Decoding A Black Box

Exploring Cellular Automata

I’ve been busy with school, travel, and a conference for the past couple of weeks, so today’s post will be a short one.

60% of the grade for my Introduction to Systems Science class is based on a group assignment. Our task is to study the behavior of a system and propose a model of what it does.

The system we are studying seems to be some form of cellular automaton, a form of simulation that I was first introduced to in Joe Norman’s Introduction to Applied Complexity class. In cellular automata, the behavior of each individual cell is influenced by the state of the cells surrounding it. Each cell in the 20 x 20 grid below can take on any value between 0-9. The state of the cells change when you press the “Next n Step” button.

This is the grid in its initial state.

This is the grid after 1000 steps.

Here it is after 10,000 steps.

The system never behaves exactly the same way, but there are very consistent patterns that emerge every time. Cellular automata are great examples of how complex organized behavior can emerge from individual components following simple rules. Our goal is to do our best to identify what those rules are for this system.

I’m finding the assignment to be a fun puzzle that is building my capacity to work in groups, and pushing me to develop my coding skills as I write python scripts to efficiently gather data from the simulation for statistical analysis.

I’ll share more as work on the project progresses, and I'd love to hear about any experiences any of you might have with cellular automata.