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Axelrod's metanorm games on networks.

Galán JM, Łatek MM, Rizi SM - PLoS ONE (2011)

Bottom Line: Recent experimental results show that network structures that underlie social interactions influence the emergence of norms that promote cooperation.Network topology strongly influences the effectiveness of the metanorms mechanism in establishing cooperation.In particular, we find that average degree, clustering coefficient and the average number of triplets per node play key roles in sustaining or collapsing cooperation.

View Article: PubMed Central - PubMed

Affiliation: Área de Organización de Empresas, Departamento de Ingeniería Civil, Universidad de Burgos, Burgos, Spain. jmgalan@ubu.es

ABSTRACT
Metanorms is a mechanism proposed to promote cooperation in social dilemmas. Recent experimental results show that network structures that underlie social interactions influence the emergence of norms that promote cooperation. We generalize Axelrod's analysis of metanorms dynamics to interactions unfolding on networks through simulation and mathematical modeling. Network topology strongly influences the effectiveness of the metanorms mechanism in establishing cooperation. In particular, we find that average degree, clustering coefficient and the average number of triplets per node play key roles in sustaining or collapsing cooperation.

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Predicted versus observed dynamics of the metanorms game for three networks.For simulated results, the mutation rate was set to 0.01. Color codes the speeds of movement of the population, either computed analytically or measured from the simulation with blue being the slowest and red the fastest. Figure 3 contains the legend for the graphs. In panel B, simulated population spends 95% of time in norm collapse zone. In panel D, the proportion of time in the norm collapse zone drops to 50%. In panel E, the simulation spends 95% of time in the norm emergence zone and 5% in the norm collapse zone.
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pone-0020474-g007: Predicted versus observed dynamics of the metanorms game for three networks.For simulated results, the mutation rate was set to 0.01. Color codes the speeds of movement of the population, either computed analytically or measured from the simulation with blue being the slowest and red the fastest. Figure 3 contains the legend for the graphs. In panel B, simulated population spends 95% of time in norm collapse zone. In panel D, the proportion of time in the norm collapse zone drops to 50%. In panel E, the simulation spends 95% of time in the norm emergence zone and 5% in the norm collapse zone.

Mentions: The map applies to both analytic and simulated gradient landscapes. The axes represent the average boldness and vengefulness of the population as its strategic characteristics. For each point, we measure the direction and speed of population drift. For analytical landscapes, we will also pinpoint the expected location of the evolutionary stable states. For simulation landscapes, we will be measuring the time that the simulation spends in each of the two key regions: norm emergence and norm collapse zones. Sample maps for different network topologies are shown in Figure 7.


Axelrod's metanorm games on networks.

Galán JM, Łatek MM, Rizi SM - PLoS ONE (2011)

Predicted versus observed dynamics of the metanorms game for three networks.For simulated results, the mutation rate was set to 0.01. Color codes the speeds of movement of the population, either computed analytically or measured from the simulation with blue being the slowest and red the fastest. Figure 3 contains the legend for the graphs. In panel B, simulated population spends 95% of time in norm collapse zone. In panel D, the proportion of time in the norm collapse zone drops to 50%. In panel E, the simulation spends 95% of time in the norm emergence zone and 5% in the norm collapse zone.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3105066&req=5

pone-0020474-g007: Predicted versus observed dynamics of the metanorms game for three networks.For simulated results, the mutation rate was set to 0.01. Color codes the speeds of movement of the population, either computed analytically or measured from the simulation with blue being the slowest and red the fastest. Figure 3 contains the legend for the graphs. In panel B, simulated population spends 95% of time in norm collapse zone. In panel D, the proportion of time in the norm collapse zone drops to 50%. In panel E, the simulation spends 95% of time in the norm emergence zone and 5% in the norm collapse zone.
Mentions: The map applies to both analytic and simulated gradient landscapes. The axes represent the average boldness and vengefulness of the population as its strategic characteristics. For each point, we measure the direction and speed of population drift. For analytical landscapes, we will also pinpoint the expected location of the evolutionary stable states. For simulation landscapes, we will be measuring the time that the simulation spends in each of the two key regions: norm emergence and norm collapse zones. Sample maps for different network topologies are shown in Figure 7.

Bottom Line: Recent experimental results show that network structures that underlie social interactions influence the emergence of norms that promote cooperation.Network topology strongly influences the effectiveness of the metanorms mechanism in establishing cooperation.In particular, we find that average degree, clustering coefficient and the average number of triplets per node play key roles in sustaining or collapsing cooperation.

View Article: PubMed Central - PubMed

Affiliation: Área de Organización de Empresas, Departamento de Ingeniería Civil, Universidad de Burgos, Burgos, Spain. jmgalan@ubu.es

ABSTRACT
Metanorms is a mechanism proposed to promote cooperation in social dilemmas. Recent experimental results show that network structures that underlie social interactions influence the emergence of norms that promote cooperation. We generalize Axelrod's analysis of metanorms dynamics to interactions unfolding on networks through simulation and mathematical modeling. Network topology strongly influences the effectiveness of the metanorms mechanism in establishing cooperation. In particular, we find that average degree, clustering coefficient and the average number of triplets per node play key roles in sustaining or collapsing cooperation.

Show MeSH
Related in: MedlinePlus