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Most risk analysis models systematically underestimate the probability andimpact of catastrophic events (e.g., economic crises, natural disasters, andterrorism) by not taking into account interconnectivity and interdependence ofrisks. To address this weakness, we propose the Cascading Alternating RenewalProcess (CARP) to forecast interconnected global risks. However, assessments ofthe model's prediction precision are limited by lack of sufficient ground truthdata. Here, we establish prediction precision as a function of input data sizeby using alternative long ground truth data generated by simulations of theCARP model with known parameters. We illustrate the approach on a model offires in artificial cities assembled from basic city blocks with diversehousing. The results confirm that parameter recovery variance exhibits powerlaw decay as a function of the length of available ground truth data. UsingCARP, we also demonstrate estimation using a disparate dataset that also hasdependencies: real-world prediction precision for the global risk model basedon the World Economic Forum Global Risk Report. We conclude that the CARP modelis an efficient method for predicting catastrophic cascading events withpotential applications to emerging local and global interconnected risks.

Limits of Risk Predictability in a Cascading Alternating Renewal Process Model