the Scoped Propagator model is based on two key insights:
1. by representing computation as mappings between nodes along edges, you do not need to know at design-time what node types exist.
2. by scoping the propagation to events, you can augment nodes with interactive behaviour suitable for the environment in which SPs have been embedded.
It's pretty specific to UI similar to the examples, but in terms of these big infinite canvas UIs, you have a whole bunch of objects you need to make dependant on each other, but who ALSO hold their current state statically. Subtly different from a spreadsheet, where Excel for example treats functions (=$A$2) differently from a single value (4). Functions are always derived, where as single values don't rely on anything.
Like a sticky note on a canvas, should always be available to type into. But maybe we also want something else to change it's value when it changes. (key insights #2, this is for infinite canvases)
Rather than building some big god-object that stores all "root" state for the static value of things, and transforming that thru a big tree of functions you need to keep maintained, you could reverse it:
Each node only knows it's own state, but it's peers give it instructions about how to update itself to meet their business rules, for just that pairing.
The result is the same, it's a big web of functions, but now you don't have to make the distinction between "derived" state or "base" state. It's always static state, and things are just updated adhoc by these events that describe the changes without explaining "why" the "to" node has to apply them. (key insights #1, you do not need to know at design-time what node types exist.)