The Power of Population Effect in Temnothorax Ant House-Hunting: A Computational Modeling Approach
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cmb.2021.0369.pdf
Description
Published version
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938.08 KB
Format
Adobe PDF
Checksum (MD5)
42e71925690fdf6c2fe245be165fbf7c
Author(s)
Zhao, Jiajia
Lynch, Nancy A.
Pratt, Stephen C
Date Issued
2022
Journal
Journal of Computational Biology
Publisher
Mary Ann Liebert Inc
Citation
Zhao, Jiajia, Lynch, Nancy and Pratt, Stephen C. 2022. "The Power of Population Effect in Temnothorax Ant House-Hunting: A Computational Modeling Approach." Journal of Computational Biology, 29 (4).
Version
Final published version
Abstract
The decentralized cognition of animal groups is both a challenging biological problem and a potential basis for bioinspired design. In this study, we investigated the house-hunting algorithm used by emigrating colonies of Temnothorax ants to reach consensus on a new nest. We developed a tractable model that encodes accurate individual behavior rules, and estimated our parameter values by matching simulated behaviors with observed ones on both the individual and group levels. We then used our model to explore a potential, but yet untested, component of the ants' decision algorithm. Specifically, we examined the hypothesis that incorporating site population (the number of adult ants at each potential nest site) into individual perceptions of nest quality can improve emigration performance. Our results showed that attending to site population accelerates emigration and reduces the incidence of split decisions. This result suggests the value of testing empirically whether nest site scouts use site population in this way, in addition to the well-demonstrated quorum rule. We also used our model to make other predictions with varying degrees of empirical support, including the high cognitive capacity of colonies and their rational time investment during decision-making. In addition, we provide a versatile and easy-to-use Python simulator that can be used to explore other hypotheses or make testable predictions. It is our hope that the insights and the modeling tools can inspire further research from both the biology and computer science community.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1089/CMB.2021.0369
