A Causal Model Approach to Dynamic Control
Abstract
Acting effectively in the world requires learning and controlling dynamic systems, that is, systems involving feedback relations among continuous variables that vary in real time. We introduce a novel class of dynamic control environments using the continuous Ornstein-Uhlenbeck process united with causal Markov graphs that allow us to systematically test people’s ability to learn and control various dynamic systems as they change in real time. We find that people’s control is robust to changing goals, and exhibits heterogeneity of performance in different environments that matches closely with complexity defined by our optimal model. These results suggest people are capable learners of dynamic systems, able to leverage a rich representation of their environment to accomplish their goals.