Simulating Resilience in Transaction-Oriented Networks

TL;DR

This paper investigates how network resilience and amplification effects influence systemic risk, using simulations of Erdos-Renyi networks to understand the impact of node capacity, load, and failures on network throughput and stability.

Contribution

It provides new insights into the nonlinear amplification of failures in transaction networks and quantifies how network parameters affect resilience and throughput.

Findings

Network throughput scales quadratically with node capacity.

Excessive load and node faults have similar, anticorrelated effects.

Resilience characteristics can inform optimal capacity planning.

Abstract

The power of networks manifests itself in a highly non-linear amplification of a number of effects, and their weakness - in propagation of cascading failures. The potential systemic risk effects can be either exacerbated or mitigated, depending on the resilience characteristics of the network. The goals of this paper are to study some characteristics of network amplification and resilience. We simulate random Erdos-Renyi networks and measure amplification by varying node capacity, transaction volume, and expected failure rates. We discover that network throughput scales almost quadratically with respect to the node capacity and that the effects of excessive network load and random and irreparable node faults are equivalent and almost perfectly anticorrelated. This knowledge can be used by capacity planners to determine optimal reliability requirements that maximize the optimal operational regions.