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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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Related in: MedlinePlus

Legend for gradient maps.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.
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pone-0020474-g003: Legend for gradient maps.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.

Mentions: Evaluating gradients from Eq. 6 for any network topology and population characteristics leads to gradient maps of predicted population movements. The legend for these maps is described on Figure 3. For any constant value of average degree, the theoretical analysis suggests that the higher the average number of triplets, the more likely a cooperative ESS is to emerge and the bigger the size of its basin of attraction. On the contrary, for a constant average number of triplets, the higher average degree, the less likely a cooperative ESS is to emerge. Moreover, we can numerically calculate the minimum average number of triplets for each average degree in order to have an ESS in the area of norm establishment and compare it with different network topologies (Figure 4).


Axelrod's metanorm games on networks.

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

Legend for gradient maps.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.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0020474-g003: Legend for gradient maps.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.
Mentions: Evaluating gradients from Eq. 6 for any network topology and population characteristics leads to gradient maps of predicted population movements. The legend for these maps is described on Figure 3. For any constant value of average degree, the theoretical analysis suggests that the higher the average number of triplets, the more likely a cooperative ESS is to emerge and the bigger the size of its basin of attraction. On the contrary, for a constant average number of triplets, the higher average degree, the less likely a cooperative ESS is to emerge. Moreover, we can numerically calculate the minimum average number of triplets for each average degree in order to have an ESS in the area of norm establishment and compare it with different network topologies (Figure 4).

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