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Imitation, network size, and efficiency

Published online by Cambridge University Press:  04 December 2020

Carlos Alós-Ferrer Open the ORCID record for Carlos Alós-Ferrer [Opens in a new window] ,
Johannes Buckenmaier Open the ORCID record for Johannes Buckenmaier [Opens in a new window]  and
Federica Farolfi
Carlos Alós-Ferrer*
Affiliation:
Zurich Center for Neuroeconomics, Department of Economics, University of Zurich, Zürich, Switzerland (email: johannes.buckenmaier@econ.uzh.ch)
Johannes Buckenmaier
Affiliation:
Zurich Center for Neuroeconomics, Department of Economics, University of Zurich, Zürich, Switzerland (email: johannes.buckenmaier@econ.uzh.ch)
Federica Farolfi
Affiliation:
CREED, Amsterdam School of Economics, Universiteit van Amsterdam, Amsterdam, the Netherlands (email: f.farolfi@uva.nl)
*
*Corresponding author. Email: carlos.alos-ferrer@econ.uzh.ch
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Abstract

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A number of theoretical results have provided sufficient conditions for the selection of payoff-efficient equilibria in games played on networks when agents imitate successful neighbors and make occasional mistakes (stochastic stability). However, those results only guarantee full convergence in the long-run, which might be too restrictive in reality. Here, we employ a more gradual approach relying on agent-based simulations avoiding the double limit underlying these analytical results. We focus on the circular-city model, for which a sufficient condition on the population size relative to the neighborhood size was identified by Alós-Ferrer & Weidenholzer [(2006) Economics Letters, 93, 163–168]. Using more than 100,000 agent-based simulations, we find that selection of the efficient equilibrium prevails also for a large set of parameters violating the previously identified condition. Interestingly, the extent to which efficiency obtains decreases gradually as one moves away from the boundary of this condition.

Keywords

agent-based modelspareto efficiencyrisk dominanceimitationnetworksstochastic stability

Information

Type
Research Article
Information
Network Science , Volume 9 , Issue 1 , March 2021 , pp. 123 - 133
Copyright
© The Author(s), 2020. Published by Cambridge University Press

Footnotes

Action Editor: Fernando Vega-Redondo

References

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