Appearance–Behavior Framework v2.2: Real-Time Digital Twin and Sequential Bayesian Updating

ABF v2.2 advances the Appearance–Behavior Framework from validation-grounded inference toward adaptive personalized monitoring through the introduction of Sequential Bayesian Updating and a Digital Twin architecture.

Building upon the predictive forecasting capabilities of ABF v2.0 and the validation and uncertainty quantification framework of ABF v2.1, this release introduces five tightly integrated contributions:

• Sequential Bayesian Updating Architecture for incremental posterior updating without full re-estimation (Fig. 28)
• ABF Digital Twin Dashboard for individualized behavioral monitoring (Fig. 29)
• Batch vs. Sequential Inference comparison using a synthetic ADNI-inspired pilot (Fig. 30)
• Personalized Behavioral Forecasts across heterogeneous risk profiles (Fig. 31)
• Adaptive Weight Evolution under Sequential Bayesian Updating (Fig. 32)

The framework enables continuous updating of:

• Behavioral Transformation Risk Score (BTRS)
• Estimated critical transition time (τ̂crit)
• Forecast trajectories
• Uncertainty decomposition

through a closed-loop Digital Twin architecture.

ABF v2.2 represents the adaptation phase of the framework roadmap:

v1.x (Theory) → v2.0 (Prediction) → v2.1 (Validation) → v2.2 (Adaptation) → v2.3 (Intervention)

The repository contains:

• Full manuscript
• Figure collection (Figs. 1–32)
• Demonstration Python implementation
• README documentation

All results are simulation-derived and intended for methodological illustration only.

ABF v2.2 is a risk assessment support framework and not a diagnostic or prognostic instrument. Empirical validation on real-world longitudinal datasets remains the primary future direction.