said. "Even though that's pretty big, that's still a smaller arena than what CDI is dealing with, where you've got not only some big bucks, but also the lives of many people."

The ACS gaming engine software adds unpredictability to disaster training because the course of the simulation is influenced by participants' decisions.

"It's not unpredictable in the sense that there's randomness," Chussil said. "It's unpredictable in that the model, like the real world, is sufficiently complex that no person can figure out with any degree of certainty exactly what's going to happen."

Contrast that with a typical disaster-response training simulation, which follows a script. Participants rehearse their roles, and everybody knows what's coming and builds reflexes in dealing with particular scenarios.

"If that situation changes, then we either apply inappropriate reflexes or we don't know what to do," Chussil said. "When we train with a system that has the realism and complexity -- so people don't know ahead of time what the right answer is -- that's when they learn. When they play [the simulation] again and again, over multiple days, that's when people get insight, as opposed to habit."

Similar to the famed "butterfly effect" in weather patterns -- the notion that the flapping of a butterfly's wings in California can have an impact on weather in China -- the disaster scenario evolves as participants make decisions.

"Econometric" Impact

The CDI also is working with Sandia National Laboratories to inject Sandia's economic consequence model into the September training event, Hendricks said.

Using GIS, Sandia National Laboratories researchers developed a computer model based on Portland's physical infrastructure -- including roads, buildings, telephone lines and bridges -- that calculates the economic consequences if a piece of infrastructure is destroyed or damaged during simulation.

Economic modeling allows leaders to immediately see how decisions that harm physical infrastructure during a disaster can impact their community's long-term economic recovery.

One example was what Hendrick's heard during Los Angeles' first Homeland Security Leadership Summit, about challenges Detroit faced after the blackout of 2003 -- especially the economic after-effects that may not be apparent at the time of a crisis.

"If businesses aren't open for business, they're not generating revenue," Hendricks said. "If they're not generating revenue, they're not generating tax revenue. That's a significant long-term impact on local and state, and oftentimes, the federal government."

He said he also heard that during a 96-hour period, millions of dollars in customs tariffs weren't collected at the Canadian and U.S. border, because a computer system that assessed tariffs on goods coming across the border was down due to the blackout.

The Sandia model will help add a sense of realism to the September exercise that would not be present otherwise, he said.

"As the simulation unfolds, their econometric model will be chugging along in parallel," Hendricks said. "When we get to the 'hot wash' or the debrief of each of these training experiences, we'll be able to say, 'Unbeknownst to you, while you were managing the operational side of this event, there's a direct impact to the economy, short term and long term, and here's what it is.'"

Perhaps the biggest benefit of economic modeling combined with the gaming engine is how quickly participants will grasp the entirety of a disaster event, he said, especially the aftermath.

"They really understand what can happen, and what they have to do to be as prepared as possible, and to come out the other side in their recovery phase with as little impact on their individual organizations and their communities as possible."

Shane Peterson  |  Associate Editor