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Related Experiment Video

Updated: Jan 27, 2026

Measurement of Larval Activity in the Drosophila Activity Monitor

Measurement of Larval Activity in the Drosophila Activity Monitor

Published on: April 30, 2015

The active selfish herd.

Shannon Dee Algar¹, Thomas Stemler², Michael Small³

¹The University of Western Australia, Department of Mathematics and Statistics, Crawley, 6009, Australia.

Journal of Theoretical Biology

|March 31, 2019

Summary

This summary is machine-generated.

This study models the selfish herd hypothesis, showing that actively minimizing individual danger domains enhances group cohesion. Optimal aggregation, especially with noise, depends on how individuals sample their environment to reduce risk.

Keywords:

Aggregation Collective behaviour Stochastic agent based model

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Area of Science:

Evolutionary Biology
Behavioral Ecology
Mathematical Modeling

Background:

The selfish herd hypothesis explains group formation as individuals minimizing predation risk.
Existing models often use proxies for danger domain reduction, not direct optimization.
A two-dimensional stochastic model is proposed to actively optimize individual danger domains.

Purpose of the Study:

To develop and test a novel selfish herd model that directly optimizes individual danger domains.
To compare the proposed model's performance against benchmark models (kNN, LCH).
To analyze the impact of noise on group aggregation and individual domain size.

Main Methods:

Simulated a two-dimensional stochastic model with local and global sampling strategies.
Compared the active selfish herd model with k-nearest neighbours (kNN) and local crowded horizon (LCH) models.
Evaluated group cohesion using packing fraction and individual safety using domain size, with and without noise.

Main Results:

The global active selfish herd model achieved the most centralized groups.
Noise improved aggregation across all tested models, particularly benefiting the local active model.
The local active model and LCH showed varying success in forming compact herds, while kNN did not.

Conclusions:

Actively minimizing individual danger domains, aligning with Hamilton's original concept, increases group cohesion.
Model performance and aggregation success are dependent on the presence and level of environmental noise.
The study confirms Hamilton's selfish herd hypothesis predictions, especially regarding noise-influenced dynamics.