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Contribution Details

Type Journal Article
Scope Discipline-based scholarship
Title When are efficient conventions selected in networks?
Organization Unit
Authors
  • Carlos Alos-Ferrer
  • Johannes Buckenmaier
  • Federica Farolfi
Item Subtype Original Work
Refereed Yes
Status Published in final form
Language
  • English
Journal Title Journal of Economic Dynamics and Control
Publisher Elsevier
Geographical Reach international
ISSN 0165-1889
Volume 124
Page Range 104074
Date 2021
Abstract Text We study the determinants of convergence to efficient conventions in coordination games played on networks, when agents focus on past performance (imitative play). Previous theoretical results provide an incomplete picture and suggest potentially-complex interactions between the features of dynamics and behavior. We conducted an extensive simulation study (with approximately 1.12 million simulations) varying network size, interaction and information radius, the probability of actual interaction, the probability of mistakes, tie-breaking rules, and the process governing revision opportunities. Our main result is that “more interactions,” be it in the form of larger interaction neighborhoods or of a higher interaction probability, lead to less coordination on efficient conventions. A second observation, confirming previous but partial theoretical results, is that a large network size relative to the size of neighborhoods (a “large world”) facilitates convergence to efficient conventions. Third, a larger information neighborhood helps efficiency because it increases visibility of efficient payoffs across the network. Last, technical details of the dynamic specification as tie-breaking or inertia, while often relevant for specific theoretical results, appear to be of little empirical relevance in the larger space of dynamics.
Digital Object Identifier 10.1016/j.jedc.2021.104074
Other Identification Number merlin-id:20760
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Keywords Economics and econometrics, control and optimization, applied mathematics, agent-based models, coordination games, local interactions, learning, networks