Vast resources are devoted to predicting human behavior in domains such as economics, popular culture, and national security, but the quality of such predictions is often poor. Thus, it is tempting to conclude that this inability to make good predictions is a consequence of some fundamental lack of predictability on the part of humans. However, recent work offers evidence that the failure of standard prediction methods does not indicate an absence of human predictability but instead reflects: 1. misunderstandings regarding which features of human dynamics actually possess predictive power 2. the fact that, until recently, it has not been possible to measure these predictive features in real world settings. This talk introduces some of the science behind these basic observations and demonstrates their utility in various case studies. We begin by considering social groups in which individuals are influenced by the behavior of others. Correctly identify and understanding the social forces in these situations can increase the extent to which the outcome of a social process can be predicted in its very early stages. This finding is then leveraged to design prediction methods which outperform existing techniques for predicting social network dynamics. We also look at the analysis of the predictability of adversary behavior in the co-evolutionary "arms races" that exist between attackers and defenders in many domains. Our analysis reveals that conventional wisdom regarding these co-evolving systems is incomplete, and provides insights which enable the development of predictive methods for computer network security.