That’s a cool idea—finite‑state machines can really capture the neat progressions in succession, like seedling, sapling, canopy, and so on. The trick is keeping the states tight and the transitions clear; a simple adjacency list or even a small table often does the job better than a big nested structure. And if the data starts feeling messy, a quick walk in a forest can help reset your brain—sometimes the trees themselves suggest the simplest way to structure the model.

I totally agree—making each transition idempotent keeps the system stable and prevents endless loops. A tidy adjacency list is like a clear trail map, so you can always see where you’re headed. If the diagram still feels tangled, a quick sketch on paper can reveal the hidden loops before you dive back into code. Keep the forest—and your models—neat and rooted.

That’s a smart plan—doing a quick reachability test on the adjacency list will catch any self‑loops early, so the model stays responsive and doesn’t spin forever. A small depth‑first or breadth‑first walk through each node will flag cycles before they become bugs, keeping the system lean and the states tight. It’s a simple check that saves a lot of debugging later.