On May 6, 2010, the US equity markets experienced a brief but highly unusual drop in prices across a number of stocks and indices. The Dow Jones Industrial Average (DJIA) fell by approximately 9% in a matter of minutes, and several stocks were traded down sharply before recovering a short time later. Earlier research by Lee, Cheng and Koh (2010) identified the conditions under which a “flash crash” can be triggered by systematic traders running highly similar trading strategies, especially when they are “crowding out” other liquidity providers in the market. The authors contend that the events of May 6, 2010 exhibit patterns consistent with the type of “flash crash” observed in their earlier study (2010). While some commentators assigned blame to high-frequency trading, the authors’ analysis was unable to identify a direct link to high-frequency trading per se, but rather the domination of market activities by trading strategies that are responding to the same set of market variables in similar ways, as well as various pre-existing market micro-structural safety mechanisms with unintended consequences when triggered simultaneously. The consequent lack of market participants interested in the “other side” of their trades may result in a significant liquidity withdrawal during extreme market movements. This paper describes the results of 9 different simulations created by using a large-scale computer model to reconstruct the critical elements of the market events of May 6, 2010. The resulting price distribution provides a reasonable resemblance to the descriptive statistics of the second-by-second prices of S&P500 e-Mini futures from 14:30 to 15:00 on May 6, 2010. There are no a priori assumptions made on asset price distributions, and our description of market dynamics is purely based on the structure of the market and the key types of market participants involved. This type of simulation avoids “over-fitting” historical data, and can therefore provide regulators with deeper insights on the possible drivers of the “flash crash”, as well as what type of policy responses may work or may not work under comparable market circumstances in the future. Our results also lead to a natural question for policy markers: If certain prescriptive measures such as position limits have a low probability of meeting its policy objectives on a day like May 6, will there be any other more effective counter measures without unintended consequences?