Chaos and Instability

Course subject(s) Chaos and Instability

Lecture video part 1

Lecture video part 2

Wikipage of the lecture: LectureOnChaosAndInstability

This lecture will discuss Chaos and instability. Students should be able to:

  • Define chaos and differentiate it from randomness
  • Understand pseudo random numbers and their role of in Agent Based Models
  • Understand model bifurcations
  • Understand the notion of attractors and attractor maps.

ToDo: Parameter sweeps in models are used to test for chaos and instability.

  • Use the learning model that you are developing to test, if you have it done already. Alternatively, choose any model from NetLogo that you like, that has at least 2 parameters that can be varied.
  • Think of a interesting experiment where you need to analyze a 2D parameter space.
  • Decide which output metrics you will measure. Use one or two, to limit the amount of graphing.
  • Think about the stop criterion, or limit the maximum number of ticks.
  • Perform the experiments and report them as usual.
  • Be aware that a parameter sweep can quickly get out of had in therms of computational time
  • First, calculate the number of parameter points that you want to test.
  • Then, time a single run to get a sense of the overall duration of the experiment
  • Adapt your parameter space as necessary. You can either reduce the bounds, or increase the coarseness.
  • Be aware that laptops can get very warm after hours of crunching numbers.

Below is a link to the lecture slides.

Creative Commons License
Agent Based Modeling of Complex Adaptive Systems (Basic) by TU Delft OpenCourseWare is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
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