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

TL;DR

This paper introduces a quantitative model of trust as a key factor influencing social group sizes, using social physics and dimensional analysis to predict community structures and their implications for technology and resource management.

Contribution

It presents a novel bipartite 'economy of work' model of trust that accurately predicts social group size distributions and explains community fractal structures.

Findings

Trust depends on memory cost of past behavior and attentive policing.

The model predicts the statistical distribution of social group sizes.

Community structures follow a fractal pattern explained by trust dynamics.

Abstract

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.