Details:

Journal Title:
PLoS Computational Biology
Personal Author:
Titus, Mathew ; Hagstrom, George ; Watson, James R.
Titus, Mathew ; Hagstrom, George ; Watson, James R. Less -
NOAA Program & Office:
CIMES (Cooperative Institute for Modeling the Earth System)
Description:
Collective behavior is an emergent property of numerous complex systems, from financial markets to cancer cells to predator-prey ecological systems. Characterizing modes of collective behavior is often done through human observation, training generative models, or other supervised learning techniques. Each of these cases requires knowledge of and a method for characterizing the macro-state(s) of the system. This presents a challenge for studying novel systems where there may be little prior knowledge. Here, we present a new unsupervised method of detecting emergent behavior in complex systems, and discerning between distinct collective behaviors. We require only metrics, d(1), d(2), defined on the set of agents, X, which measure agents' nearness in variables of interest. We apply the method of diffusion maps to the systems (X, d(i)) to recover efficient embeddings of their interaction networks. Comparing these geometries, we formulate a measure of similarity between two networks, called the map alignment statistic (MAS). A large MAS is evidence that the two networks are codetermined in some fashion, indicating an emergent relationship between the metrics d(1) and d(2). Additionally, the form of the macro-scale organization is encoded in the covariances among the two sets of diffusion map components. Using these covariances we discern between different modes of collective behavior in a data-driven, unsupervised manner. This method is demonstrated on a synthetic flocking model as well as empirical fish schooling data. We show that our state classification subdivides the known behaviors of the school in a meaningful manner, leading to a finer description of the system's behavior.
Keywords:
[+]
Collective behavior
Source:
PLoS Computational Biology, 17(2)
DOI:
https://doi.org/10.1371/journal.pcbi.1007811
Document Type:
Journal Article
Funding:
Grant no. AWD100582 ; Grant no. NA18OAR4320123
Grant no. AWD100582 ; Grant no. NA18OAR4320123 Less -
Rights Information:
CC BY
Compliance:
Submitted
Main Document Checksum:
[+]
urn:sha256:854009ffa13e82909ff8ddb714af166d25932bcb022026d07b7af84bb3f74c35
Download URL:
https://repository.library.noaa.gov/view/noaa/29603/noaa_29603_DS1.pdf
File Type:

Supporting Files

• Titus_PLoSCompBio_2021.xml

  xml

More +