Abstract
Most risk analysis models systematically underestimate the probability and impact of catastrophic events (e.g., economic crises, natural disasters, and terrorism) by not taking into account interconnectivity and interdependence of risks. To address this weakness, we propose the Cascading Alternating Renewal Process (CARP) to forecast interconnected global risks. However, assessments of the model’s prediction precision are limited by lack of sufficient ground truth data. Here, we establish prediction precision as a function of input data size by using alternative long ground truth data generated by simulations of the CARP model with known parameters. We illustrate the approach on a model of fires in artificial cities assembled from basic city blocks with diverse housing. The results confirm that parameter recovery variance exhibits power law decay as a function of the length of available ground truth data. Using CARP, we also demonstrate estimation using a disparate dataset that also has dependencies: real-world prediction precision for the global risk model based on the World Economic Forum Global Risk Report. We conclude that the CARP model is an efficient method for predicting catastrophic cascading events with potential applications to emerging local and global interconnected risks.
Highlights
We discuss how the limits of prediction precision can be established for a specific system as a function of the input data size by simulating the Cascading Alternating Renewal Process (CARP) model with known parameters to generate many alternative ground truth datasets of arbitrary length
The CARP model is a novel method in which interdependencies and interconnections are explicitly represented, and the model parameters are recovered from historical data using Maximum Likelihood Estimation
Unlike real-life, in simulations, we can arbitrarily vary the length of time over which we collect historical data and produce many variants of such data to measure the prediction precision of our model
Summary
We discuss how the limits of prediction precision can be established for a specific system as a function of the input data size by simulating the CARP model with known parameters to generate many alternative ground truth datasets of arbitrary length. Confidence level, typically “tail” outcomes)[25] for catastrophic events such as economic crashes, natural disasters, and terrorist attacks[6] Underestimation in such models is due to speciously assuming the sequences of risk materializations can be represented by normal, independent, and identically distributed (IID) random variables[7,8] discounting the potential of interdependencies and interconnections between events. The ARP can be used to find the best strategy for replacing worn-out machinery[17,18]
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