William F. Lawless
We are producing an innovative theory to make a group of autonomous systems as intelligent as possible to serve as surrogate human decision makers in a hybrid group of humans and machines for a variety of purposes to include countering human interpretive and decision-making deficiencies. This theory could help design systems for UxVs and computational alternatives to prevent or mitigate military incidents. Unfortunately, unlike the physical sciences, the social sciences have no overarching theory of fundamental principles to build from individuals to collectives. To move into a future governed effectively and efficiently by humans interacting with smart systems, we propose a scalable model of human behavior based on the mathematical physics of interdependent uncertainty. Unlike building a bridge or robot, while predictions about social behavior remain possible, our interdependent model of uncertainty for collectives finds traditional explanations of decisions unavoidably incomplete that makes the search for understanding largely “meaningless”, a blow to traditional theories (bounded rationality, game and social learning theory). We address whether incompleteness can be exploited to provide a computational alternative perspective to make better decisions under uncertainty.