arXiv subjects

R. I. M. Dunbar

Publications and source records attributed to R. I. M. Dunbar.

7 recordsLinked to original sources

arXiv2025

A Quantitative Model Of Trust as a Predictor of Social Group Sizes and its Implications for Technology

The human capacity for working together and with tools builds on cognitive abilities that, while not unique to humans, are most developed in humans both in scale and plasticity. Our capacity to engage with collaborators and with technology requires a continuous expenditure of attentive work that we show may be understood in terms of what is heuristically argued as`trust' in socio-economic fields. By adopting a `social physics' of information approach, we are able to bring dimensional analysis to bear on an anthropological-economic issue. The cognitive-economic trade-off between group size and rate of attention to detail is the connection between these. This allows humans to scale cooperative effort across groups, from teams to communities, with a trade-off between group size and attention. We show here that an accurate concept of trust follows a bipartite `economy of work' model, and that this leads to correct predictions about the statistical distribution of group sizes in society. Trust is essentially a cognitive-economic issue that depends on the memory cost of past behaviour and on the frequency of attentive policing of intent. All this leads to the characteristic `fractal' structure for human communities. The balance between attraction to some alpha attractor and dispersion due to conflict fully explains data from all relevant sources. The implications of our method suggest a broad applicability beyond purely social groupings to general resource constrained interactions, e.g. in work, technology, cybernetics, and generalized socio-economic systems of all kinds.

physics.soc-ph↗

arXiv2024

Causal evidence for social group sizes from Wikipedia editing data

Human communities have self-organizing properties in which specific Dunbar Numbers may be invoked to explain group attachments. By analyzing Wikipedia editing histories across a wide range of subject pages, we show that there is an emergent coherence in the size of transient groups formed to edit the content of subject texts, with two peaks averaging at around $N=8$ for the size corresponding to maximal contention, and at around $N=4$ as a regular team. These values are consistent with the observed sizes of conversational groups, as well as the hierarchical structuring of Dunbar graphs. We use the Promise Theory model of bipartite trust to derive a scaling law that fits the data and may apply to all group size distributions, when based on attraction to a seeded group process. In addition to providing further evidence that even spontaneous communities of strangers are self-organizing, the results have important implications for the governance of the Wikipedia commons and for the security of all online social platforms and associations.

cs.SI↗

arXiv2024

A Promise Theory Perspective on the Role of Intent in Group Dynamics

We present a simple argument using Promise Theory and dimensional analysis for the Dunbar scaling hierarchy, supported by recent data from group formation in Wikipedia editing. We show how the assumption of a common priority seeds group alignment until the costs associated with attending to the group outweigh the benefits in a detailed balance scenario. Subject to partial efficiency of implementing promised intentions, we can reproduce a series of compatible rates that balance growth with entropy.

cs.SI↗

arXiv2022

The Internet of People: A human and data-centric paradigm for the Next Generation Internet

The cyber-physical convergence, the fast expansion of the Internet at its edge, and tighter interactions between human users and their personal mobile devices push towards a data-centric Internet where the human user becomes more central than ever. We argue that this will profoundly impact primarily on the way data should be handled in the Next Generation Internet. It will require a radical change of the Internet data-management paradigm, from the current platform-centric to a human-centric model. In this paper we present a new paradigm for Internet data management that we name Internet of People (IoP) because it embeds human behavior models in its algorithms. To this end, IoP algorithms exploit quantitative models of the humans' individual and social behavior, from sociology, anthropology, psychology, economics, physics. IoP is not a replacement of the current Internet networking infrastructure, but it exploits legacy Internet services as (reliable) primitives to achieve end-to-end connectivity on a global-scale. In this opinion paper, we first discuss the key features of the IoP paradigm along with the underlying research issues and challenges. Then, we present emerging data-management paradigms that are anticipating IoP.

cs.NI↗

arXiv2018

Multichannel social signatures and persistent features of ego networks

The structure of egocentric networks reflects the way people balance their need for strong, emotionally intense relationships and a diversity of weaker ties. Egocentric network structure can be quantified with 'social signatures', which describe how people distribute their communication effort across the members (alters) of their personal networks. Social signatures based on call data have indicated that people mostly communicate with a few close alters; they also have persistent, distinct signatures. To examine if these results hold for other channels of communication, here we compare social signatures built from call and text message data, and develop a way of constructing mixed social signatures using both channels. We observe that all types of signatures display persistent individual differences that remain stable despite the turnover in individual alters. We also show that call, text, and mixed signatures resemble one another both at the population level and at the level of individuals. The consistency of social signatures across individuals for different channels of communication is surprising because the choice of channel appears to be alter-specific with no clear overall pattern, and ego networks constructed from calls and texts overlap only partially in terms of alters. These results demonstrate individuals vary in how they allocate their communication effort across their personal networks and this variation is persistent over time and across different channels of communication.

physics.soc-ph↗

arXiv2013

The persistence of social signatures in human communication

The social network maintained by a focal individual, or ego, is intrinsically dynamic and typically exhibits some turnover in membership over time as personal circumstances change. However, the consequences of such changes on the distribution of an ego's network ties are not well understood. Here we use a unique 18-month data set that combines mobile phone calls and survey data to track changes in the ego networks and communication patterns of students making the transition from school to university or work. Our analysis reveals that individuals display a distinctive and robust social signature, captured by how interactions are distributed across different alters. Notably, for a given ego, these social signatures tend to persist over time, despite considerable turnover in the identity of alters in the ego network. Thus as new network members are added, some old network members are either replaced or receive fewer calls, preserving the overall distribution of calls across network members. This is likely to reflect the consequences of finite resources such as the time available for communication, the cognitive and emotional effort required to sustain close relationships, and the ability to make emotional investments.

physics.soc-ph↗

arXiv2004

Discrete Hierarchical Organization of Social Group Sizes

The ``social brain hypothesis'' for the evolution of large brains in primates has led to evidence for the coevolution of neocortical size and social group sizes. Extrapolation of these findings to modern humans indicated that the equivalent group size for our species should be approximately 150 (essentially the number of people known personally as individuals). Here, we combine data on human grouping in a comprehensive and systematic study. Using fractal analysis, we identify with high statistical confidence a discrete hierarchy of group sizes with a preferred scaling ratio close to 3: rather than a single or a continuous spectrum of group sizes, humans spontaneously form groups of preferred sizes organized in a geometrical series approximating 3, 9, 27,... Such discrete scale invariance (DSI) could be related to that identified in signatures of herding behavior in financial markets and might reflect a hierarchical processing of social nearness by human brains.

cond-mat.stat-mech↗