The Global Financial Markets: An Ultra-Large-Scale Systems Perspective

We argue here that, in recent years, the global financial markets have become a complex adaptive ultra-large-scale socio-technical system-of-systems, and that this has important consequences for how the financial markets should be engineered and managed in future. The very high degree of interconnectedness in the global markets means that entire trading systems, implemented and managed separately by independent organizations, can rightfully be considered as significant constituent entities in the larger global super-system: that is, the global markets are an instance of what is known in the engineering literature as a system-of-systems (SoS). The sheer number of human agents and computer systems connected within the global financial-markets SoS is so large that it is an instance of an ultra-large-scale system, and that largeness-of-scale has significant effects on the nature of the system. Overall system-level behaviour may be difficult to predict, for two reasons. First, the constituent (sub-) systems may change their responses over time, either because they involve human agents as key “components” within the system (that is, the system is actually socio-technical), or because they involve software systems that evolve over time and “learn from experience” (that is, the system is adaptive). Second, even when given perfect knowledge of the constituent systems that combine to make up the SoS, the overall system-level behaviour may be difficult or impossible to predict; that is, the SoS may exhibit emergent behaviour. For these reasons, the global financial markets SoS can also rightly be considered as a complex adaptive system. Major failures in the financial markets SoS can now occur at super-human speeds, as was witnessed in the “Flash Crash” of May 6th 2010. Events such as the Flash Crash may become more commonplace in future, unless lessons are learned from other fields where complex adaptive socio-technical systems of systems have to be engineered for high-integrity, safety-critical applications. In this document we review the literature on failures in risky technology and high-integrity approaches to safety-critical SoS engineering. We conclude with an argument that, in the specific case of the global financial markets, there is an urgent need to develop major national strategic modelling and predictive simulation capabilities, comparable to national-scale meteorological monitoring and modelling capabilities. The intent here is not to predict the price-movements of particular financial instruments or asset classes, but rather to provide test-rigs for principled evaluation of systemic risk, estimating probability density functions over spaces of possible outcomes, and thereby identifying potential “black swan” failure modes in the simulations, before they occur in real life, by which time it is typically too late.