Evolution of the Global Risk Network Mean-Field Stability Point

TL;DR

This paper analyzes the evolution of the global risk network's stability points over time using a CARP model, revealing how global threats and their interconnections impact economic stability and identifying causal relationships between risks.

Contribution

It introduces a novel application of steady state analysis in the global risk network using a CARP model, providing insights into the evolution and causality of global risks.

Findings

Steady state differences highlight changing global risk challenges over time.

Causal analysis distinguishes between correlated and causative risk relationships.

Mathematical and simulation methods validate the network evolution insights.

Abstract

With a steadily growing human population and rapid advancements in technology, the global human network is increasing in size and connection density. This growth exacerbates networked global threats and can lead to unexpected consequences such as global epidemics mediated by air travel, threats in cyberspace, global governance, etc. A quantitative understanding of the mechanisms guiding this global network is necessary for proper operation and maintenance of the global infrastructure. Each year the World Economic Forum publishes an authoritative report on global risks, and applying this data to a CARP model, we answer critical questions such as how the network evolves over time. In the evolution, we compare not the current states of the global risk network at different time points, but its steady state at those points, which would be reached if the risk were left unabated. Looking at the steady states show more drastically the differences in the challenges to the global economy and stability the world community had faced at each point of the time. Finally, we investigate the influence between risks in the global network, using a method successful in distinguishing between correlation and causation. All results presented in the paper were obtained using detailed mathematical analysis with simulations to support our findings.