Emergence and emergent behaviors are often defined as cases where changes in local interactions between agents at a lower level effectively changes what occurs in the higher level of the system (i.e., the whole swarm) and its properties. However, the manner in which these collective emergent behaviors self-organize is less understood. The focus of this paper is in presenting a new framework for characterizing the conditions that lead to different macrostates and how to predict/analyze their macroscopic properties, allowing us to indirectly engineer the same behaviors from the bottom up by tuning their environmental conditions rather than local interaction rules. We then apply this framework to a simple system of binary sensing and acting agents as an example to see if a re-framing of this swarms problem can help us push the state of the art forward. By first creating some working definitions of macrostates in a particular swarm system, we show how agent-based modeling may be combined with control theory to enable a generalized understanding of controllable emergent processes without needing to simulate everything. Whereas phase diagrams can generally only be created through Monte Carlo simulations or sweeping through ranges of parameters in a simulator, we develop closed-form functions that can immediately produce them revealing an infinite set of swarm parameter combinations that can lead to a specifically chosen self-organized behavior. While the exact methods are still under development, we believe simply laying out a potential path towards solutions that have evaded our traditional methods using a novel method is worth considering. Our results are characterized through both simulations and real experiments on ground robots.