Software helps time-stressed people make better decisions

New software can help people make better decisions in time-stressed situations
Human teams aided by a software system can make decisions more accurately and quickly in time-stressed situations than teams of just people, according to the Penn State researchers who developed the new software.

The researchers tested their software in a military command-and-control simulation which involved intelligence gathering, logistics and force protection. When time pressures were normal, the human teams functioned well, sharing information and making correct decisions about the potential threat, according to the researchers. But when the time pressure increased, the teams’ performance suffered, according to the researchers. Because there was no time to share information, the teams made incorrect decisions about whether to avoid or attack the coming aircraft.

“This is the first test of the R-CAST architecture, and it shows that software agents can play an essential role in helping human partners make the right decision at the right time,” said Xiaocong Fan, a post-doctoral scholar in Penn State’s School of Information Sciences and Technology (IST) and lead author.

The results of the experiment are described in a paper, “Extending the Recognition-Primed Decision Model to Support Human-Agent Collaboration,” presented today (July 29) by John Yen at the Fourth International Joint Conference on Autonomous Agents and Multi-Agent Systems in Amsterdam. Co-authors are Shuang Sun, a doctoral student in information sciences and technology; Michael McNeese, associate professor of information sciences and technology; and Yen, the University professor of information sciences and technology.

In the simulation, team members had to protect an airbase and supply route which were under attack by enemy aircraft. The scenarios were configured with different patterns of attack and at different tempos. The situation was complicated because team members had to determine at first if the aircraft were neutral or hostile. Furthermore, two team members were dependent on the third whose role was to gather information and communicate it to them.

“When the teams don’t know if the incoming aircraft is the enemy, the defense team can’t attack, and the supply team takes action to avoid the incoming threat which causes a delay in delivery,” Sun said. “These decisions lower the performance of the whole team.”

When the information gatherer was supported by the researchers’ R-CAST software system, the information was gathered and shared more quickly. As a result, the human-agent teams were better able to defend themselves from enemy attack and deliver supplies without delay, Sun said.

The researchers also learned that in contrast to human teams whose performance suffers from increased tempos, the software enables human teams to better maintain their performance at an acceptable level.

While the simulation involved a military scenario, people on distributed teams in other areas such as emergency management operations also need information to make decisions in stressful situations. This software agent can help team members share information as well as identify salient information in uncertain environments.

The software is based on the recognition-primed decision (RPD) model which posits that people make decisions based on their recognition of similarities between past experiences and current situations. Earlier research showed this model addresses situations where there is little or no time for extensive reasoning.

The software, R-CAST, supports collaborative activities among teammates, comprised of both humans and software systems. Penn State has filed for a patent on the software methods and architecture embodied in R-CAST.

From Penn State

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