Award-Winning Researchers Work to Predict Human Behavior and Emotions

Award-Winning Researchers Work to Predict Human Behavior and Emotions

By La Monica Everett-HaynesUniversity Communications
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Young-Jun Son and Seungho Lee
Young-Jun Son and Seungho Lee

Turning speculation into predictability is the objective of a human decision-making model University of Arizona researchers have developed – an effort that has earned international recognition for the team.

Young-Jun Son, an associate professor in systems and industrial engineering at the UA, worked with one of his doctoral students, Seungho Lee, to create a computer model to predict how groups and even individuals will react during emergency situations, such as a major fire or a bombing.

"Individual behavior is not like ant behavior," Son said. "We want realistic human behavior. And if you look at our model, it is applicable for individual behavior."

Theirs is the type of research that can help law enforcement and first responders make better decisions and improve real-time planning and decision making during a crisis.

"We thought we could make a model that would mimic human decision making," Lee said. "It's not just about assuming what would happen, but we want to understand the psychological behaviors, including the emotions people would have, including fear and fatigue. And we claim that we can."

Son and Lee took the Best Paper Award in the area of homeland security at the Institute of Industrial Engineers Annual Conference this month for their paper, titled "Integrated Human Decision Behavior Modeling Using Extended Decision Field Theory and Soar Under BDI Framework." Son and another team of his students earned the same award in 2005.

The international institute, the world's largest professional society dedicated to industrial engineering, allowed 700 papers to be presented during the conference in Vancouver, Canada.

"To homeland security, this is very important," Son said. "I think that's why we got a lot of attention from the judges."

Lee, a doctoral degree candidate in systems and industrial engineering, took the conference's Best Ph.D. Scientific Poster Award.

Researchers have for some time studied intelligence, cognition and how humans make decisions, but have tended to focus on one of three different models: engineering, psychological and economical. Yet Son and Lee created a model that integrates them all in an attempt to advance the way researchers evaluate both human decision-making processes and planning.

In their paper, Son and Lee expanded on the Belief-Desire-Intention system, which is used to predict the future actions of humans.

"We believe that in the details of the BDI there are a lot more details that can address planning and human characteristics," Son said. "BDI is the guideline structure so as time goes on, we need to add more and more human structures so that 20 or 30 years later our human decision model will get closer to the human model."

For now, the model "is very abstract," Son said. "We needed to develop a model with a lot more detail."

During the UA team's analysis, individuals were asked to record their perceptions of different emergency situations in the Cave Automatic Virtual Environment, a configuration of screens at University Information Technology Services.

Using the CAVE allowed the researchers to study the ways that participants would react to situations such as the presence of smoke or a fire.

The team then tried to determine how actions would change under certain conditions, such as the presence of police of the intensity of the crowd.

Son and Lee used the data collected from the answers to make a virtual model, mapping individual perceptions to create a group environment in a given situation.

The researchers then loaded the responses into a computer simulation model, testing different configurations that would not only determine how groups would react but also how individuals would react. For example, a computer considers a number of factors and determines the direction in which people would run after they came to an intersection.

Son and Lee said additional research is required to understand the exact emotional response people would have in any given situation and the team continues to work on the project. For instance, they want to study in greater details how an individual's confidence relates to them doing what they plan to do, or changing their mind.

Such a model would help determine how many police officers would be needed, how to predict evacuation times, how to estimate the number of casualties and how to get information to survivors.

In the paper, the team wrote: "The proposed simulation has a potential to allow the responsible governmental and law enforcement agencies to evaluate different evacuation and damage control policies beforehand, which in turn allows the execution of the most effective crowd evacuation scheme during an actual emergency situation."

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