Good prediction is necessary for autonomous robots to make informed decisions in dynamic environments. These predictions can be generated by models whose parameters are estimated from sampled sensory data. The sampling strategy for collecting this data strongly impacts realized model performance. Methods for curiosity incentivize exploration based on mathematical proxies for expected information gain which can be used to perform targeted sampling. These curious approaches to choosing actions allow a framework for learning predictive models which is not tied to accomplishing specific tasks enabling higher model generalization performance. In this presentation, I will present our recent research on curiosity starting with simulation results in video games. Then, I will present a robotic domain transfer problem in which we improve prediction performance with the use of curiosity-driven decision making. I will also present our preliminary work analyzing the effect of curiosity-driven decision making on multiagent system dynamics in a competitive resource allocation problem.