DeFi Primitive Risk Methodology (DPRM)

“DeFi Primitive Risk Methodology (DPRM) is an open source risk management library (currently in development) that lets users perform both quantitative and qualitative risk analysis on groups of DeFi primitives using stochastic methods to simulate first and second order effects from any combination of tokenomics designs.”

Problem - Risk Management in DeFi is hard

In DeFi, a plethora of innovation is occurring with the creation of many new macro DeFi  primitives such as liquidity pools, bonds, and staking. Within these macro DeFi primitives exists a myriad of micro DeFi primitives. Together, the combination of these two groups of primitives create a composable set of tools for developers that allows for an infinite amount of creativity when designing novel tokenomics. Since most of the smart contracts are publicly verified, anyone can take primitives from various protocols and mix and match them to create something new. While this approach often creates novel systems, it’s often not always apparent that novelty translates to innovation. It’s difficult to understand the second order effects that each newly created group of primitives have, specifically in the context of financial risk management.

Currently the industry standard is to launch tokenomics systems without having completed robust financial risk analysis. Individuals shoulder all of the liabilities and responsibilities in determining financial risk and must use their best judgment and ‘DYOR’ to validate the tokenomics of a protocol before using it. The process of risk analysis requires considerable effort to perform. Unfortunately the average DeFi individual often does not have the resources and knowledge to answer important questions such as:

  • How do you characterize the risk in a tokenomics system?
  • How does the risk profile of a tokenomics system evolve over time?
  • What is the risk/reward for using this tokenomics system?
  • Is there a threshold in which the tokenomics system becomes overly diluted over time?
  • What is the probability that a tokenomics system can be manipulated?
  • How does investing in a new tokenomics system change the overall risk profile to an individual’s portfolio strategy?

In TradFi, financial institutions are regulated heavily by different standards to quantify financial risk in an effort to protect individuals and investors. For example the Basel Framework is a comprehensive set of global standards used to regulate banks. In the USA, there are federal regulatory bodies such as the Federal Trade Commission (FTC) and the Consumer Financial Protection Bureau (CFPB) whose primary responsibilities are to protect customers from being taken advantage of. The burden of risk management is put on the company, not the individual.

Although DeFi is built on the blockchain where information is transparent, this does not necessarily translate into information being accessible. In order to increase the adoption of DeFi, there needs to be more transparency and standards around the risks involved in using various tokenomics systems. Furthermore, the burden of risk management needs to be shifted from the individual to the protocol similar to TradFi.

What has been done in risk management so far?

Gauntlet networks is focused on building infrastructure to manage risk for blockchain and blockchain projects and is the leading provider of risk management services, covering over $40B in DeFi assets. Gauntlet leverages agent-based simulations, running thousands of simulations with different volatilities and parameters in order to optimize key parameters to improve capital efficiency, risk management, and incentive structures of a protocol.

While this is a great start, there are some limitations with this approach. First, any infrastructure related to risk management should be an open source public good that anyone can use when starting a new project or research and not proprietary software constrained to a subset of the DeFi ecosystem to maximize scalability. Second, any risk management methodology needs to be easy to use such that it lowers the barriers to entry for new projects and research while simultaneously encouraging a higher industry standard for risk management.

Introducing DPRM

DeFi Primitive Risk Methodology (DPRM) is a risk management framework that lets users perform both quantitative and qualitative risk analysis on groups of DeFi primitives using stochastic methods to simulate first and second order effects from any combination of tokenomics designs.

DPRM allows protocols to perform mathematically rigorous quantitative and qualitative risk analysis on tokenomics, shifting the responsibility of risk analysis from the individual to the protocol, making the risk analysis process more transparent. DPRM increases the speed of DeFi innovation by streamlining the ability to experiment and robustly test new tokenomics at scale and in real time via a microservice architecture approach.

There are three main parts to DPRM:

  • Microservices - The main advantage for using microservices is to decouple DeFi primitives into independent systems, modularity and upgradeability, ease of use.
  • Quantitative and Qualitative Analysis - Perform quantitative and qualitative analysis on both historical data or stochastic simulation data.
  • Visualization - monitor and visualize vital key metrics and be hook up metrics into any protocol frontend.

DPRM will be publicly released as an open source python library package (soon ™) and aims to set a higher industry standard for financial risk analysis for tokenomics systems as well as becoming a useful tool in the innovation stack. If anyone is interested in chatting and asking about progress on the DPRM library, DMs are always open.



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