Forecasting Israel-Iran Escalation Bands with Structured Judgment Using Artificial Intelligence Algorithms

This study estimates the near-term risk of renewed Israel–Iran escalation using a modular, multi-model framework that converts expert judgments and open-source indicators into probabilistic scenarios over a 90-day horizon. We elicit LOW–CENTRAL–HIGH priors from domain experts across 18 indicators spanning air/missile, land, maritime, cyber, diplomacy, information, and proxy alignment (including Israeli standoff and air-strike activity), then fuse evidence via Bayesian updating and infer a weekly escalation state on a seven-rung ladder with a light stocks/flows scaffold for magazines, interceptor use, and repair/re-supply constraints. We triangulate forecasts across three complementary stacks (Bayesian state-space, statistical ensemble, and game-theoretic signalling). At the analysis cut-off (2025-10-22, UK), All three models agree that S1 (Managed Conflict) is modal, S2 (Northern War with Maritime Squeeze) is the principal alternative, and S3–S4 remain lower-probability tails. Sensitivity analysis reveals that the S1<->S2 margin is most responsive to air/missile defence saturation and combined launch/strike pressure together with maritime war-risk stress, with mediation activity providing the strongest stabilising counterweight. We also formalise tail-risk triggers for potential state fracture and specify how crossing them would reweight S4. The result is a transparent, updateable, and non-partisan forecast designed for decision support: it communicates where risk mass sits, what could move it, and which levers plausibly bend trajectories while avoiding operationally sensitive detail.