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Topic: HLS 6040 DF7 Comments: Week 7 - Benjamin Luckey, Dan Reed
Comment on these 2 postings as if you were talking in my voice. 100 words and 3 sources each:
Assessing risk is complex and not an exact science. However, Using risk models to aid in predicting future events/threats is extremely useful for emergency managers. By analyzing historical data analysts can identify correlations, and come up with a set of possible scenarios that can be ranked by likelihood percentage. By studying risk emergency managers can isolate threats/vulnerabilities that may have otherwise gone unnoticed/unaddressed. That said, whenever an individual is attempting to forecast future events there must be an ongoing effort to remain objective — this can best be accomplished by having peer reviews and open discussion to defend the logic behind the argument and conclusions. Bias is also a serious consideration — intentional bias, or a political leaning may compromise the integrity of the risk assessment (hence the peer review process). Additionally, accidental bias is an insidious obstacle to overcome, and emergency managers must continuously reevaluate their analysis from different perspectives (not simply from the perspective of an analyst or manager but from the perspective of different stakeholders and the public).
An example of such risk forecasting could be the utilization of historical records to look into hurricane patterns and then identify pieces of critical infrastructure that have been historically prone to issues as well as new infrastructure that may be in a high risk area. After gaining an understanding of where the high risk areas are, and what infrastructure is most vulnerable emergency managers can begin the mitigation process (building flood banks, examining drainage, looking into slopes/how to direct water, analyzing building materials, replacing compromised windows, designing backup power systems, establishing emergency safe zones, etc). Risk assessment and forecasting is extremely helpful and should continue to be developed and coupled with technology to offer emergency managers the greatest opportunity to design systems and plans to protect the public as well as critical infrastructure.
Week 7 Discussion - Dan Reed
Risk Mapping has the potential to predict the `likelihood` of future events, but not necessarily the events themselves. Predicting future conditions of any system encountering disruptive circumstances is difficult because it can be based on assumptions or underlying trends, such as which elements are most critical to supporting and operation. Bias is difficult to overcome and mindsets are extremely resistant to change. Identifying the dependencies and interdependencies between elements can be affected by these biases. If the interdependencies are not properly identified, the tool may be useless. Using a tool like risk mapping must be shaped to fit specific criteria and several important variables may be missed if not careful. Having the geospatial and virtual aspects of a threat, such as a natural disaster, or public health outbreak is not always enough to predict the future. Determining element relationships can be subjective because most entities can be connected to most or all other entities in some form. It is important to have enough historical data to accurately identify `normal` activity, or else identifying outlier events will prove much more challenging. Also, data needs to be updated regularly to reflect any changes to key resources or critical infrastructure, that would change the likelihood of future events. Attempting to predict the future will always remain a challenge, where assumptions must be clearly identified, and an understanding of the process. Risk Mapping will be able to aid in preparing individuals for public health and natural disaster potential, but not clearly predict the future.
HLS 6040 DF7 COMMENTS Name Institution Date Benjamin Luckey - week 7 Using a risk model indicates the vulnerability and other characteristics that influence risk, but models are effective when the assumptions made are reasonable and the information used presents a real case scenario (Valencia & Thal, 2012). Disasters like wildfires and floods are recurring events, and innovative methods to determine the likelihood and severity of threats (Faulkner, 2007). Taking into consi