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Structural power and the evolution of collective fairness in social networks

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ABSTRACT

Mug: From work contracts and group buying platforms to political coalitions and international climate and economical summits, often individuals assemble in groups that must collectively reach decisions that may favor each part unequally. Here we quantify to which extent our network ties promote the evolution of collective fairness in group interactions, modeled by means of Multiplayer Ultimatum Games (). We show that a single topological feature of social networks—which we call structural power—has a profound impact on the tendency of individuals to take decisions that favor each part equally. Increased fair outcomes are attained whenever structural power is high, such that the networks that tie individuals allow them to meet the same partners in different groups, thus providing the opportunity to strongly influence each other. On the other hand, the absence of such close peer-influence relationships dismisses any positive effect created by the network. Interestingly, we show that increasing the structural power of a network leads to the appearance of well-defined modules—as found in human social networks that often exhibit community structure—providing an interaction environment that maximizes collective fairness.

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Intuitive representation of graphs with different average SP.The Fig provides an intuition for the effect of increasing SP in a small network of 100 nodes, while keeping the average degree, <k> = 6, constant. As the SP increases ((a) SP = 0.2, (b) SP = 0.4 and (c) SP = 0.65), different modular sub-structures increasingly appear. The disposition of nodes follows the Force Atlas algorithm [78] and the color scheme represents the detected communities by the Louvain method [78, 79].
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pone.0175687.g005: Intuitive representation of graphs with different average SP.The Fig provides an intuition for the effect of increasing SP in a small network of 100 nodes, while keeping the average degree, <k> = 6, constant. As the SP increases ((a) SP = 0.2, (b) SP = 0.4 and (c) SP = 0.65), different modular sub-structures increasingly appear. The disposition of nodes follows the Force Atlas algorithm [78] and the color scheme represents the detected communities by the Louvain method [78, 79].

Mentions: Fig 5 illustrates the structural effects induced by maximizing the SP of a network, while keeping the average degree <k> constant. Additionally, we concentrate our analysis on sparse structures (<k> << Z), as it is often the case in social networks [41, 42]. When maximizing the SP under these constraints, one witnesses the emergence of highly modular sub-structures, with the concomitant appearance of different communities [43]. In fact, each node acquires high SP by repeatedly appearing in the interaction groups of individuals belonging to the same community, which leads, as a consequence, to a distinguishing characteristic of modular networks: high average SP.


Structural power and the evolution of collective fairness in social networks
Intuitive representation of graphs with different average SP.The Fig provides an intuition for the effect of increasing SP in a small network of 100 nodes, while keeping the average degree, <k> = 6, constant. As the SP increases ((a) SP = 0.2, (b) SP = 0.4 and (c) SP = 0.65), different modular sub-structures increasingly appear. The disposition of nodes follows the Force Atlas algorithm [78] and the color scheme represents the detected communities by the Louvain method [78, 79].
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5391959&req=5

pone.0175687.g005: Intuitive representation of graphs with different average SP.The Fig provides an intuition for the effect of increasing SP in a small network of 100 nodes, while keeping the average degree, <k> = 6, constant. As the SP increases ((a) SP = 0.2, (b) SP = 0.4 and (c) SP = 0.65), different modular sub-structures increasingly appear. The disposition of nodes follows the Force Atlas algorithm [78] and the color scheme represents the detected communities by the Louvain method [78, 79].
Mentions: Fig 5 illustrates the structural effects induced by maximizing the SP of a network, while keeping the average degree <k> constant. Additionally, we concentrate our analysis on sparse structures (<k> << Z), as it is often the case in social networks [41, 42]. When maximizing the SP under these constraints, one witnesses the emergence of highly modular sub-structures, with the concomitant appearance of different communities [43]. In fact, each node acquires high SP by repeatedly appearing in the interaction groups of individuals belonging to the same community, which leads, as a consequence, to a distinguishing characteristic of modular networks: high average SP.

View Article: PubMed Central - PubMed

ABSTRACT

Mug: From work contracts and group buying platforms to political coalitions and international climate and economical summits, often individuals assemble in groups that must collectively reach decisions that may favor each part unequally. Here we quantify to which extent our network ties promote the evolution of collective fairness in group interactions, modeled by means of Multiplayer Ultimatum Games (). We show that a single topological feature of social networks&mdash;which we call structural power&mdash;has a profound impact on the tendency of individuals to take decisions that favor each part equally. Increased fair outcomes are attained whenever structural power is high, such that the networks that tie individuals allow them to meet the same partners in different groups, thus providing the opportunity to strongly influence each other. On the other hand, the absence of such close peer-influence relationships dismisses any positive effect created by the network. Interestingly, we show that increasing the structural power of a network leads to the appearance of well-defined modules&mdash;as found in human social networks that often exhibit community structure&mdash;providing an interaction environment that maximizes collective fairness.

No MeSH data available.


Related in: MedlinePlus