TY - JOUR
T1 - When are efficient conventions selected in networks?
AU - Alós-Ferrer, Carlos
AU - Buckenmaier, Johannes
AU - Farolfi, Federica
N1 - Publisher Copyright:
© 2021 The Author(s)
PY - 2021/3
Y1 - 2021/3
N2 - 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.
AB - 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.
KW - Agent-based models
KW - Coordination games
KW - Learning
KW - Local interactions
KW - Networks
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U2 - 10.1016/j.jedc.2021.104074
DO - 10.1016/j.jedc.2021.104074
M3 - Article
AN - SCOPUS:85100395321
SN - 0165-1889
VL - 124
JO - Journal of Economic Dynamics and Control
JF - Journal of Economic Dynamics and Control
M1 - 104074
ER -