Things like a GUI designer or level editor map a 2D or 3D domain to a 2D or 3D-projected-to-2D space. A 3D animation editor maps a 4D domain to a 2D projection of a 3D representation plus a timeline representing the 4th time dimension. These mappings are natural, intuitive and work well generally.
Visual programming tools attempt to map logic to a (usually) 2D domain where there is no natural or intuitive general mapping. The representation has both too many degrees of freedom (arbitrary positions of nodes in 2D space that are not meaningful in the problem domain) and too few (connections between nodes end up crossing in 2D adding visual confusion due to constraints of the representation that don't exist in the problem domain).
I've been exploring colored Petri nets for our product and they do seem to have promise for certain use cases though so I do think it's an interesting area to explore.
> Visual programming tools attempt to map logic to a (usually) 2D domain where there is no natural or intuitive general mapping. The representation has both too many degrees of freedom (arbitrary positions of nodes in 2D space that are not meaningful in the problem domain) and too few (connections between nodes end up crossing in 2D adding visual confusion due to constraints of the representation that don't exist in the problem domain).
In general this is true, but the diagrams we use at Statebox are different in the sense that there is a completeness theorem between the diagrammatic language and an underlying mathematical structure (a category). In this case the mapping is sound by definition.
Also, it is worth stressing that our diagrammatic calculus is topologically invariant, meaning that the position of diagrams in space is meaningless, everything that matters is connectivity. This is also the approach originally used by Coecke and Abramsky in the field of Categorical Quantum Mechanics, which is getting huge success to define quantum protocols :)
Visual programming tools attempt to map logic to a (usually) 2D domain where there is no natural or intuitive general mapping. The representation has both too many degrees of freedom (arbitrary positions of nodes in 2D space that are not meaningful in the problem domain) and too few (connections between nodes end up crossing in 2D adding visual confusion due to constraints of the representation that don't exist in the problem domain).
I've been exploring colored Petri nets for our product and they do seem to have promise for certain use cases though so I do think it's an interesting area to explore.