(Helbing 2013). The most famous of these occurred on 6 May 2010 when the Dow Jones Industrial Average dropped by 998.5 points in twenty minutes– the largest intraday point drop in history at that time – and then recovered 600 points. The cause of the sudden swing was attributed in part to a spoofing scheme by Navinder Sarao, a London based equity trader. Sarao is alleged to have manipulated the price of near-month E-mini S&P 500 futures contract by layering the sell side of the order book with large quantities of non-executable orders at nonmarketable prices. Recent analysis suggest instead that the 2010 flash crash was instead a form of self-organized criticality driven by systemic structures that were vulnerable to feedback loops with automated trading systems (e.g. Aldrich, et al. 2016). Additionally, some speculate that because of these issues underlying the system, future crashes were inevitable, and in fact seen with rapid fluctuations that have occurred since. Findings after the event include that many of the mechanisms of the system are not well understood (Madhavan 2012). An extensive analysis can be found in Kirilenko et al. (2017).

Self-organized criticality can also be particularly important in the study of failure in interdependent infrastructures. In systems such as these, large catastrophic events can occur as a consequence of the same dynamics that produce small ordinary events. Examples of this have been seen in power grids which operate with a dynamic a balance between production and load. If the balance is not quickly reestablished after disturbances (planned and otherwise) generator and load breakers will trip to keep from damaging components. This process can evolve to cause cascading outages that can last for varying degrees of time. Exactly this was seen in a 2009 blackout in Brazil that the Itaipu hydroelectric facility to shutdown (e.g. Carlotto and Grzybowski 2014). Analyses of grid blackouts from several countries show that the frequency of large events follows a power-law distribution (e.g. Dobson et al. 2007) which is consistent with self-organized criticality (Helbing 2013). If such situations are to be avoided in lifeline infrastructures, steps must be taken during the design phase, or mitigation procedures put in place to address a failure before it cascades out of control (Perrow 1999; Bak and Paczuski 1995).

The study of resiliency has its roots in the study of ecological systems and their ability to ‘bounce back’ after a disturbance or at least maintain a certain level of function (e.g. May 1971) Although widely recognized as a serious concern, how to mitigate the risks posed by infrastructure interdependency and complexity remains a nascent area of research (Turner et al. 2003; Bocchini Paolo et al. 2014; Schweikert, Espinet, and Chinowsky 2018). With few exceptions, even quantifying the linkages between infrastructures, their interdependencies, or failure mechanisms, has rarely been done (World Economic Forum 2011; Helbing 2013; Little 2002). This paper, commissioned by the World Bank’s Global Facility for Disaster Reduction and Recovery division, provides a primer on the vulnerabilities of networked energy infrastructure. This work reviews literature related to these subjects and how they may vary geographically. The work is broken into a discussion of electricity transmission and supply chains with subsequent conclusions and recommendations for areas of near-term research to address ongoing work in this field.

# **ELECTRICITY TRANSMISSION**

There are multiple components to understanding whether a power grid will be resilient to a 6