ArticleScientific reports2017
Network effects on coordination in asymmetric games.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Criminal organizations exhibit hysteresis, resilience, and robustness by balancing security and efficiency.Scientific reports · 2024Article
- Voter-like Dynamics with Conflicting Preferences on Modular Networks.Entropy (Basel, Switzerland) · 2023Article
- Network coevolution drives segregation and enhances Pareto optimal equilibrium selection in coordination games.Scientific reports · 2023Article
- The dual problems of coordination and anti-coordination on random bipartite graphs.New journal of physics · 2021Article
- An experimental study of network effects on coordination in asymmetric games.Scientific reports · 2019Article
- Nash Equilibria in the Response Strategy of Correlated Games.Scientific reports · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Network structure can have an important effect on the behavior of players in an iterated 2 × 2 game. We study the effect of network structure on global and local behavior in asymmetric coordination games using best response dynamics. We find that global behavior is highly dependent on network topology. Random (Erdös-Rényi) networks mostly converge to homogeneous behavior, but the higher the clustering in the network the more heterogeneous the behavior becomes. Behavior within the communities of the network is almost exclusively homogeneous. The findings suggest that clustering of networks facilitates self-organization of uniform behavior within clusters, but heterogeneous behavior between clusters. At the local level we find that some nodes are more important in determining the equilibrium behavior than other nodes. Degree centrality is for most networks the main predictor for the behavior and nodes with an even degree have an advantage over nodes with an uneven degree in dictating the behavior. We conclude that the behavior is difficult to predict for (Erdös-Rényi) networks and that the network imposes the behavior as a function of clustering and degree heterogeneity in other networks.
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Registered trials
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