Yes, it's always cool to have multiple visualizations of the same concept at different levels of abstraction, but in real-life there's always a trade-off between how much time you invest in visualizations and what you actually learn from it.
In this example I would say that the real-time simulation doesn't actually teach me a lot. It behaves exactly how I would suspect from reading the algorithm the first time. Being able to control the time and the angle is cool, but I need to interact a lot with the visualization to learn anything. It's also hard to actually compare different angles.
In comparison, with just a simple plotting tool I can easily create two graphs:
- For a specific angle, let X-axis be time and Y-axis be the displacement from the center.
- Let X-axis be angle and Y-axis be mean (or max) of the displacement for the whole period.
The first one will give me some intuition about how the system behaves, and the second one will immediately show me that it's impossible to find an angle which gives a good result — which suggests that the algorithm in itself isn't good enough.
I totally see that the tooling created in this neat, but how much more to learn from these tool over the two mentioned plots? And how long did it take to create these tool? That is for me the biggest question.
Example:
> We want direct, interactive control of each parameter, so we can go forward and backward, stop, and jump to arbitrary positions. This allows us to travel freely around the territory, investigate areas of interest, and pose and verify hypotheses as quickly we think of them.
In my physics simulation I've rarely needed a tool at this fidelity. Looking at a single use case just doesn't give me enough information and dragging sliders is very time consuming. I've pretty much always ended up defining a metric (which is mentioned in the article) and plotting that for various parameters. It's amazing how much you can learn about a system with such a simple visualization.