CARP Model for Multi-Risk Dynamics

Epidemic modeling has been studied for nearly a century, with most work focused on compartmental models where a single disease spreads amongst a large number of carriers. However, risks threatening modern societies form an intricately interconnected network infecting one carrier. Surprisingly little is known about how risk materializations in distinct domains influence each other. We present an approach in which expert assessments of likelihoods and influence of risks underlie a quantitative model of global risk dynamics. Using maximum likelihood estimation, we find the optimal model parameters and demonstrate that the network model significantly outperforms others, uncovering the full value of the expert crowd-sourced data. We analyze model dynamics and study the model resilience, stability and asymptotic trajectory. Our findings elucidate the identity of risks most detrimental to system stability at various points in time. The model provides quantitative means for measuring the adverse effects of risk interdependencies and the materialization of risks in the network and is shown to have a similar mean field approximation to that of traditional epidemiological models such as SIR.