This provides a powerful secular incentive for non-decomposable socio-economic systems. Moreover, if these hyper-connected networks do face systemic threat, they are often able to adapt in ways which avoids extinction. For example, the risk of social, economic or financial disorder will typically lead to an adaptation of policies to prevent systemic collapse. These adaptive policy responses may preserve otherwise-fragile socio-economic topologies. They may even further encourage the growth of connectivity and complexity of these networks. For example, policies to support “super-spreader” banks in a crisis may encourage them to become larger, and more complex, still over time (Haldane (2009)). The combination of network economies, and policy responses to failure, means socio-economic systems may be less Darwinian, and hence decomposable, than natural and biological systems. It is against this backdrop that a complex, socio-economic “system of systems” may emerge. This can defined as one comprising an interlocking set of individually complex webs ((Gorod et al (2014)). The system of system concept initially emerged for engineering and enterprise systems, which involved the multi-layered assembly of component parts. But it has since found its way into a number of other domains including military planning, ecological evolution, power grids, transport networks and neurological structures (Gao et al (2014)). Although still in its infancy, there are some general properties of a “system of systems” perspective that are worth bringing out. For example, Kurant and Thiran (2006) look at the behaviour of a particular topology – a layered complex network.

Cross-border interbank lending flows fell by around 20% between 2008Q2 and 2010Q2. The largest crossborder banks – the “super-spreaders” – appear to have played a central role in instigating and propagating this global contagion (Hills and Hoggarth (2013)). (e)  Correlations among sub-systems  Another dimension when gauging risks to the macro-financial system is the correlation between layers of the system. For correlations between individual banks and the financial system as a whole, one metric is provided by looking at the common component within the joint distribution of bank equity prices. For example, the first principal component of UK bank equity prices accounts for around 70% of the overall movement in bank equities. At the time of the financial crisis, this common component rose to nearer 90%. These correlations are high and highly crisis-sensitive, suggesting a close link between individual and systemic bank risk. The picture is similar when looking at correlations between developments in the financial system and the wider economy. For example, the pairwise correlation between the financial and non-financial components of UK equity prices is high, at around 0.8–0.9. The picture is similar if we move from the national to the international level: the first principal component of international equity prices accounts for around 80–90% of the variation. Each of these  11  This analysis uses the Bank for International Settlements bilateral locational statistics. This does not capture flows between periphery countries, so there is a risk that it overstates the importance of the core.