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Make a model, explore a problem

Professors across campus collaborate

phone signals

Complexity modeling helps make sense of volumes of data on anything from cell phone signals to frog migration. 

Whether predicting how a disease will spread through a population or tracking the transmission of cell phone signals, researchers today have access to more data than ever before. But making sense of it is simple: Use complexity modeling. 

“Complexity modeling is a mathematical representation of real-world problems,” says Ray Semlitsch, a biological sciences professor and founder of a new group of complexity modelers across campus. 

It’s long a favorite tool of civil and environmental engineers, who use it to estimate things such as how a bridge closure will affect traffic. Now, many other disciplines are using complexity modeling is now being used in many disciplines to analyze patterns and predict change—how brain signals would travel through the nervous system if a synapse was disabled, for instance. 

To generate the predictions, a modeler first creates an equation that represents the research problem, based on the information that exists and the information that is needed. Then, data are entered into the equation. The data can come from observational studies, experiments or researchers’ educated guesses, depending on the situation. A high-powered computer — hundreds of times faster than an average consumer laptop — runs the equation up to a million times over, each time with a different variable, to create a simulation of a real-life scenario. 

Semlitsch gives an example from his research in population ecology: He could create an equation that describes the distribution of one frog species over a thousand acres of  land and then input the data he has collected about the frog’s migration pattern. The computer could then predict the probability of that species moving from one area to another if, say, a large swath of forested area was cut down.

After tapping into computer modeling six years ago, Semlitsch discovered that faculty across campus were using the same technology and techniques. The group he founded includes 20 faculty from departments including engineering, anthropology, psychology, geology and life sciences. They meet monthly to discuss how modeling fits into their research. Because complexity modeling can predict the movement of anything from nerve impulses in the brain to cars on the road at rush hour, researchers can collaborate to address multifaceted problems, Semlitsch says.

“Now we can communicate across disciplines,” he says, “which is nice, because the world’s problems are much broader than one discipline.”

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