CompressionFalsifier
plain-language theorem explainer
The falsifier structure enumerates three conditions that would refute the J-cost derivation of compression limits. Researchers testing Recognition Science information bounds would cite these criteria to state empirical refutation tests. The definition directly bundles the failure propositions with an implication to falsehood.
Claim. A record whose fields are the propositions that compression can achieve average code length below Shannon entropy $H(X)$, that J-cost fails to decrease under compression, that random data admits systematic compression, and that the first of these propositions yields a contradiction.
background
The module INFO-003 derives fundamental limits on lossless compression from J-cost. In Recognition Science, information carries J-cost; compressed representations lower this cost while entropy supplies the minimum J-cost for faithful representation. Shannon's source coding theorem states that average code length is at least $H(X) = -∑ p(x) log₂ p(x)$, and the RS mechanism identifies this bound with J-cost minimization.
proof idea
Structure definition with empty body. It directly encodes the three falsification conditions and the contradiction clause listed in the module documentation.
why it matters
Supplies the explicit falsifiability criteria for the compression limits derived from J-cost. It operationalizes the claim that entropy equals minimum J-cost and that compression equals J-cost minimization. No downstream theorems are recorded.
Switch to Lean above to see the machine-checked source, dependencies, and usage graph.