Why understanding multiplex social network structuring processes will help us better understand the evolution of human behavior. Issue 3 (16th June 2020)

• Record Type: Journal Article
• Authors: Atkisson, Curtis; Górski, Piotr J.; Jackson, Matthew O.; Hołyst, Janusz A.; D'Souza, Raissa M.
• Is Part Of: Evolutionary anthropology. Volume 29:Issue 3(2020)
• Page Start: 102
• Page End: 107
• Publication Date: 2020-06-16
• Subjects: cooperation -- food sharing -- mathematical model -- multiplex networks -- social networks; Human evolution -- Periodicals; Anthropology -- Periodicals; Physical anthropology -- Periodicals; Human ecology -- Periodicals; Homme -- Évolution -- Périodiques; Anthropologie -- Périodiques; Écologie humaine -- Périodiques; 599.905
• DOI: 10.1002/evan.21850
• Languages: English
• ISSNs: 1060-1538

Abstract

Abstract: Social scientists have long appreciated that relationships between individuals cannot be described from observing a single domain, and that the structure across domains of interaction can have important effects on outcomes of interest (e.g., cooperation; Durkheim, 1893). One debate explicitly about this surrounds food sharing. Some argue that failing to find reciprocal food sharing means that some process other than reciprocity must be occurring, whereas others argue for models that allow reciprocity to span domains in the form of trade (Kaplan and Hill, 1985.). Multilayer networks, high‐dimensional networks that allow us to consider multiple sets of relationships at the same time, are ubiquitous and have consequences, so processes giving rise to them are important social phenomena. The analysis of multi‐dimensional social networks has recently garnered the attention of the network science community (Kivelä et al., 2014). Recent models of these processes show how ignoring layer interdependencies can lead one to miss why a layer formed the way it did, and/or draw erroneous conclusions (Górski et al., 2018). Understanding the structuring processes that underlie multiplex networks will help understand increasingly rich data sets, giving more accurate and complete pictures of social interactions.