messageJCost
plain-language theorem explainer
messageJCost defines the J-cost of a message as its length multiplied by one minus its redundancy. Information theorists deriving compression bounds in Recognition Science would cite this expression when linking entropy to minimum organizational cost. The definition is a direct algebraic product with no lemmas or tactics applied.
Claim. For a message of length $l$ and redundancy $r$, the J-cost is $J = l(1-r)$. Maximum compression sets $J$ equal to the source entropy when all redundancy is removed.
background
The Information.Compression module derives fundamental limits on data compression from J-cost. J-cost measures the organizational cost of a message, with compression acting as J-cost minimization while preserving faithful representation. The module restates Shannon's source coding theorem as the statement that average code length cannot fall below entropy $H(X)$ and identifies this bound with minimum J-cost in Recognition Science units.
proof idea
This is a one-line definition that directly computes the product of length and (1 minus redundancy). No upstream lemmas are invoked beyond the arithmetic operations on real numbers.
why it matters
The definition supplies the explicit J-cost expression used to connect compression to the Recognition Composition Law and the reparametrized cost function $H(x) = J(x) + 1$. It supports the module claim that entropy equals minimum J-cost for lossless representation and feeds the sibling result that compression is J-cost minimization. The construction aligns with the broader framework in which information carries J-cost derived from the functional equation and the eight-tick octave structure.
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