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Can a math equation solve your disaster?

Most companies simply make best guesses to predict the outcome of future disasters

By Dian Schaffhauser
April 14, 2008 12:00 PM ET

Computerworld - When disaster looms, who you gonna call? It could increasingly be a mathematician, if IBM scientists succeed in one of their current research efforts.

IBM announced last week that its scientists have created specialized math algorithms to help model and manage disasters. The Stochastic Optimization Model is being used to address disaster scenarios, including the management of resources to battle giant wildfires and grapple with pandemics. Eventually, the model could be applied to solving large, seemingly intractable problems such as how to improve the American health care system, as well as to more modest business challenges, such as scheduling limousine service. The result, according to IBM, is more-accurate insight into what needs to be done to survive a disaster or work through a business problem.

The Stochastic Optimization Model provides a framework for addressing problems that involve uncertainty or randomness, similar to game theory and discrete event simulation, according to Gartner Inc. research director John Morency. All models involve examining reasonable probabilities based on current and past events but add in a measure of randomness to examine how participants may respond depending on how the situation changes or evolves.

IBM began developing the model in 2003 for a consortium of government agencies that were responsible for fighting forest fires. As Baruch Schieber, senior manager of IBM's optimization center, explains, the government's firefighting budget was easily in the hundreds of millions of dollars. The agencies in charge -- including the Bureau of Land Management, the Bureau of Indian Affairs, and the U.S. Forest Service and other agencies under the auspices of the U.S. Department of Agriculture -- wanted help deciding where to position their resources, including personnel and equipment, without knowing in advance where the fires would be.

The project, called Fire Program Analysis, involves developing scenarios and plans to address myriad possible situations in which the probability of occurrence is high and where the loss could be the greatest.

"The question is, what's the best way to do this planning so you get the biggest bang for your buck?" says Schieber, whose group is part of the Business Analytics and Mathematical Sciences Department at the T.J. Watson Research Center in Yorktown Heights, N.Y.

IBM's Global Business Services division worked with its math scientists as well as the government agencies and Colorado State University's Warner College of Natural Resources to create a sampling of scenarios to which a probability was applied, taking into account variables such as seasonal rainfall levels and how much damage to agriculture or structures a fire might cause.

But that was only part of the challenge, says Schieber. "If I just give the government agency or budget planner this list of all these probabilities, it would be hard for them to deduce what needs to be done." The next step, he says, is to design the optimal policy for allocating resources.

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