Abstract
Modern society heavily relies on strongly connected, socio-technical systems. As a result, distinct risks threatening the operation of individual systems can no longer be treated in isolation. Consequently, risk experts are actively seeking for ways to relax the risk independence assumption that undermines typical risk management models. Prominent work has advocated the use of risk networks as a way forward. Yet, the inevitable biases introduced during the generation of these survey-based risk networks limit our ability to examine their topology, and in turn challenge the utility of the very notion of a risk network. To alleviate these concerns, we proposed an alternative methodology for generating weighted risk networks. We subsequently applied this methodology to an empirical dataset of financial data. This paper reports our findings on the study of the topology of the resulting risk network. We observed a modular topology, and reasoned on its use as a robust risk classification framework. Using these modules, we highlight a tendency of specialization during the risk identification process, with some firms being solely focused on a subset of the available risk classes. Finally, we considered the independent and systemic impact of some risks and attributed possible mismatches to their emerging nature.
Highlights
An enhanced understanding of the nature of risk is the epitome of modern science (Bernstein 1996; Buchanan and O’Connell 2006), with its successful management yielding significant benefits across a wide range of societal facets (Ganin et al 2016; Helbing 2013; Vespignani 2012)
Working toward capturing risk interdependence in a more robust way, we developed a methodology to generate weighted risk networks based on risk similarity, where risks are connected based on the similarity of their characteristics
We analyze the topology of the risk network, focusing on its modular composition (see the supporting information (SI) in the online appendix www.risk.net/journals
Summary
An enhanced understanding of the nature of risk is the epitome of modern science (Bernstein 1996; Buchanan and O’Connell 2006), with its successful management yielding significant benefits across a wide range of societal facets (Ganin et al 2016; Helbing 2013; Vespignani 2012). In this context, risk is traditionally defined as the “effect of uncertainty on objectives”; it is generally quantified as the probability of an event materializing multiplied by its expected impact (International Organization for Standardization 2009). Incorporating the effect of interdependence into the risk management process has attracted much recent interest (Battiston et al 2012; Battiston et al 2016a; DasGupta and Kaligounder 2014; Helbing 2013; Roukny et al 2013; Szymanski et al 2015), partly due to the 2007–8 global financial crisis and the way in which traditional risk models, grounded in the assumption of risk independence, failed to foresee it (Battiston et al 2016a,b; Besley and Hennessy 2009; Schweitzer et al 2009)
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