Under the estimated distributions for economic and financial systems, such an event would occur every 10 to 15 years. Implications for policy design So what might this mean for the design of the policy architecture? Let me mention three areas where clear progress has been made since the crisis, but where more may be needed in the period ahead if we are to manage effectively this complex, macro-financial system of systems: data, modelling and policy design. (a)  Data  Monitoring risk in a system of systems requires data on each of the sub-systems, often at a high level of granularity. Often in response to crisis, efforts have been made over time to improve data on economic and financial systems. For example, there have been successive waves of improvement to statistics on international banking flows, starting in the 1970s. In response to the latest crisis, renewed efforts have been made to fill data gaps and to improve the degree of disclosure by banks of their balance sheet positions (FSB (2012)). As one example, data on banks’ cross-border assets and liabilities only distinguish between banks and non-banks. In practice, a richer sectoral decomposition of these data is often desirable to understand interactions between different parts of the financial sector and between it and the wider economy (Hills and Hoggarth (2013), Lane (2014)). For example, the dollar funding crisis which faced European banks in 2011/12 involved a complex string of cross-border financing.
If the world involves three layers of complexity, then missing a layer will lead to a significant underestimation of risk. This, too, has a parallel with the pre-crisis situation where the macroprudential layer was essentially ignored by both micro-prudential and macro-economic policymakers. This led both to under-estimate the policy risk they faced. Pulling this together, what are the public policy implications which follow from this complex system of systems perspective? First, it underscores the importance of accurate data, and timely mapping, of each layers in a system of systems. This is especially important when these layers are themselves complex. Granular data is needed to capture the interactions within and between these complex sub-systems. Second, modelling of each of these layers, and their interaction with other layers, is likely to be important, both for understanding system risks and dynamics and for calibrating potential policy responses to them. Third, in controlling these risks, something akin to the Tinbergen Rule is likely to apply. There is likely to need to be at least as many policy instruments as there are complex subcomponents of a system of systems if risk is to be monitored and managed effectively. Put differently, an under-identified complex system of systems is likely to result in a loss of control, both system-wide and for each of the layers. The architecture of macro-financial systems How, then, does this theory relate to real-world, macro-financial systems? These systems are likely to contain many moving parts.