Senior Theses
Publication Date
5-15-2019
Document Type
Thesis (Open Access)
Degree Name
Bachelor of Science in Physics
Department
Physics
Faculty Advisor(s)
Joelle Murray (Thesis Advisor)
Michael Crosser & Jennifer Heath (Committee Members)
Subject Categories
Applied Mathematics | Biological and Chemical Physics | Dynamic Systems | Non-linear Dynamics | Physics
Abstract
Complexity is prevalent both in natural and in human-made systems, yet is not well understood quantitatively. Qualitatively, complexity describes a phenomena in which a system composed of individual pieces, each having simple interactions with one another, results in interesting bulk properties that would otherwise not exist. One example of a complex biological system is the bird flock, in particular, a starling murmuration. Starlings are known to move in the direction of their neighbors and avoid collisions with fellow starlings, but as a result of these simple movement choices, the flock as a whole tends to exhibit fluid-like movements and form interesting structures. To understand complexity, we chose fly swarms as the system to model. To do this, we utilized stochastic modeling to simulate the movements of individuals, giving them different guiding rules based on both laboratory observation and other models to best produce a realistic model. We hope to compare values of key properties both with other research groups as well as under varying conditions within our model to find if there is a property that can qualitatively describe if the system is complex or not.
Recommended Citation
Taylor, Troy, "Quantifying Complex Systems via Computational Fly Swarms" (2019). Senior Theses. 43.
https://digitalcommons.linfield.edu/physstud_theses/43
Included in
Biological and Chemical Physics Commons, Dynamic Systems Commons, Non-linear Dynamics Commons
