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Functional Near-Infrared Spectroscopy Hyperscanning Study in Psychological Counseling
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Trust based attachment.

Julian Kates-Harbeck1, Martin Nowak2,3

  • 1Department of Physics, Harvard University, Cambridge, MA, United States of America.

Plos One
|August 23, 2023
PubMed
Summary
This summary is machine-generated.

A larger audience for social interactions increases reputational stakes, incentivizing cooperation. This concept underpins trust based attachment (TBA), a new algorithm for generating realistic social networks.

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Area of Science:

  • Social network analysis
  • Computational sociology
  • Game theory

Background:

  • Indirect reciprocity relies on reputation for positive future interactions.
  • Gossip flow influences reputation spread, posing a percolation problem.
  • Reputational stakes incentivize prosocial behavior and increase trust.

Purpose of the Study:

  • To quantify the impact of information spread on social interactions.
  • To introduce a novel algorithm for social network generation.
  • To provide a new theoretical basis for social tie formation.

Main Methods:

  • Calculating the expected audience size for social interactions.
  • Developing the trust based attachment (TBA) algorithm.
  • Comparing TBA-generated networks to real-world social graphs.

Main Results:

  • TBA generates networks with high clustering, small-world properties, and power-law distributions.
  • TBA's mechanisms can be approximated by friend-of-friend routines.
  • Trust, gossip, and information spread are identified as key factors in social tie formation.

Conclusions:

  • Trust based attachment offers a novel framework for understanding social network evolution.
  • The study provides a new justification for triadic closure in social contexts.
  • Information spread dynamics significantly influence human social behavior and network structure.