Last time we established that our Conveyance Function for output object is The Conveyance Function maps the Input Space to the Output Space but the question that we really want answered is: “Given some Output State , what is its corresponding Input State ?”
The Process Language does not itself provide insight, it is simply a closed-form mathematical way of representing the structure and and behaviour of processes and objects.
It enables us, through the language, to perceive the lifetime of an object or a process, simply by referring to its (end) state.
I’m beginning now to think that the key insight of a well defined Process Model is the Inverse Conveyance Function that is it enables us to walk backwards from a finished product to its constituents.
The Conveyance Model provides an analytical basis for insighting the object. By simply knowing the state of your object, the complete lifetime of the object can be known via the Inverse Conveyance Function. That is saying that embedded in the structure of an object, and with the Inverse Conveyance Function… (this trails off here)
The process dynamics state that for a complete model I attempt to produce a figure (see i) but then scribbled it out
It must be time which binds all things together.
It is in this back-projection that I am most interested, because it provides a deep insight into the intrinsic nature of the objective. I think more than just back-propagation per-se, but the ability, facilitated by a well defined objective structure, to traverse the object lifetime is what excites me.
By having time as a universal dimension, it enables communication across the process space.