RT Dissertation/Thesis
T1 Ego-centred models of social networks: the social atom
A1 Tamarit, Ignacio
AB This thesis set out to contribute to the realm of social physics, with a particularfocus on human social networks. Our approach, however, is somewhatdierent from what is typical in disciplines such as complex systems or statisticalphysics. Rather than simplifying the features of the constituents ofour system (people), and stressing their rules of interaction, we focus onbetter understanding those very same constituents, modelling them as socialatoms. Our rationale is that a better understanding of such an atommay shed light on how (and why) it interacts with other atoms to formsocial collectives.Given its robustness and the evolutionary roots of its premises, we usethe Social Brain Hypothesis as our departure point. This theory states thatthe evolutionary drive behind the development of large brains in humanswas the need to process social information and that the limited capacity ofour brains imposes a limit to the number of relationships we can manage—the so-called “Dunbar’s number”, roughly 150. Moreover, evidence keepsrevealing that these relationships are further organised in a series of hierarchicallyinclusive layers with decreasing emotional intensity, whose sizesexhibit a more or less constant scaling. Notwithstanding the empirical evidence,neither the presence of scaling in the organisation of personal networksnor its connection with limited cognitive skills had been explainedso far.In Chapter 2 we present a mathematical model that solves this puzzle.The assumptions of the model are quite simple, and well founded on empiricalevidence. Firstly, the number of relationships we maintain tendsto be stable on average. Secondly, these relationships are costly, and our resources are limited. With these two premises, our results show that thehierarchical organisation emerges naturally from the principle of maximumentropy. Not only that, but we also predict a hitherto unnoticed regime oforganisation whose existence we prove using several datasets from communitiesof immigrants.The former model considers that relationships can only belong to adiscrete set of categories (layers). In Chapter 3 we extend it so that relationshipsare classified in a continuum. This modification allows us to testthe model with data from very dierent sources such as online communications,face-to-face contacts, and phone calls. Our results show that the tworegimes of organisation found in the previous model persist in this variant,and reveal the underlying existence of a (universal) scaling parameterwhich does not depend on any particular number of layers.To incorporate these ideas into socio-centric models, we build on theso-called Structural Balance Theory. This theory, underpinned by psychologicalmotivations, posits that the structure of social networks of positiveand negative relationships are highly interdependent. However, the theoryhas received little empirical validation, and negative social relationshipsare poorly understood—both from an ego-centric and a socio-centric perspective.For that reason, we turn to developing an experimental softwarein order to gather data within a school.In Chapters 4 and 5 we present results from these experiments. InChapter 4 we analyse the socio-centric networks using machine learningtechniques and find that the structure of positive and negative networksis indeed very much connected. Besides, we study the two types of networksseparately, showing that they exhibit quite distinct features and thatgender eects in negative social networks are weak and asymmetrical forboys and girls. In Chapter 5, on the other hand, we focus on the structureof negative personal networks. Remarkably, using data from two dierentexperimental settings, we show that the structure of personal networksof negative relationships mirrors that of the positive ones and exhibits asimilar scaling—albeit their size is significantly smaller.Chapter 6 summarises our results and presents future (and current) linesof investigation. Among them, we outline a model of a social fluid thatuses the insights gained with this thesis to build a model of social collectivesas ensembles of personal networks. This model is compatible, at the micro-level, with the observations of the social brain hypothesis, and, atthe macro-level, with the premises of the structural balance theory.
YR 2019
FD 2019-05-30
LK https://hdl.handle.net/10016/28871
UL https://hdl.handle.net/10016/28871
LA eng
NO Mención Internacional en el título de doctor
NO This thesis would not have been possible without the support of Fundación BBVA through its 2016 call project ”Los números de Dunbar y la estructura de las sociedades digitales: modelización y simulación (DUNDIG)”, and we are very thankful for it. Support for early stages of this work through projects IBSEN (European Commission, H2020 FET Open RIA 662725) and VARIANCE (Ministerio de Economía y Competitividad/FEDER, project no. FIS2015-64349-P) is also acknowledged
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RD 16 may. 2024