The Power of Social Information in Ant-Colony House-Hunting: A Computational Modeling Approach
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BDA2021_House_Hunting_Abstract (002).pdf
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Author(s)
Zhao, Jiajia
Lynch, Nancy A.
Pratt, Stephen C.
Date Issued
July 2021
Publisher
8th Workshop on Biological Distributed Algorithms (BDA), July 2021
Citation
Zhao, Jiajia, Lynch, Nancy and Pratt, Stephen C. 2021. "The Power of Social Information in Ant-Colony House-Hunting: A Computational Modeling Approach."
Version
Author's final manuscript
Abstract
The decentralized cognition of animal groups is both a challenging biological problem and a potential basis for bio-inspired design.
The understanding of these systems and their application can benefit from modeling and analysis of the underlying algorithms.
In this study, we define a modeling framework that can be used to formally represent all components of such algorithms. As an
example application of the framework, we adapt to it the much-studied house-hunting algorithm used by emigrating colonies of
Temnothorax ants to reach consensus on a new nest. We provide a Python simulator that encodes accurate individual behavior
rules and produces simulated behaviors consistent with empirical observations, on both the individual and group levels. Critically,
through multiple simulated experiments, our results highlight the value of individual sensitivity to site population in ensuring
consensus. With the help of this social information, our model successfully reproduces experimental results showing the high
cognitive capacity of colonies and their rational time investment during decision-making, and also predicts the pros and cons of
social information with regard to the colonies’ ability to avoid and repair splits. Additionally, we use the model to make new
predictions about several unstudied aspects of emigration behavior. Our results indicate a more complex relationship between
individual behavior and the speed/accuracy trade-o than previously appreciated. The model proved relatively weak at resolving
colony divisions among multiple sites, suggesting either limits to the ants’ ability to reach consensus, or an aspect of their behavior
not captured in our model.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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DOI of Published Version
https://www.navlakhalab.net/BDA/2021/
