Hi @RogerS , we hear your constructive feedback and always welcome thoughts from the community.
To clarify, at a high level Gauntlet’s Risk Management platform quantifies risk, optimizes risk parameters, runs economic stress tests, and raises the alarm when needed. These are the decisions that Wall Street failed to make in the 2008 financial crisis.
In late January 2022, for example, there was a market downturn. Over the months preceding, we have largely been raising the capital efficiency of all of the protocols we work with. In light of this, none of our clients experienced any meaningful insolvencies even though some assets crashed by more than 50%. This is how our platform makes robust tradeoffs between risk and capital efficiency.
While working to accomplish this primary objective, we actively participate in the most impactful ways to the community, including providing analysis on lowering MKR’s borrow cap, working with OpenZeppelin and the FRAX team on listing new assets, and providing a market risk analysis on TUSD. We prioritize whatever is most valuable to the community from a market risk perspective, and we receive such feedback via user studies. We note the examples you mentioned above and apologize that we were not able to respond to you as soon as you would have liked - moving forward, we’ll certainly try to be more expeditious while also balancing your requests against other needs of the protocol.
Roger, you mention transparency. Your point around verifying data for market risk reports is well taken. We have been thinking through how to make the metrics publicly available to the community. As for explainability of models, this question speaks to a common issue in AI/ML systems (or complex systems in general). For complex systems, like Gauntlet’s agent-based simulations, there are many ingested features that drive our simulations in non-linear ways. Although we will not open-source our intellectual property, to increase transparency we published our Parameter Recommendation Methodology, Model Methodology, and Deep Dive on Value-at-Risk to explain our product in detail to the community. In every set of parameter recommendations, we also include rationale and relevant datasets that feed into our simulation platform.