Entropy Signatures of Behavioral Transitions: A Quantum-Like Framework for Human and AI Decision Analysis

Preprint, originally published April 2026; this is v1.1, corrected October 2026. We present an entropy-based behavioral measurement framework integrating quantum-like Bayesian network interference (Moreira & Wichert, 2016), Deng entropy (Deng, 2016), and belief-entropy interference measurement (Huang et al., 2019) into a four-dimensional entropy profiling system (Ψ). We evaluate this framework across 82,932 samples spanning four domains: human survey decisions (53,078 participants across 72 studies), AI agent shutdown avoidance (2,200 transcripts across 11 models), AI agent complex sabotage (7,254 transcripts), and keystroke biometrics (20,400 samples). Key findings: (1) the Huang interference method reduces prediction error by 49% versus static quantum approaches on Prisoner's Dilemma data (avg error 0.061 vs 0.119), with paper-verified mathematical consistency (Db = −0.9421 matching the published −0.9420); (2) applied to AI safety, the framework detects behavioral mode transitions with Cohen's h = 1.986 (comply-to-bypass) and produces large effect sizes across three Ψ dimensions (Ψ₁ d = 1.182, Ψ₂ d = 0.806, Ψ₃ d = 1.024); (3) five distinct behavioral archetypes emerge across AI models, with no single detection method covering all types; (4) a four-phase temporal model (持樞, chí shū) identifies intervention windows averaging 6.8 steps before transition consolidation, evaluated on Palisade transcripts (25 seeds; Fisher p < 0.0001, Cohen's h = 1.611), with SHADE-Arena and CMU keystroke data serving as scope checks; (5) dimensional independence is domain-specific (Ψ₁–Ψ₂ r = −0.59 in human surveys, r = 0.014 in AI behavioral data). No raw third-party data is redistributed. v1.1 corrections (six items, listed in the erratum section of the PDF): QOdata72 participant count corrected to 53,078; total sample count corrected to 82,932 (previously double-counted); the "75,600 samples in three domains" phrase withdrawn; o3/o1-preview bypass rates relabelled as pooled classification over the released subset; the |D_b| > 0.48 subgroup labelled exploratory with a post-hoc threshold; "Ψ₂ as automatic model selector" restated as a hypothesis for a pre-registered test.