What is complex systems’ role in augmented cognition? Neurotechnology—especially electroencephalography (EEG), brain-computer interface (BCI), and neural networks—have seen extensive use in augmented cognition (AugCog). Functional near-infrared spectroscopy (fNIRS) was also used in [5], and functional magnetic resonance imaging (fMRI) in [6]. To what extent can such technologies address the total complexity of augmented cognition? EEG data complexity [2] and “complex structures” [1] are topics that were taken up in AugCog research. While there has been some attention paid to complex systems in recent augmented cognition research [3, 7, 8], their relation to AugCog could be better-determined. Complex systems display emergent properties arising from interdependent parts and aggregate behavior, contain stochasticity, are multistate, and operate autonomously. Complex systems has arisen recently given research and industrial interests. It is hypothesized now that augmented cognition is a complex system, with its interactive parts being humans and computers. As humans interact with computers and vice versa, augmented cognition ideally arises. This augmented cognition consists of extended performance, presumably-distinct neurophysiological signatures, and possibly enhanced phenomenology. Such a phenomenology may reinforce the human-computer interaction, behaviorally. It is an open question whether augmented cognition, if it is a complex system, gives rise to emergent properties currently undocumented. AugCog (on its own or as a complex system) can begin to be quantified using a formula provided in this chapter. A variable X can be added to this equation as a multiplier, though it is not currently known to what extent AugCog extends performance, alters neurophysiology, or enhances phenomenology overall. In this chapter, the following question is taken up: Is augmented cognition a complex system that gives rise to emergent properties other than extended performance, unique neurophysiology, or enhanced phenomenology?