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Why understanding multiplex social network structuring processes will help us better understand the evolution of human behavior
Author(s) -
Atkisson Curtis,
Górski Piotr J.,
Jackson Matthew O.,
Hołyst Janusz A.,
D'Souza Raissa M.
Publication year - 2020
Publication title -
evolutionary anthropology: issues, news, and reviews
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.401
H-Index - 85
eISSN - 1520-6505
pISSN - 1060-1538
DOI - 10.1002/evan.21850
Subject(s) - structuring , reciprocal , reciprocity (cultural anthropology) , interdependence , multiplex , computer science , process (computing) , data science , domain (mathematical analysis) , cognitive science , epistemology , sociology , psychology , social psychology , political science , biology , social science , mathematics , philosophy , linguistics , bioinformatics , mathematical analysis , law , operating system
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.