A network is an abstract structure capturing only the basics of connection patterns and little else. Because it is a generalized pattern, tools developed for analyzing, modeling and understanding networks can theoretically be implemented across disciplines. As long as a system can be represented by a network, there is an extensive set of tools – mathematical, computational, and statistical – that are well-developed and if understood can be applied to the analysis of the system of interest.
Tools that are currently employed in risk assessment are often sufficient, but model complexity and limitations of computational power can tether risk assessors to involve more causal connections and account for more Black Swan event outcomes. By applying network theory tools to risk assessment, computational limitations may be overcome and result in broader coverage of events with a narrowed range of uncertainties. Decision-making processes are not incorporated into routine risk assessments; however, they play a critical role in such processes. It is therefore very important for risk assessors to minimize confirmation bias by carrying out their analysis and publishing their results with minimal involvement of external factors such as politics, media, and advocates. In reality, however, it is nearly impossible to break the iron triangle among politicians, scientists (in this case, risk assessors), and advocates and media. Risk assessors need to be sensitive to the difference between risk studies and risk perceptions. One way to bring the two closer is to provide decision-makers with data they can easily rely on and understand. Employing networks in the risk analysis process can visualize causal relationships and identify heavily-weighted or important contributors to the probability of the critical event. Bow-tie diagrams, cause-and-effect diagrams, Bayesian networks (a directed acyclic network) and fault trees are few examples of how network theories can be applied in risk assessment. In epidemiology risk assessments (Figure 7 and 9), once a network model was constructed, we can visually see then quantify and evaluate the potential exposure or infection risk of people related to the well-connected patients (Patient 1, 6, 35, 130 and 127 in Figure 7) or high-traffic places (Hotel M in Figure 9). In ecological risk assessments (Figure 8), through a network model we can identify the keystone species and determine how widespread the impacts will extend from the potential hazards being investigated.
Risk assessment key components
Risk assessment is a method for dealing with uncertainty. For it to be beneficial to the overall risk management and decision making process, it must be able to capture extreme and catastrophic events. Risk assessment involves two parts: risk analysis and risk evaluation, although the term “risk assessment” can be seen used indistinguishable with “risk analysis”. In general, risk assessment can be divided into these steps:
Plan and prepare the risk analysis. Define and delimit the system and the scope of the analysis. Identify hazards and potential hazardous events. Determine causes and frequency of each hazardous event. Identify accident scenarios (i.e. even sequences) that may be initiated by each hazardous event. Select relevant and typical accident scenarios. Determine the consequences of each accident scenario. Determine the frequency of each accident scenario. Assess the uncertainty. Establish and describe the risk picture. Report the analysis. Evaluate the risk against risk acceptance criteria Suggest and evaluate potential risk-reducing measures. Naturally, the number of steps required varies with each assessment. It depends on the scope of the analysis and the complexity of the study object. Because these is always varies degrees of uncertainty involved in any risk analysis process, sensitivity and uncertainty analysis are usually carried out to mitigate the level of uncertainty and therefore improve the overall risk assessment result.
Network theory key components
A network is a simplified representation that reduces a system to an abstract structure. Simply put, it is a collection of points linked together by lines. Each point is known as a “vertex” (multiple: “vertices”) or “nodes”, and each line as “edges” or “links”. Network modeling and studying have already been applied in many areas, including computer, physical, biological, ecological, logistical and social science. Through the studying of these models, we gain insights into the nature of individual components (i.e. vertices), connections or interactions between those components (i.e. edges), as well as the pattern of connections (i.e. network). Undoubtedly, modifications of the structure (or pattern) of any given network can have a big effect on the behavior of the system it depicts. For example, connections in a social network affect how people communicate, exchange news, travel, and, less obviously, spread diseases. In order to gain better understanding of how each of these systems functions, some knowledge of the structure of the network is necessary.
Basic terminology Small-World Effect The small-world effect is one of the most remarkable network phenomena. It describes a finding that in many (perhaps most) networks the mean path distances between vertices are surprisingly small. It has many implications in various areas of network studies. For instance, in social network, one can ruminate how fast a rumor (or a contagious disease) is spread in a community. From a mathematical point of view, since path lengths in networks are typically scale as log n (where n = number of network vertices), it is only logical it remains a small number even with large complex networks. Another idea comes along with the small-world effect is called funneling. It was derived from a social network experiment conducted by the experimental psychologist Stanley Milgram in the 1960s. In that experiment he concluded, along with the small-world effect phenomenon, that in any given social network, there were always few that were especially well connected. These few individuals were therefore responsible for the connection between any members and the rest of the world. Degree, Hubs, and Paths
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