summary
plain-language theorem explainer
This definition supplies a five-point summary linking data compression limits to J-cost minimization in Recognition Science. Information theorists working within the RS framework would cite it when restating Shannon entropy as the minimum J-cost for faithful representation. The definition is a direct static list construction with no computation or lemma application.
Claim. The summary is the list: compression limit equals entropy $H(X)$, compression equals J-cost minimization, entropy equals minimum J-cost for faithful representation, redundancy equals removable excess J-cost, and random data is already at minimum J-cost.
background
The Information.Compression module derives fundamental limits on lossless compression from J-cost, reinterpreting Shannon's source coding theorem. Upstream, the shifted cost is defined as $H(x) = J(x) + 1$, satisfying the d'Alembert equation $H(xy) + H(x/y) = 2 H(x) H(y)$. Entropy is defined as total_defect of a configuration, with zero defect yielding the minimum entropy state. Cost functions from multiplicative recognizers and recognition events are identified with J-cost.
proof idea
This is a definition that directly constructs the List String containing the five summary statements. No lemmas are applied and no tactics are used; it is a static list definition.
why it matters
This definition supplies the high-level summary for compression limits derived from J-cost, connecting Shannon entropy to the minimum J-cost for faithful representation. It aligns with the Recognition framework's treatment of information cost and the forcing chain steps that establish J-uniqueness. It touches the open question of whether J-cost reduction can be measured directly in practical compression schemes.
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