Nassim Taleb (Taleb 2010) labels black swans with the following attributes: rarity, impact, and retrospective apparent predictability. However, Benoit Mandlebrot (Wright 2007) claims gray swans are events of considerable nature, which are predictable and for which one can take precaution. Admiral Nimitz is quoted as stating, “The war with Japan had been enacted in the game rooms at the War College by so many people and in so many different ways that nothing that happened during the war was a surprise—absolutely nothing except the kamikaze tactics toward the end of the war.â€� Surely, this may have been a black swan from his perspective. This paper addresses three questions posed at the “2015 I/ITSEC Black Swan Kickoff.â€� 1. How do we prepare, organize, train and equip for Black Swan resiliency? 2. How can Modeling and Simulation be used to analyze and prepare or create a Black Swan? 3. Can we develop complex adaptive models and simulation tools that will enable the analysis? The authors will follow Dr. Mandelbrot's assertion to answer the first question. For the second and third questions, we will outline a procedure to use modeling and simulation with prescriptive analytics to reduce the potential intractability of black swans, thus demoting their status to gray. With frequency histograms and curve-fitting, we first show how distributions with thin-tails don't fully account for risk, while fat-tail distributions better fit extremely rare event data. Then, by applying Percent Point Functions and stochastic optimization techniques to a Monte Carlo simulation of fat-tailed distributions, we show which configuration of input parameters creates a black swan. Given our approach, we offer an analytics method to evaluate black swan events and downgrade them to gray swan events.