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Updated: Oct 20, 2025

The HoneyComb Paradigm for Research on Collective Human Behavior

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

Agent-based neutral competition in two-community networks.

Kota Ishida¹, Beata Oborny², Michael T Gastner¹

¹Division of Science, Yale-NUS College, 01-220 Singapore 138527, Singapore.

Physical Review. E

|September 16, 2021

Summary

This summary is machine-generated.

Neutral competition in networks, influenced by state transmission rates, leads to consensus. A new theory accurately predicts consensus times and state success probabilities even with incomplete network data.

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Area of Science:

Network science
Statistical physics
Mathematical biology

Background:

Neutral competition is a key process in biological and social networks.
It can be modeled as a random drift process on networks where nodes adopt neighbor states.
Reaching a consensus state is the typical outcome.

Purpose of the Study:

To investigate the impact of three limiting factors on neutral competition dynamics.
To analyze how community structure and update rules affect consensus time and state success.
To develop and validate a theoretical framework for predicting consensus in networks.

Main Methods:

Monte Carlo simulations were used to model neutral competition.
Networks with two communities were generated using a stochastic block model.
A heterogeneous mean-field theory was developed and compared with simulation results.

Main Results:

Community structure and update rules significantly influence state success probabilities and consensus time.
The heterogeneous mean-field theory demonstrated strong agreement with simulation outcomes.
The model successfully predicts consensus dynamics even with incomplete network data.

Conclusions:

The study provides a quantitative understanding of neutral competition in structured networks.
The developed theory offers a powerful tool for predicting consensus dynamics in complex systems.
This framework is applicable even when complete network information is unavailable.