Formalising the multidimensional nature of social networks

TL;DR

This paper introduces a multidimensional formal model of social networks driven by individuals' efforts to reduce uncertainty, supported by simulations and baboon data showing small-world properties and adaptive responses to social perturbations.

Contribution

It formalizes social networks as multidimensional objects influenced by individual needs, linking network topology to uncertainty reduction, and demonstrates this with empirical and simulation evidence.

Findings

Social networks can exhibit small-world properties.

Interactions become more predictable after social perturbations.

Network uncertainty decreases following social changes.

Abstract

Individuals interact with conspecifics in a number of behavioural contexts or dimensions. Here, we formalise this by considering a social network between n individuals interacting in b behavioural dimensions as a nxnxb multidimensional object. In addition, we propose that the topology of this object is driven by individual needs to reduce uncertainty about the outcomes of interactions in one or more dimension. The proposal grounds social network dynamics and evolution in individual selection processes and allows us to define the uncertainty of the social network as the joint entropy of its constituent interaction networks. In support of these propositions we use simulations and natural 'knock-outs' in a free-ranging baboon troop to show (i) that such an object can display a small-world state and (ii) that, as predicted, changes in interactions after social perturbations lead to a more certain social network, in which the outcomes of interactions are easier for members to predict. This new formalisation of social networks provides a framework within which to predict network dynamics and evolution under the assumption that it is driven by individuals seeking to reduce the uncertainty of their social environment.