 10.3390/g2020200 Wasilios Hariskos Johannes Leder Kinneret Teodorescu http://www.mdpi.com/2073-4336/2/1/187/ 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. http://www.mdpi.com/2073-4336/2/1/187/ Mon, 21 Mar 2011 00:00:00 CET Games 2011-03-21 2 1 Article 187 199 2073-4336 Bounded Memory, Inertia, Sampling and Weighting Model for Market Entry Games 2011-03-21 doi: 10.3390/g2010187 Wei Chen Shu-Yu Liu Chih-Han Chen Yi-Shan Lee http://www.mdpi.com/2073-4336/2/1/136/ 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. http://www.mdpi.com/2073-4336/2/1/136/ Wed, 16 Mar 2011 00:00:00 CET Games 2011-03-16 2 1 Article 136 162 2073-4336 A Loser Can Be a Winner: Comparison of Two Instance-based Learning Models in a Market Entry Competition 2011-03-16 doi: 10.3390/g2010136 Cleotilde Gonzalez Varun Dutt Tomás Lejarraga http://www.mdpi.com/2073-4336/2/1/21/ The Intergroup Prisoner’s Dilemma with Intragroup Power Dynamics (IPD^2) is a new game paradigm for studying human behavior in conflict situations. IPD^2 adds the concept of intragroup power to an intergroup version of the standard Repeated Prisoner’s Dilemma game. We conducted a laboratory study in which individual human participants played the game against computer strategies of various complexities. The results show that participants tend to cooperate more when they have greater power status within their groups. IPD^2 yields increasing levels of mutual cooperation and decreasing levels of mutual defection, in contrast to a variant of Intergroup Prisoner’s Dilemma without intragroup power dynamics where mutual cooperation and mutual defection are equally likely. We developed a cognitive model of human decision making in this game inspired by the Instance-Based Learning Theory (IBLT) and implemented within the ACT-R cognitive architecture. This model was run in place of a human participant using the same paradigm as the human study. The results from the model show a pattern of behavior similar to that of human data. We conclude with a discussion of the ways in which the IPD^2 paradigm can be applied to studying human behavior in conflict situations. In particular, we present the current study as a possible contribution to corroborating the conjecture that democracy reduces the risk of wars. http://www.mdpi.com/2073-4336/2/1/21/ Tue, 08 Feb 2011 00:00:00 CET Games 2011-02-08 2 1 Article 21 51 2073-4336 Intergroup Prisoner’s Dilemma with Intragroup Power Dynamics 2011-02-08 doi: 10.3390/g2010021 Ion Juvina Christian Lebiere Jolie M. Martin Cleotilde Gonzalez http://www.mdpi.com/2073-4336/2/1/16/ This note briefly summarizes the consequences of adding correlated individual differences to the best baseline model in the Games competition, I-SAW. I find evidence that the traits of an individual are correlated, but refining I-SAW to capture these correlations does not significantly improve the model’s accuracy when predicting average behavior. http://www.mdpi.com/2073-4336/2/1/16/ Mon, 07 Feb 2011 00:00:00 CET Games 2011-02-07 2 1 Short Note 16 20 2073-4336 Correlated Individual Differences and Choice Prediction 2011-02-07 doi: 10.3390/g2010016 Luke Lindsay http://www.mdpi.com/2073-4336/1/3/221/ Ion Juvina found an error in our manuscript published in Games. [...] http://www.mdpi.com/2073-4336/1/3/221/ Wed, 21 Jul 2010 00:00:00 CEST Games 2010-07-21 1 3 Correction 221 225 2073-4336 Erev, I. et al. A Choice Prediction Competition for Market Entry Games: An Introduction. Games 2010, 1, 117-136 2010-07-21 doi: 10.3390/g1030221 Ido Erev Eyal Ert Alvin E. Roth http://www.mdpi.com/2073-4336/1/2/117/ A 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. http://www.mdpi.com/2073-4336/1/2/117/ Fri, 14 May 2010 00:00:00 CEST Games 2010-05-14 1 2 Article 117 136 2073-4336 A Choice Prediction Competition for Market Entry Games: An Introduction 2010-05-14 doi: 10.3390/g1020117 Erev Ert Roth