Optimal teaming is integral to the success of any organization. Innovators and leaders at Parallax Advanced Research concur that the process of creating effective, efficient, and intelligent teams requires careful consideration and systematic organization.
One example of the above is a graduate program in the Department of Psychology at Wright State University that Parallax facilitates in partnership with General Dynamics. The program involves computer engineering students, who work for General Dynamics and are attending classes as part of a graduate program at the Department of Psychology, to gain advanced knowledge of human-machine teaming. This collaboration also illustrates Parallax’s approach to incorporating and fostering intelligent teaming in diverse contexts.
Dr. Darrell Lochtefeld, Parallax division manager of Research, Development, Testing, and Evaluation elaborates on the meaning of the Science of Intelligent Teaming™.
According to Parallax Advanced Research Director of Cognitive Research, Dr. Mary Frame, who works on the human elements of human machine teaming, the concept of intelligent teaming in the scientific context originated with the cognitive revolution at least as far back as the 1950s. However, the way Parallax imbibes the concept and the extent to which the organization incorporates it across its operations is novel.
To further illustrate the role of Science of Intelligent Teaming™ in research done at Parallax, Dr. Othalia Larue, Computational Cognitive scientist at Parallax, uses an example from her work on modeling of individual differences that aims to capture the cognitive profiles of people to predict their response to automation.
All major decisions at Parallax are structured around strengths and opportunities for individuals in the organization. The Science of Intelligent Teaming™ affects how and where new employees are placed and considers that each addition to or subtracted from the team results in both new strengths and new weaknesses. In the past few years, with constantly evolving scenarios for collaborations, the definition and demands of teaming have changed rapidly. To keep up with these changes, it is critical to have a robust system of assessing and developing efficient and dynamic teaming methods.