IndisputableMonolith.Information.RecognitionEntropy
This module supplies self-contained definitions for the golden ratio phi and phi-bit entropy measures in the Information domain of Recognition Science. It supports calculations involving recognitionEntropy, phiBit, and capacity bounds without external dependencies. Researchers analyzing information content in RS events or phi-ladder structures would cite these definitions. The module is purely definitional with supporting inequalities and no proofs.
claim$\phi = \frac{1 + \sqrt{5}}{2}$, recognitionEntropy, phiBit, cp6CapacityPhiBits, recognition_event_12_dof, uniform_maximizes_entropy
background
The module resides in the Information domain and imports the RS time quantum $\tau_0 = 1$ tick from Constants together with J-cost definitions from JcostCore. It supplies a local definition of the golden ratio for self-containment, along with basic properties (phi > 1, phi < 2) and derived objects such as phiBit (information unit) and recognitionEntropy (entropy measure in phi bits). The sibling definitions include cp6CapacityPhiBits and uniform_maximizes_entropy, which operate in the same phi-native information setting.
proof idea
this is a definition module, no proofs
why it matters in Recognition Science
This module grounds the information-theoretic layer of Recognition Science by providing phi-based entropy and capacity primitives. It feeds downstream results on recognition events and entropy maximization within the phi-ladder framework (T5 J-uniqueness and T6 self-similar fixed point). The local phi definition ensures the module remains independent while supporting RCL-compatible calculations.
scope and limits
- Does not prove any entropy maximization theorems.
- Does not connect entropy to physical constants or forcing chain steps T0-T8.
- Does not import or reference external information theory libraries.
- Does not address spatial dimensions or mass formulas.