Limits...
Structural power and the evolution of collective fairness in social networks

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—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.

No MeSH data available.


Average values of proposals and acceptance values that emerge for different topologies.The average values of the (a) proposals, <p> and (b) acceptance thresholds, <q>, as a function of the threshold M(the fraction of individual acceptances needed to ratify a proposal in MUG), when MUG is played on unstructured populations (well-mixed), on regular rings (regular) or on random networks with homogeneous degree distribution (homogeneous random, horand, generated by swapping the edges initially forming a ring [37, 40, 66]). M has a positive effect on the average values of <p> [22]. Notwithstanding, this effect is much more pronounced in the case of regular networks, where we also witness a similar increase in the average values of <q>. Other parameters: average degree <k> = 6 (meaning that groups have a constant size of N = 7); population size, Z = 1000; mutation rate, μ = 0.001; imitation error, ε = 0.05 and selection strength, β = 10 (see Methods for definitions of all these parameters).
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC5391959&req=5

pone.0175687.g002: Average values of proposals and acceptance values that emerge for different topologies.The average values of the (a) proposals, <p> and (b) acceptance thresholds, <q>, as a function of the threshold M(the fraction of individual acceptances needed to ratify a proposal in MUG), when MUG is played on unstructured populations (well-mixed), on regular rings (regular) or on random networks with homogeneous degree distribution (homogeneous random, horand, generated by swapping the edges initially forming a ring [37, 40, 66]). M has a positive effect on the average values of <p> [22]. Notwithstanding, this effect is much more pronounced in the case of regular networks, where we also witness a similar increase in the average values of <q>. Other parameters: average degree <k> = 6 (meaning that groups have a constant size of N = 7); population size, Z = 1000; mutation rate, μ = 0.001; imitation error, ε = 0.05 and selection strength, β = 10 (see Methods for definitions of all these parameters).

Mentions: We start by simulating MUG on regular rings (regular) [36], and in homogeneous random networks (horand) [37] (see Methods for information regarding the construction and characterization of both networks, together with details of the simulation procedures). As Fig 2 shows, regular networks induce higher fairness and empathy, when compared with homogeneous random networks. Furthermore, there is an increase with M in both <p> and <q>, unlike what is observed for the other 2 classes of networks.


Structural power and the evolution of collective fairness in social networks
Average values of proposals and acceptance values that emerge for different topologies.The average values of the (a) proposals, <p> and (b) acceptance thresholds, <q>, as a function of the threshold M(the fraction of individual acceptances needed to ratify a proposal in MUG), when MUG is played on unstructured populations (well-mixed), on regular rings (regular) or on random networks with homogeneous degree distribution (homogeneous random, horand, generated by swapping the edges initially forming a ring [37, 40, 66]). M has a positive effect on the average values of <p> [22]. Notwithstanding, this effect is much more pronounced in the case of regular networks, where we also witness a similar increase in the average values of <q>. Other parameters: average degree <k> = 6 (meaning that groups have a constant size of N = 7); population size, Z = 1000; mutation rate, μ = 0.001; imitation error, ε = 0.05 and selection strength, β = 10 (see Methods for definitions of all these parameters).
© Copyright Policy
Related In: Results  -  Collection

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

pone.0175687.g002: Average values of proposals and acceptance values that emerge for different topologies.The average values of the (a) proposals, <p> and (b) acceptance thresholds, <q>, as a function of the threshold M(the fraction of individual acceptances needed to ratify a proposal in MUG), when MUG is played on unstructured populations (well-mixed), on regular rings (regular) or on random networks with homogeneous degree distribution (homogeneous random, horand, generated by swapping the edges initially forming a ring [37, 40, 66]). M has a positive effect on the average values of <p> [22]. Notwithstanding, this effect is much more pronounced in the case of regular networks, where we also witness a similar increase in the average values of <q>. Other parameters: average degree <k> = 6 (meaning that groups have a constant size of N = 7); population size, Z = 1000; mutation rate, μ = 0.001; imitation error, ε = 0.05 and selection strength, β = 10 (see Methods for definitions of all these parameters).
Mentions: We start by simulating MUG on regular rings (regular) [36], and in homogeneous random networks (horand) [37] (see Methods for information regarding the construction and characterization of both networks, together with details of the simulation procedures). As Fig 2 shows, regular networks induce higher fairness and empathy, when compared with homogeneous random networks. Furthermore, there is an increase with M in both <p> and <q>, unlike what is observed for the other 2 classes of networks.

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.