Powered by OpenAIRE graph
Found an issue? Give us feedback
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Preprint
Data sources: ZENODO
addClaim

Structural Optionality and the Precursor Window: SCFL as a Substrate-Level Measurement Framework for Decision-Window Expansion and Real Options Generalization

Authors: Brogdon, Ronald;

Structural Optionality and the Precursor Window: SCFL as a Substrate-Level Measurement Framework for Decision-Window Expansion and Real Options Generalization

Abstract

Structural optionality is defined as the form of capital whose value derives from measured substrate-level deformation rather than from stochastic volatility or market-implied uncertainty. This paper establishes the theoretical and formal foundations of structural optionality through the Scale-Free Coherence Framework (SCFL) and its primary instrument, the Standard Coherence Fidelity Layer (SCFL). The central claim is precise: SCFL is an upstream measurement framework that characterizes the coherence dynamics governing transitions between stochastic regimes, which Real Options Theory (ROT) assumes as exogenous inputs to optimal decision-making under uncertainty. ROT tells you how to value optionality. SCFL tells you when optionality exists. The paper formalizes the Precursor Window W_P = (T₀, T₁) as the maximal interval in which substrate degradation is active (dC/dt < 0), outcome-level stationarity holds (dO/dt ≈ 0), and coherence half-life compression is present (τ½(t) < τ½_baseline). This three-condition definition ties T₀ identification directly to τ½ behavior rather than qualitative early movement, and is empirically grounded in the ERCOT April 7–8, 2026 prospective validation event (τ½ = 3.36 hours, R² = 0.964, ΔT ≈ 7 hours). A formal crosswalk maps classical ROT constructs to SCFL analogues: the coherence field C(t) replaces the stochastic underlying asset; coherence deformation operators Δ1 and Δ2 replace volatility σ; the precursor window ΔT replaces time-to-expiration; and the recoverability corridor D(C(t)) > 0 replaces the exercise boundary. SCFL does not generate stochastic processes — it constrains the admissible regime space Ω_{C(t)} in which those processes operate, detecting regime transitions in C(t) prior to distributional change in S(t). The Non-Reducibility Theorem establishes that if W_P ≠ ∅, then C(t) ∉ span{F(O(s))}_{s≤t} for any measurable functional F — making SCFL irreducible to Early Warning Signal theory, network robustness frameworks, or dynamical systems models. The Domain-Invariance Axiom establishes that the existence of W_P is preserved under any invertible measurable embedding, grounding SCFL’s cross-domain applicability across infrastructure, finance, healthcare, supply chains, and government systems. Synthetic validation across three testbed system classes (flow-network, queue-based logistics, agent-based institutional) confirms domain-invariant precursor detection. The paper includes a full mathematical appendix (coherence field construction, operator definitions, stability analysis, Rupture-Corridor Lemma) and a literature positioning appendix (Non-Reducibility Theorem with corollaries against EWS, network robustness, and dynamical systems theory). This paper is part of the Upstream Coherence Measurement Stratum (UCMS) canon (128 confirmed Zenodo publications).

Powered by OpenAIRE graph
Found an issue? Give us feedback