IndisputableMonolith.Measurement.BornRuleLight
This module defines the normalized Born rule in Recognition Science by converting recognition weights into probabilities that sum to one. It supplies the mapping needed for measurement outcomes in the framework. The module consists of a single core definition ensuring the probabilities align with the J-cost structure.
claimThe normalized Born rule converts recognition weights $w_i$ into probabilities $p_i = w_i / (sum_j w_j)$.
background
Recognition Science derives physics from a single functional equation whose solutions yield the J-cost function. This module sits in the Measurement domain and introduces the normalization step that turns raw recognition weights into probabilities obeying the Born rule. The setting uses the J-functional as the source of weights prior to normalization.
proof idea
This is a definition module, no proofs.
why it matters in Recognition Science
This module feeds the measurement theory in Recognition Science by supplying the normalized Born rule. It connects recognition weights to observable statistics and supports applications of the recognition composition law.
scope and limits
- Does not derive the explicit form of recognition weights.
- Does not prove equivalence to the standard quantum Born rule.
- Does not include applications to specific physical systems.