Designing complex systems often requires consideration of many components interacting across vast scales of space and time, thus producing highly challenging design spaces to search. In particular, nano-based technologies may require considerations of how nanoscale (10-9) embodiments affect macroscale (∼100) systems and typically have multiple layers of emergent behavior. It is frequently cited that counterintuitive properties of emergence complicates design tasks; however, we investigate whether some multiscale emergent systems have organizational levels that may inform more effective design methods and searches. Investigations are conducted by extending an agentbased simulation that predicts the emergent interactions of myosin motors interacting with a motile actin filament. Both the behaviors of individual motors and an ensemble of motors are stochastic, therefore analytical methods are often unable to form accurate design evaluations and computationally intensive simulations are required for investigation. Our modification of the simulation enables the prediction of the duration of time that motors will carry an actin filament before system dissociation (termed its processive life-time), which is a vital performance metric for future nanotechnology designs such as molecular sorters. Virtual experiments were conducted that determines how perturbations of synthetic myosin design configurations, and the number of myosins present, affects emergent ensemble performance with respect to run-length and energy usage. It is found that all systems have nearly identical average processive life-times for a given input energy regardless of how much energy individual myosins utilize. Such a finding reduces the total information that a designer must consider, since it is a fundamental relationship that holds regardless of lower level component configurations. Such relationships may occur in complex systems in additional domains, and knowledge of these emergent relationships could greatly facilitate the efficiencies of design methods and automated searches for future technologies.