For example, the CRISIS (Complexity Research Initiative for Systemic Instabilities) project is a consortium of EU universities, private firms and policymaking institutions which is building large-scale calibrated models to capture sectoral behaviour, including between the economy and the financial system. 12 At national level, promising progress has also been made in developing ABM models of the housing market. For example, Geanakoplos et al (2012) develop a regional model of the US housing market, which does a much better job than aggregate macro-economic models in matching housing market patterns, both in in the run-up to, and following, the financial crisis. As part of its new research agenda, the Bank of England is working currently with the Institute for New Economic Thinking (INET) at Oxford to try and develop an ABM model of the UK housing market. 13 As with Geanakoplos et al (2012), the aim is to provide a calibrated, granular model which better captures the dynamics of the housing market. It could also, potentially, be used to help evaluate the efficacy of macro-prudential interventions in that market – for example, policies which alter mortgage loan-to-income or loan-to-value limits. A third promising area of research concerns the interaction the macro-prudential and macroeconomic layers of the system – that is, the interplay between macro-prudential and monetary policy tools. This is another priority area for the Bank’s new research agenda. Although these policies have distinct primary objectives, their transmission channels are likely to be closely interwoven, with both affecting risk-taking and economic activity.

These dynamic properties include non-linearity; discontinuities in responses to shocks; amplifying feedback effects; and so-called “emergent” system-wide behaviour which is difficult to predict from the behaviour of any one element. These properties of complex systems typically give rise to irregular, and often highly nonnormal, statistical distributions for these systems over time. This manifests itself as much fatter tails than a normal distribution would suggest. In other words, system-wide interactions and feedbacks generate a much higher probability of catastrophic events than Gaussian distributions would imply (Newman (2005), Gabaix (2009)). They may also result in distributions which are multi-modal, consistent with models of multiple equilibria (Bisin et al (2011)). The topology and wiring of these complex systems appears, perhaps predictably, to have a crucial bearing on their resilience to shocks. Many complex networks have been found, in practice, to exhibit a “scale-free” property (Barabasi and Albert (1999)). That is to say, they comprise a core set of nodes with a large number of connections and a large set of peripheral nodes with few connections. There is a core-periphery, or hub-and-spokes, network configuration. This scale-free property has been found in everything from food webs to the World Wide Web, from eco-systems to economic systems, from synapses to cities, from social networks to financial networks (Jackson (2010)). These scale-free topologies have important, if subtle, implications for system resilience. For example, core-periphery models have been found to be very robust, at a systemic level, to random shocks.