Networks that self-organize in response to information are one of the most central studies in complex systems theory. A new time series analysis tool for studying self-organizing systems is developed and demonstrated. This method is applied to interacting complex swarms to explore the connection between information transport and group size, providing evidence for Dunbar's numbers having a foundation in network dynamics. A complex network model of information spread is developed. This network infodemic model uses reinforcement learning to simulate connection and opinion adaptation resulting from interaction between units. The model is applied to study polarized populations and echo chamber formation, exploring strategies for network resilience and weakening. The model is straightforward to extend to multilayer networks and networks generated from real world data. By unifying explanation and prediction, the network infodemic model offers a timely step toward understanding global collective behavior.
| Identifer | oai:union.ndltd.org:unt.edu/info:ark/67531/metadc1873551 |
| Date | 12 1900 |
| Creators | Culbreth, Garland |
| Contributors | Grigolini, Paolo, Buongiorno Nardelli, Marco, Krokhin, Arkadii, Weathers, Duncan |
| Publisher | University of North Texas |
| Source Sets | University of North Texas |
| Language | English |
| Detected Language | English |
| Type | Thesis or Dissertation |
| Format | viii, 91 pages, Text |
| Rights | Public, Culbreth, Garland, Copyright, Copyright is held by the author, unless otherwise noted. All rights Reserved. |
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