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Bayesian Inference for Identifying Interaction Rules in Moving Animal Groups
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics, Analysis and Applied Mathematics.
2011 (English)In: PLoS ONE, ISSN 1932-6203, Vol. 6, no 8, e22827- p.Article in journal (Refereed) Published
Abstract [en]

The emergence of similar collective patterns from different self-propelled particle models of animal groups points to a restricted set of "universal'' classes for these patterns. While universality is interesting, it is often the fine details of animal interactions that are of biological importance. Universality thus presents a challenge to inferring such interactions from macroscopic group dynamics since these can be consistent with many underlying interaction models. We present a Bayesian framework for learning animal interaction rules from fine scale recordings of animal movements in swarms. We apply these techniques to the inverse problem of inferring interaction rules from simulation models, showing that parameters can often be inferred from a small number of observations. Our methodology allows us to quantify our confidence in parameter fitting. For example, we show that attraction and alignment terms can be reliably estimated when animals are milling in a torus shape, while interaction radius cannot be reliably measured in such a situation. We assess the importance of rate of data collection and show how to test different models, such as topological and metric neighbourhood models. Taken together our results both inform the design of experiments on animal interactions and suggest how these data should be best analysed.

Place, publisher, year, edition, pages
2011. Vol. 6, no 8, e22827- p.
Keyword [en]
Collective behavior, Decision-making, Leadership, Information, Particles, Tracking, Pigeons, System, Flocks
National Category
Biological Sciences
URN: urn:nbn:se:uu:diva-158169DOI: 10.1371/journal.pone.0022827ISI: 000293561200033OAI: oai:DiVA.org:uu-158169DiVA: diva2:438495
Available from: 2011-09-02 Created: 2011-09-01 Last updated: 2012-11-09Bibliographically approved

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Mann, Richard P.
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