Learning in Network Games

We report the findings of an experiment designed to study how people learn and make decisions in network games. Network games offer new opportunities to identify learning rules, since on networks (compared to e.g. random matching) more rules differ in terms of their information requirements. Our experimental design enables us to observe both which actions participants choose and which information they consult before making their choices. We use this information to estimate learning types using maximum likelihood methods. There is substantial heterogeneity in learning types. However, the vast majority of our participants' decisions are best characterized by reinforcement learning or (myopic) best-response learning. The distribution of learning types seems fairly stable across contexts. Neither network topology nor the position of a player in the network seem to substantially affect the estimated distribution of learning types.

[1]  Glenn Ellison.  Learning, Local Interaction, and Coordination , 1993 .

[2]  A. Roth,et al.  Learning in Extensive-Form Games: Experimental Data and Simple Dynamic Models in the Intermediate Term* , 1995 .

[3]  Matthew O. Jackson,et al.  An Overview of Social Networks and Economic Applications , 2011 .

[4]  M. Keane,et al.  Behavior in a dynamic decision problem: An analysis of experimental evidence using a bayesian type classification algorithm , 2004 .

[5]  A. Cameron,et al.  Microeconometrics: Methods and Applications , 2005 .

[6]  C. Nielsen,et al.  Learning to Coordinate by Forward Looking Players , 2000 .

[7]  Debraj Ray,et al.  Evolving Aspirations and Cooperation , 1998 .

[8]  Nathaniel T. Wilcox,et al.  Theories of Learning in Games and Heterogeneity Bias , 2006 .

[9]  Ying-Wong Cheung,et al.  Individual Learning in Games: Some Laboratory Results , 1995 .

[10]  A. Cabrales,et al.  Estimating Learning Models from Experimental Data , 2000 .

[11]  Fang-Fang Tang,et al.  Anticipatory learning in two-person games: some experimental results , 2001 .

[12]  David Gill,et al.  Cognitive Ability, Character Skills, and Learning to Play Equilibrium: A Level-k Analysis , 2014, Journal of Political Economy.

[13]  Reinhard Selten,et al.  Anticipatory Learning in Two-Person Games , 1991 .

[14]  L. Blume.  The Statistical Mechanics of Strategic Interaction , 1993 .

[15]  Colin Camerer,et al.  Experienced-Weighted Attraction Learning in Normal Form Games , 2007 .

[16]  D. Fudenberg,et al.  The Theory of Learning in Games , 1998 .

[17]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[18]  Xin Wang,et al.  Individual Differences in EWA Learning with Partial Payoff Information , 2008 .

[19]  Geoffrey J. McLachlan,et al.  Finite Mixture Models , 2019, Annual Review of Statistics and Its Application.

[20]  M. Kenward,et al.  An Introduction to the Bootstrap , 2007 .

[21]  E. Hopkins.  Two Competing Models of How People Learn in Games (first version) , 1999 .

[22]  Sanjeev Goyal,et al.  Network Formation and Social Coordination , 2003, Games Econ. Behav..

[23]  A. Roth,et al.  Predicting How People Play Games: Reinforcement Learning in Experimental Games with Unique, Mixed Strategy Equilibria , 1998 .

[24]  U. Fischbacher.  z-Tree: Zurich toolbox for ready-made economic experiments , 1999 .

[25]  Carlos Alós-Ferrer,et al.  Contagion and efficiency , 2008, J. Econ. Theory.

[26]  David W Harless,et al.  The predictive utility of generalized expected utility theories , 1994 .

[27]  Dilip Mookherjee,et al.  Learning and Decision Costs in Experimental Constant Sum Games , 1997 .

[28]  Fernando Vega-Redondo,et al.  Complex Social Networks: Searching in Social Networks , 2007 .

[29]  Glenn Ellison,et al.  Rules of Thumb for Social Learning , 1993, Journal of Political Economy.

[30]  M. El-Gamal,et al.  Are People Bayesian? Uncovering Behavioral Strategies , 1995 .

[31]  B. Leroux.  Consistent estimation of a mixing distribution , 1992 .

[32]  Fernando Vega-Redondo,et al.  Economics and the Theory of Games , 2003 .

[33]  Lawrence E. Blume,et al.  Evolutionary Equilibrium with Forward-Looking Players , 1995 .

[34]  Colin F. Camerer,et al.  Studying learning in games using eye-tracking , 2009 .

[35]  Timothy C. Salmon.  An Evaluation of Econometric Models of Adaptive Learning , 2001 .

[36]  K. Schlag.  Why Imitate, and If So, How?, : A Boundedly Rational Approach to Multi-armed Bandits , 1998 .

[37]  Pravin K. Trivedi,et al.  Microeconometrics Using Stata , 2009 .

[38]  Friederike Mengel,et al.  (Anti-) Coordination in Networks , 2009 .

[39]  S. T. Buckland,et al.  An Introduction to the Bootstrap. , 1994 .

[40]  Gary King,et al.  What to Do When Your Hessian is Not Invertible , 2004 .

[41]  B Skyrms,et al.  A dynamic model of social network formation. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[42]  Tilman Börgers,et al.  Learning Through Reinforcement and Replicator Dynamics , 1997 .

[43]  Ayala Arad,et al.  Multi-dimensional iterative reasoning in action: The case of the Colonel Blotto game , 2012 .

[44]  R. Rob,et al.  Learning, Mutation, and Long Run Equilibria in Games , 1993 .

[45]  Daniel A. Hojman,et al.  Endogenous networks, social games, and evolution , 2006, Games Econ. Behav..

[46]  Jörg Oechssler,et al.  Imitation - Theory and Experimental Evidence , 2003, J. Econ. Theory.

[47]  Teck-Hua Ho,et al.  Sophisticated Experience-Weighted Attraction Learning and Strategic Teaching in Repeated Games , 2002, J. Econ. Theory.

[48]  Nick Feltovich,et al.  Reinforcement-based vs. Belief-based Learning Models in Experimental Asymmetric-information Games , 2000 .

[49]  Friederike Mengel,et al.  Learning by (limited) forward looking players , 2014 .

[50]  Mark Walker,et al.  Unobserved heterogeneity and equilibrium: an experimental study of Bayesian and adaptive learning in normal form games , 2004, J. Econ. Theory.

[51]  Richard T. Boylan,et al.  Fictitious Play: A Statistical Study of Multiple Economic Experiments , 1993 .

[52]  Eric J. Johnson,et al.  Detecting Failures of Backward Induction: Monitoring Information Search in Sequential Bargaining , 2002, J. Econ. Theory.

[53]  R. McKelvey,et al.  Quantal Response Equilibria for Normal Form Games , 1995 .

[54]  ปิยดา สมบัติวัฒนา.  Behavioral Game Theory: Experiments in Strategic Interaction , 2013 .

[55]  Miguel A. Costa-Gomes,et al.  Cognition and Behavior in Normal-Form Games: An Experimental Study , 1998 .

[56]  A. Shaked,et al.  Altruists, Egoists and Hooligans in a Local Interaction Model , 1996 .

[57]  A. Kirman.  Whom Or What Does the Representative Individual Represent , 1992 .

[58]  William A. Brock,et al.  A rational route to randomness , 1997 .

[59]  Teck-Hua Ho,et al.  Experience-Weighted Attraction Learning in Coordination Games: Probability Rules, Heterogeneity, and Time-Variation. , 1998, Journal of mathematical psychology.

[60]  Veronika Grimm,et al.  An experiment on learning in a multiple games environment , 2012, J. Econ. Theory.

[61]  Constanza Fosco,et al.  Cooperation Through Imitation and Exclusion In Networks , 2009 .

[62]  Matthew O. Jackson,et al.  On the formation of interaction networks in social coordination games , 2002, Games Econ. Behav..

[63]  Guillaume R. Frechette,et al.  Learning in a multilateral bargaining experiment , 2009 .

[64]  Prasad A. Naik,et al.  Extending the Akaike Information Criterion to Mixture Regression Models , 2007 .

[65]  Rosemarie Nagel,et al.  Naive learning and cooperation in network experiments , 2007, Games Econ. Behav..