This is an awesome tool, thank you!
I have a suspicion, that with one addition this could be turing-complete (I don't have proof, just gut feeling). Also, this feature would help people to mirror and demonstrate simple natural networks like prototypical neural networks or simple gene-regulatory networks.
The idea, simply, is to allow nodes to 'decide' whether to pass through, or consume, an arrow. The two most useful 'functions' I can imagine are to simply filter arrows by polarity (e.g., nodes that can consume but not pass-through negative arrows, but pass-through positive arrows), and to pass-through only if the node is fuller than a certain threshold, or less-full than a threshold.
For neurons, for example, you could then mimic threshold-firing behaviour using a pattern like:
(input)--->(threshold 0.9)---(filter(positive))--->(output)
^ |
| V
(feed-back emits positive *and* negative)
So, when input pushes threshold over fullness 0.9, it starts to pass-through, and the next input triggers a cascade of positive and negative arrows (more negative than positive, though). The positives are allowed through by the filter, and hit output. The negatives pull the neuron back below its threshold, and the cycle begins again.
This is a bit of a big ask, so I'd understand if it's overcomplicated for what loopy's designed to do.
I do feel that these thresholds would improve some of the example simulations, though; for example, in the automation/job scenario, you can use these to model how social unrest only starts to cause political upheaval above a certain threshold, or how tax income only increases until unemployment hits a certain threshold, and then may start to crash as consumer spending halts.