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The HoneyComb Paradigm for Research on Collective Human Behavior
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Memory Transmission in Small Groups and Large Networks: An Agent-Based Model.

Christian C Luhmann1, Suparna Rajaram2

  • 1Department of Psychology, Stony Brook University christian.luhmann@stonybrook.edu.

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Summary

This study models social influence in large networks, finding that individuals are influenced not only by direct contacts but also by indirect connections. This reveals how information spread links to behavioral transmission in complex social systems.

Keywords:
computer simulationmemorysocial influencessocial structure

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Area of Science:

  • Computational Social Science
  • Social Network Analysis
  • Cognitive Psychology

Background:

  • Social influence and information transmission are key in large social networks.
  • Previous research focused on small-scale collaborative memory experiments.
  • Laboratory studies face limitations in studying large-scale network dynamics.

Purpose of the Study:

  • To computationally model social influence spread in large, realistic social networks.
  • To bridge findings from small-group memory experiments to large-scale network behavior.
  • To investigate the mechanisms of information and behavioral transmission beyond direct interactions.

Main Methods:

  • Developed a computational model incorporating theoretical knowledge from small-group memory experiments.
  • Simulated agent interactions and information spread within large-scale, realistic social network structures.
  • Analyzed influence patterns, including direct and indirect (neighbor-of-neighbor) effects.

Main Results:

  • The model successfully replicated foundational findings like collaborative inhibition and memory convergence in small groups.
  • In large networks, agents were influenced by both direct contacts and non-neighbors (neighbors of neighbors).
  • Demonstrated a link between behavioral transmission and the spread of information in complex networks.

Conclusions:

  • Computational modeling offers a scalable approach to studying social influence beyond laboratory constraints.
  • Social influence extends beyond immediate connections, impacting individuals through indirect network pathways.
  • The findings provide a theoretical link between information spread and observed behavioral transmission in large social networks.