The Evolutionary Theory of Behavior Dynamics (ETBD) is a theory of adaptive behavior based on the idea that behavior is shaped by the consequences that follow it in a process similar to Darwinian natural selection. It operates by instantiating rules inspired by natural selection on a population of behaviors in a computer-based algorithm, and therefore constitutes a complex systems theory. Complex systems rely on the interaction of relatively simple rules to produce an outcome; however, the outcome often cannot be predicted from the rules. The ETBD uses Darwinian-inspired rules of selection, reproduction, and mutation to animate artificial organisms that can be put into virtual experimental environments similar to a living organism. How the artificial organism behaves in a virtual environment is the theory’s prediction for how a living organism would behave under similar conditions. The theory can make two types of predictions. First-stage predictions are those that align with what was known prior to the theory’s development and provide evidence supporting a theory by ensuring it aligns with existing data. Second-stage predictions pertain to phenomena that have not been tested with live organisms. These predictions are especially important for the theory because they put it at greater risk of falsification but can also result in new discoveries. This chapter provides a comprehensive review of the ETBD’s predictions, both first- and second-stage, and hopes to inspire future research that continues to develop and test these predictions.