In this project-based course, you will be introduced to and explore one of the most relevant features of NetLogo: BehaviorSpace. The context behind such a feature is that a model's true insights arise when it runs multiple times with different combinations of settings (parameter values). This approach, sometimes referred to as parameter sweeping, allows the researcher to observe a large range of behaviors that the system is capable of producing. And that is exactly what you will be doing.
In addition to that, you will analyze the results of your BehaviorSpace experiments with an open and entry-level (codeless) data analysis tool: RAWGraphs 2.0. With it, you will create many insightful data visualizations, which can all be brought together in a project's report.
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
NetLogo's BehaviorSpace + RAWGraphs
Coursera Project Network via Coursera
This course may be unavailable.
Overview
Syllabus
- BehaviorSpace + RawGraphs
- Do you know what is the parameter space of a model? It is the whole set of possibilities a model have based on the range of values of its settings (sliders, switches, choices, or any global variable). The dimensions of the parameter space are the number of settings of the model, in which every point is a particular combination of values. Our approach in this project will be to explore the Ants model's "space" of possible behaviors and determine which combinations of settings cause the behaviors of interest. Let's go?
Taught by
Danilo Oliveira Vaz
