By one estimate, attention-related cognitive disorders cost the U.S. economy $143 to a $266 billion annually. As WSRI we know the cognitive and social sciences are key to understanding the human element in any complex system. Our cognitive and social science research portfolio cuts across multiple focus areas – from human performance and human-machine teaming to LVC and autonomy – and spans topics ranging from individual to group-level behaviors.
At the individual level, our cognitive science research includes efforts aimed at understanding and preventing attentional lapses in real-world settings. Such lapses of attentional control, while seemingly benign, can have devastating consequences in both civilian and military contexts.
In addition to understanding fundamental cognitive processes like attention, several of our efforts reside at the intersection of human and machine cognition. Our Hybrid Forecasting Competition project seeks to combine human analytic judgments with predictive algorithms to improve the ability of US intelligence analysts to accurately forecast global events. More broadly, our Human-Centered Big Data effort aims to make Big Data analytic algorithms such as deep neural networks more transparent to human users, thereby rendering them more trustworthy.
WSRI’s social science research includes work on the so-called “wisdom of crowds” effect – a phenomenon in which the average judgment of a group of individuals (e.g., a team of intelligence analysts) is shown to exceed that of any one group member. In order for a crowd to be wise, a key requirement is that its members possess diverse knowledge and viewpoints. Our research has shown that it is possible to quantify a crowd’s “cognitive diversity” by analyzing its social network structure and communication patterns, and to then use this information to construct wiser crowds.
Finally, our research team has recently embarked on a multi-year project to develop large-scale computational simulations of complex social phenomena, such as refugee crises, for use in testing the robustness and validity of current social science modeling methods. Results of these efforts will enable our government sponsors to better understand and predict human social dynamics and to develop more effective policy interventions.