The Twinkle Dip: A Non-Monotonic Optimum in Thermodynamic Efficiency from Recursive Causal Structuring

This manuscript presents the "Twinkle Dip" hypothesis: a prediction that recursively structured dissipative systems exhibit a sharp, non-monotonic optimum in thermodynamic efficiency at a critical recursive depth of DRTB≈6.5DRTB≈6.5. This optimum is derived as a topological phase transition within the Neutral Relata + Asymmetric Causation (NR+AC) ontological framework, where the Asymmetric Causation (A) field stabilizes a state of maximal causal alignment—termed "thermodynamic superconductivity." We validate this prediction through a stochastic chemical reaction network (CRN) model implementing catalytic feedback. Gillespie simulation reveals a clear efficiency (η=P/ση=P/σ) peak at a feedback strength of β=3.2β=3.2. Spectral calibration maps this physical parameter to an effective recursive depth (neffneff), confirming the predicted optimum is bracketed by dynamical regimes (neff=9.0neff=9.0 and 5.05.0). This work completes a three-part validation: theoretical derivation, computational instantiation, and metric calibration, providing a testable bridge to experimental systems in non-equilibrium physics and a quantitative model for optimal conscious states.