In 1948, Claude Shannon published "A Mathematical Theory of Communication" and formalized the structure of information transmission. He gave us entropy as a measure of uncertainty, channel capacity as a bound on reliable transmission rate, the source coding theorem establishing minimum encoding length, and the noisy channel theorem establishing the conditions under which reliable communication is possible despite corruption. The framework is complete, proven, and foundational. Every digital communication system operates within the boundaries Shannon defined. Shannon also drew a deliberate exclusion. In his opening remarks, he stated that the semantic aspects of communication are irrelevant to the engineering problem. Meaning — what a message signifies, what a receiver does with it, how information becomes action — was outside the scope. This exclusion was correct and productive. It allowed Shannon to isolate the channel and solve it completely without entanglement in the unbounded complexity of interpretation and response. The result is a framework shaped like a pipeline: Source → Encoder → Channel → Decoder → Destination. Shannon formalized everything between Encoder and Decoder — the middle of the pipeline. The endpoints — Source and Destination — were treated as given. The source produces messages with some statistical structure. The destination receives them. What happens at the source before encoding, and what happens at the destination after decoding, was not Shannon's problem. This paper operates on the other side of Shannon's boundary. It does not challenge or modify Shannon's channel mathematics. It formalizes what happens at the endpoints — the processing that occurs before a message is encoded and after a message is decoded. The claim is that processing, like transmission, has mathematical structure that is universal across domains and substrates. Shannon proved that all channels share a common mathematics regardless of physical medium. This paper proposes that all processors share a common mathematics regardless of what they process. The goal is not to replace Shannon but to complete the picture. Shannon gave us the mathematics of information movement. This paper proposes the mathematics of information action — the conversion of received information into a state where a processor can operate on it.
Geoffrey Howland (Mon,) studied this question.