Hey Matt, Thank you for the kind words. Chaos theory is more in line with the way we think about our models and the heuristic inputs that drive them & the market as a whole. It is very similar to standard Bayesian stats but as a practitioner there is no definitive solution to this practice when it comes to markets. We are not modeling out Lorenz attractors for each condition. As the "fractals" or factors in the model change it impacts our probability assumption towards the trade. In areas we believe the fractals are large, this creates a shorter path dependency to the outcome, which in turn is a focus for us because although the probability assumption may not technically change, it means there is an easier path for us to get the outlier event we are focused on. Ex: Imagine a chain with 100 small links, then imagine a chain with only 4 large links. We are firm believers that as this market shifts to passive, the reach for passive and structured products actually takes away from market breadth. Think about how many people's 401ks/ pension funds/ ETF basket/ Mutual fund/ Exotic note, is all tied to the performance of AAPL. The reach for diversification has actually led to a lack of diversification as the strength of market moves is now being driven by about 10 large core names. U.S equities as a whole would tumble if any of the large powerhouse names were to ever have any real single stock issue. Couple the lack of true diversification in with the fact that the main market participants + liquidity providers are all driven by a few large names and you can slowly start to see how these things can lead to excess chaos as investors constantly underestimate the distribution of potential outcomes at extreme events. Hopefully this helps KS

Hey Kris - thanks, that makes sense. I was thinking it would be tricky to model Lorentz attractors. Conceptually it sounds brilliant. Cheers.