Detecting Dynamic Patterns
with
Twinking Nodes Representation
and
Sound Representation

When Tributaries are long

Now that you're familiar with the basic idea ideas of N, K Boolean systems, let's examine their representation with twinkling nodes for a more complex example with N=100 nodes. Optionally (see instructions at end of this page) you will be able to represent the dynamics with sound.

Instructions for Twinkling Nodes
Instructions for Sound Controls

FIRST, click the DELAY button and SET delay to around 50 to 100 msec., then click the PLAY button.

This system has very long tributaries leading into the attractor cycle patterns. So you may have to let the system run for a while before cycle patterns begin. Can you detect when patterns begin (as distinct from the tributary)?

The nodes will change states (twinkle) in a pattern that reflects the shifts inherent to the basin the system is in. The fundamental epistemological questions posed by a dynamic ecology to all sentient beings include noticing which basin a system is in and noticing when the dynamics shift from one basin to another. Which repetitive pattern of behaviro characterizes the prey in at this moment? A predator needs to be able to extract basin patterns from the enviroment and adjust its behavior to the differences in those patterns.

Perturbing the System. The Perturb button allows you to pseudo-randomly change the state of some percentage (chosen by you but defaulting to 50%) of the system's nodes. If you perturb the system does it change to another basin? Which? As the system is perturbed can you learn to recognize its many basins? Can you tell one from another?

This is a rather complex system with many basins (86 basins were identified in one search). The basin lengths include L=4, 8, 12, 16, 32, 40, and 72. Many tributaries into the the various basins are hundreds of iterations long. Learning to recognize these shifts from tributary into attractor cycle with Twinkling nodes is very difficult, as you will most likely notice as you perturb the system mulitple times. The use of sound sequences (below) is a more useful representation to many people. The use of the visual representation we call Smilie 3 (see other web pages) is even more useful.

The central issue is this: If we think of input from the univerese as dynamic systems then how do we know the patterns generated by those systems?