Network theory in risk assessment

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.

In reality, however, it is nearly impossible to break the iron triangle among politicians, scientists (in this case, risk assessors), and advocates and media.

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.

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.

[10] Network modeling and studying have already been applied in many areas, including computer, physical, biological, ecological, logistical and social science.

For example, connections in a social network affect how people communicate, exchange news, travel, and, less obviously, spread diseases.

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 (that is, the number of edges the email message has to pass from one unique individual to the intended targets in different countries) for the experiment was around five to seven, which is not much deviated from the original six degree of separation.

Contagious diseases can spread through connection networks such as work space, transportation, intimate body contacts and water system (see Figure 7 and 9).

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.

Figure 1: A bow-tie diagram of components in a directed network
Figure 2: Risk Analysis, evaluation, assessment, and management
Figure 3: Bow-tie diagram of risk management
Figure 4: A small network with both multiedges and self-edges
Figure 5: A disconnected directed network with two components (shaded)
Figure 6: A connected directed network with two components (shaded)
Figure 7. An example of acyclic directed network in epidemiology by CDC.
Figure 8. East River Valley Trophic Web
Figure 9. Chain of transmission among guests at Hotel M -- Hong Kong, 2003