Assume we have a number of transducers with limited processing capabilities (time and/or space). Are there theoretical results (proofs) on how these transducers can be combined by some sort of communication into one joint transducer to reach a maximum of processing capabilities? Does this work for universal transducers or only for certain problems?

Example: Let's say I have 3 signal-processing computers and need to combine them to steer a self-driving car. Are there theoretical results on how or how not to do that?

Background: I recently came across Stafford Beer's "Viable Systems Model". In my interpretation, it claims to be such an optimal architecture for combination. But I have only found anecdotal evidence or reference to common sense for most parts of that claim, no formal proof.

Assume we have a number of transducers with limited information processing capabilities. Are there theoretical results (proofs) on how these transducers can be combined by some sort of communication into one joint transducer to reach a maximum of information processing capabilities? Does this work for universal transducers or only for certain problems?

Illustrative example: Let's say I have 3 signal-processing computers and need to combine them to steer a self-driving car. The cameras and other sensors provide a lot of information, e.g., x GBit/s. Each single computer would be overwhelmed by the amount of information. But by combining the computers the information processing capabilities might be sufficient. Are there theoretical results on how or how not to do that to make the best use of the available computers?

Background: I recently came across Stafford Beer's "Viable Systems Model". In my interpretation, it claims to be such an optimal architecture for combination. But I have only found anecdotal evidence or reference to common sense for most parts of that claim, no formal proof.