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, <a href="/facts/Mathematical_model/CGhyuxgz">modeling</a> 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 – <a href="/facts/Mathematics/pxTouaz4">mathematical</a>, <a href="/facts/Computation/qLsELosq">computational</a>, and <a href="/facts/Statistics/DNPsKGYU">statistical</a> – that are well-developed and if understood can be applied to the analysis of the system of interest.

Tools that are currently employed in <a href="/facts/Risk_assessment/vlG9Uf0h">risk assessment</a> are often sufficient, but model complexity and limitations of computational power can tether risk assessors to involve more causal connections and account for more <a href="/facts/Black_swan_theory/jcmfwymP">Black Swan</a> event outcomes. By applying <a href="/facts/Network_theory/m6CQovwm">network theory</a> 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 <a href="/facts/Confirmation_bias/o5rHY4lP">confirmation bias</a> 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 <a href="/facts/Iron_triangle_(US_politics)/N5cljgvS">iron triangle</a> 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.
<a href="/facts/Bow-tie_diagram/SISHey6g">Bow-tie diagrams</a>, <a href="/facts/Cause-and-effect_diagram/vHFlT4dk">cause-and-effect diagrams</a>, <a href="/facts/Bayesian_network/Ry31vq6T">Bayesian networks</a> (a directed acyclic network) and <a href="/facts/Fault_trees/rTD7Xd0t">fault trees</a> 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 <a href="/facts/Keystone_species/JE5sSY3X">keystone species</a> and determine how widespread the impacts will extend from the potential hazards being investigated.

Network theory in risk assessment open-in-new

Network theory in risk assessment