Loop Gain

Canonical definition

Loop Gain is the strength of the feedback cycle inside the Decision Hub through which Mimetic activation (M), the Decision Hub's Signed Valuation (V), and the Observer's Salience state (S), the Salience the Observer computes under the prior-configuration it reads from the Belief Register, drive one another in turn. The cycle runs in a closed loop: Mimetic activation (M) lifts the Decision Hub's Signed Valuation (V); the Signed Valuation (V) updates the Salience state (S) computed on the Observer's channel; the updated Salience state (S) feeds back into Mimetic activation (M) on the next operating cycle. Loop Gain measures how strongly a perturbation at any point in the cycle is amplified by the time it returns to the same point. When Loop Gain is below the Critical Coupling Strength, a perturbation shrinks each time it travels around the loop and dies out. Mimetic transmission registers, the Individual learns from the Model, and the system returns to baseline. Mimetic activity in this regime is transient social learning. When Loop Gain exceeds the Critical Coupling Strength, the same perturbation grows each time it travels around the loop. Mimetic activation compounds, the Mimetic Premium accumulates, and rivalry escalates. The Critical Coupling Strength is the threshold value of Loop Gain at which the qualitative behaviour of the loop changes from decay to compounding.