Replication data for: When Does Learning in Games Generate Convergence to Nash Equilibria? The Role of Supermodularity in an Experimental Setting

Principal Investigator(s):  View help for Principal Investigator(s) Yan Chen; Robert Gazzale

Version:  View help for Version V1

Name File Type Size Last Modified
LICENSE.txt text/plain 14.6 KB 12/06/2019 10:22:AM
chen_gazzale_data.txt text/plain 471.3 KB 12/06/2019 10:22:AM
chen_gazzale_data_readme.pdf application/pdf 7 KB 12/06/2019 10:22:AM

Project Citation: 

Chen, Yan, and Gazzale, Robert. Replication data for: When Does Learning in Games Generate Convergence to Nash Equilibria? The Role of Supermodularity in an Experimental Setting. Nashville, TN: American Economic Association [publisher], 2004. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2019-12-06. https://doi.org/10.3886/E116032V1

Project Description

Summary:  View help for Summary This study clarifies the conditions under which learning in games produces convergence to Nash equilibria in practice. We experimentally investigate the role of supermodularity, which is closely related to the more familiar concept of strategic complementarities, in achieving convergence through learning. Using a game from the literature on solutions to externalities, we find that supermodular and "near-supermodular" games converge significantly better than those far below the threshold of supermodularity. From a little below the threshold to the threshold, the improvement is statistically insignificant. Increasing the parameter far beyond the threshold does not significantly improve convergence.

Scope of Project

JEL Classification:  View help for JEL Classification
      C72 Noncooperative Games
      D83 Search; Learning; Information and Knowledge; Communication; Belief; Unawareness


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V1 [2019-12-06]

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