Using Monte Carlo Simulations for Disaster Preparedness

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The Next Step

Problem: You've used quantitative risk analysis to quantify the likelihood, and impact, of a climate change disaster. What's the next step?

Tip: Now it's time to model your response to climate change — create a data set with a range of potential adaptation options or responses, which can be analyzed for cost-efficiency and likelihood of effectiveness. Adaptation measures can have high capital and ongoing maintenance costs, so businesses and organizations need to demonstrate value for money and a return on their investment. Thus, optimization techniques are crucial — they allow you to determine the timing and scale of mitigation measures, ensuring that any commitments to capital expenditures will have a likely maximum return on investment.

A recent report, "Risky Business: The Economic Risks of Climate Change in the United States," co-chaired by Michael R. Bloomberg, Henry Paulson and Tom Steyer, suggests that "by 2050 between $66 billion and $106 billion worth of existing coastal property will likely be below sea level nationwide, with $238 billion to $507 billion worth of property below sea level by 2100."

Additionally, the U.S. National Research Council recently suggested the necessity of a "national vision" that will take precautionary, rather than reactionary, approaches to flooding, particularly in the Atlantic and Gulf coasts, where water has reached flood levels an average of 20 days per year since 2001.

These reports, however, don't address what businesses should do to protect themselves from the increase of extreme weather changes or how to take on that task. For quite some time, solutions that incorporate a method known as Monte Carlo simulation (MCS) have been used to examine probability of weather risk and the financial impact those risks may produce.

So what is Monte Carlo simulation? MCS performs risk analysis by building models of possible results by substituting a range of values — a probability distribution — for any factor that has inherent uncertainty. It then calculates results over and over using a different set of random values from the probability functions.

Depending upon the number of uncertainties and the ranges specified for them, a Monte Carlo simulation could involve thousands or tens of thousands of recalculations before it is complete.

By using probability distributions, variables can have different probabilities of different outcomes occurring. Probability distributions are a much more realistic way of describing uncertainty in variables of a risk analysis. In this presentation, Randy Heffernan, vice president, Palisade, addresses common problems organizations may face when developing a model for weather risk, utilizing MCS.

 

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