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When are efficient conventions selected in networks?


Alós-Ferrer, Carlos; Buckenmaier, Johannes; Farolfi, Federica (2021). When are efficient conventions selected in networks? Journal of Economic Dynamics and Control, 124:104074.

Abstract

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.

Abstract

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.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Economics
Dewey Decimal Classification:330 Economics
Scopus Subject Areas:Social Sciences & Humanities > Economics and Econometrics
Physical Sciences > Control and Optimization
Physical Sciences > Applied Mathematics
Uncontrolled Keywords:Economics and econometrics, control and optimization, applied mathematics, agent-based models, coordination games, local interactions, learning, networks
Language:English
Date:March 2021
Deposited On:15 Feb 2021 11:15
Last Modified:16 Feb 2021 21:01
Publisher:Elsevier
ISSN:0165-1889
OA Status:Hybrid
Publisher DOI:https://doi.org/10.1016/j.jedc.2021.104074

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