Network theory in risk assessment


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, 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.
A "bow-tie" diagram, cause-and-effect diagram, Bayesian network and fault trees are few examples of how network theories can be applied in risk assessment.
In epidemiology risk assessments, 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 or high-traffic places. In ecological risk assessments, 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:
  1. Plan and prepare the risk analysis.
  2. Define and delimit the system and the scope of the analysis.
  3. Identify hazards and potential hazardous events.
  4. Determine causes and frequency of each hazardous event.
  5. Identify accident scenarios that may be initiated by each hazardous event.
  6. Select relevant and typical accident scenarios.
  7. Determine the consequences of each accident scenario.
  8. Determine the frequency of each accident scenario.
  9. Assess the uncertainty.
  10. Establish and describe the risk picture.
  11. Report the analysis.
  12. Evaluate the risk against risk acceptance criteria
  13. 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” 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, connections or interactions between those components, as well as the pattern of connections.
Undoubtedly, modifications of the structure 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

Degree, Hubs, and Paths
Centrality
Components
Directed Networks
Weighted Network
Trees

Other Examples of Network Theory Application

Social network

Early social network studies can be traced back to the end of the nineteenth century. However well-documented studies and foundation of this field are usually attributed to a psychiatrist named Jacob Moreno. He published a book entitled Who Whall Survive? in 1934 which laid out the foundation for sociometry.
Another famous contributor to the early development of social network analysis is a perimental psychologist known as Stanley Milgram. His "small-world" experiments gave rise to concepts such as six degrees of separation and well-connected acquaintances. This experiment was recently repeated by Dodds et al. by means of email messages, and the basic results were similar to Milgram's. The estimated true average path length for the experiment was around five to seven, which is not much deviated from the original six degree of separation.

Food web

A food web, or food chain, is an example of directed network which describes the prey-predator relationship in a given ecosystem. Vertices in this type of network represent species, and the edges the prey-predator relationship. A collection of species may be represented by a single vertex if all members in that collection prey upon and are preyed on by the same organisms. A food web is often acyclic, with few exceptions such as adults preys on juveniles and parasitism.

Epidemiology

is closely related to social network. Contagious diseases can spread through connection networks such as work space, transportation, intimate body contacts and water system. Though it only exists virtually, a computer viruses spread across internet networks are not much different from their physical counterparts. Therefore, understanding each of these network patterns can no doubt aid us in more precise prediction of the outcomes of epidemics and preparing better disease prevention protocols.
The simplest model of infection is presented as a SI model. Most diseases, however, do not behave in such simple manner. Therefore, many modifications to this model were made such as the SIR, the SIS and SIRS models. The idea of latency is taken into accounts in models such as SEIR. The SIR model is also known as the Reed-Frost model.
To factor these into an outbreak network model, one must consider the degree distributions of vertices in the giant component of the network. Theoretically, weighted network can provide more accurate information on exposure probability of vertices but more proofs are needed. Pastor-Satorras et al. pioneered much work in this area, which began with the simplest form and applied to networks drawn from the configuration model.
The biology of how an infection causes disease in an individual is complicated and is another type of disease pattern specialists are interested in.