IndisputableMonolith.Information.NetworkTopologyFromSigma
This module predicts the degree exponent of networks from the sigma cost in Recognition Science as gamma equals one plus phi. Network science researchers would cite it for the RS-native scaling in scale-free graphs. The module assembles the claim from imported constants and cost structures through direct definitions.
claimThe degree exponent in the network topology derived from the sigma cost satisfies $gamma = 1 + phi$.
background
Recognition Science derives all physics from one functional equation with the J-cost at its core. The Constants module supplies the fundamental RS time quantum tau_0 equal to one tick. The Cost module defines the associated cost structures. This module in the Information domain applies these to construct network properties.
proof idea
This is a definition module, no proofs. It introduces degreeExponent as one plus phi along with the NetworkTopologyCert to encapsulate the topology from sigma.
why it matters in Recognition Science
The module supplies the predicted degree exponent gamma equals one plus phi that follows from the self-similar fixed point phi. It supports the Information domain by providing the topology prediction aligned with the forcing chain.
scope and limits
- Does not derive the exponent without the sigma cost assumption.
- Does not simulate finite networks or compare to data.
- Does not address directed graphs or higher moments.