Background: Sustainability assessment commonly remains retrospective, providing limited capacity to anticipate behavioral deterioration, resilience shifts, or the effects of ESG-related shocks. Building on the Alaali Behavioral Sustainability Index (A-BSI) and the Alaali Expected Behavioral Efficiency Index with Adaptive Intelligence, this study develops the Alaali Behavioral Sustainability Forecasting Model (A-BSF), the eighth analytical model in the Alaali Self-Funded Behavioral Model (AFMF-SFBM) series. Methods: The study develops a conceptual simulation-based forecasting framework that integrates behavioral durability, financial resilience, governance alignment, environmental integration, behavioral volatility, and ESG shocks. The model introduces Behavioral Forecast Confidence (BFC), which combines relative forecast error and behavioral forecast entropy, and the Dynamic Resilience Function (DRF), which represents post-shock recovery capacity. Illustrative Monte Carlo simulations (10,000 iterations) were conducted using Bayesianupdated input assumptions across alternative behavioral and ESG conditions. The reported accuracy measure represents the relative agreement between the model forecast and the simulated benchmark outcome; it does not constitute validation against real-world historical observations. Results: Under the simulated high-sustainability condition-behavioral durability coefficient (?d) above 0.80 and behavioral volatility below 0.20- the model produced simulation forecast accuracy between 93% and 97%. The results indicate, within the model's simulated conditions, that higher behavioral durability supports forecast stability, whereas greater behavioral volatility reduces forecast confidence and ESG shocks alter projected sustainability trajectories. These findings are illustrative and require subsequent empirical validation using longitudinal organizational data. Discussion: A-BSF shifts behavioral sustainability analysis from descriptive measurement toward structured predictive assessment. Its contribution is a transparent simulation architecture for examining how behavioral resilience, uncertainty, and ESG disturbances may interact over time. The framework is proposed as a decision-support and research instrument rather than as an empirically validated forecasting system. Future research should calibrate its parameters and test its predictive performance across organizations and sectors.
Behavioral sustainability; sustainability forecasting; Monte Carlo simulation; behavioral forecast confidence; dynamic resilience; ESG shocks; organizational resilience; behavioral managerial finance; AFMF-SFBM.