Designing Systems of Study
Systematic Space and Representation
If we can examine a system in which the structure and rules can be performed in fixed steps of calculation we can extrapolate that behavior over a much larger structure and perform larger computations of a state predictive nature.
When examining a system, to be debugged or to performed in parallel operation, if we consider an approach of representation spaces, we can enumerate the likeness of a space to a presentation relevant and spatially significant for the purpose of identifying a unique state.
What we can extrapolate from Neural Networks, we can apply to the nature of study of systematic representationalism, at any given dimensionality we can interpolate an identifiable number of vectors which represent a large and wide variable set of states.
If we can assume the nature of the outputs are relevant to the simulation we can expect such enumeration of variables to represent a limited set of actions or spatial dimensionality representing some attributable identity.
Through the actions of propagation we can create sub-similar identities in which major features are predicted by the application of a latent vector which can represent some structure of this system.
As more transformations occur, the fixed nature of the transformation should assume that all the hashes of all the provided data matches its own.