Using Monte Carlo Simulations for Disaster Preparedness

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Hidden Costs

Problem: Your business has estimated the total cost of lost assets in the event of a climate change disaster event. Is this enough planning?

Tip: Not necessarily. It's important to take into account all the 'hidden' costs when a disaster strikes. In the case of events such as storm surges or massive flooding, roads may be flooded and impassable for days, resulting in trucking and shipment detours and delays and thus increasing the cost of freight delivery. Shops and factories may stay closed for repairs — resulting in a loss of revenue, while employees will likely have to take leave — either paid or unpaid. It's vital to incorporate these more indirect costs into your risk analysis models when planning for climate change events.

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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