A Multi-Domain Stochastic Framework for Forecasting Catastrophic Risk from Artificial Intelligence Development Through 2030
Ibrahim Elchami · Team AI Forecasting with more realistic geopolitical climate
Submitted to The AI Forecasting Hackathon. Sprint projects are early-stage work by participants, not Apart Research publications.
We present a novel integrated forecasting framework that synthesizes five distinct statistical methodologies: Solow-Romer endogenous growth theory for compute scaling, Generalized Extreme Value theory for capability forecasting, Nash equilibrium game theory for geopolitical dynamics, Computable General Equilibrium economics, and Hawkes self-exciting point processes for catastrophic risk assessment. The assessment is to project AI-related systemic risks through 2030. Employing Monte Carlo simulation with 500 trajectories per scenario, we evaluate four policy intervention frameworks: baseline (no coordination), global coordination, Western alliance (excluding China), and unilateral US regulation. Our findings indicate that under baseline assumptions, the cumulative probability of at least one catastrophic AI incident by 2030 reaches 26% (90% CI: 18-35%). Here is a 2 minute demo of the app https://youtu.be/9om7_DtjQ2w
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@misc{elchami2025multidomain,
title = {{A Multi-Domain Stochastic Framework for Forecasting Catastrophic Risk from Artificial Intelligence Development Through 2030}},
author = {Ibrahim Elchami},
year = {2025},
month = nov,
note = {Submitted to The AI Forecasting Hackathon, an Apart Research Sprint},
howpublished = {\url{https://apartresearch.com/sprints/projects/a-multidomain-stochastic-framework-for-forecasting-catastrophic-risk-from-artificial-intelligence-development-through-2030-wzzl}},
url = {https://apartresearch.com/sprints/projects/a-multidomain-stochastic-framework-for-forecasting-catastrophic-risk-from-artificial-intelligence-development-through-2030-wzzl}
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