Uniting Product Management And Governance
This article is in response to a Smart Contract Research Forum podcast on Computer-Aided Governance. Link is here: https://www.smartcontractresearch.org/t/scrf-interviews-computer-aided-governance-michael-zargham-and-jeff-emmett-ep-18/2587
The podcast episode and article are thought-provoking and insightful to what BlockScience does. Though I’ve never used cadCAD, I see it used all the time in the Token Engineering Commons. I now feel I have a better understanding of what it’s used for and why we would need computer assistance in governance. I also appreciated UmarKhanEth’s response and his example using Ray Dalio’s feedback-enhanced investment strategy. It’s interesting to see how various industries came across the power of closed-loop systems and what innovations sprang from them. I particularly want to address current issues in product management and how computer-aided governance can be used to strengthen this process.
Ever since entering the web3 space, I’ve noticed quality value creation has been at the center of critics’ debate. Questions like “why would I use a crypto wallet?” or “what makes web3 better than what we have today?” have implied a need for outstanding improvements in web3 when plotted against web2. I think product management is a field that can experience outsized results from web3 business models. My reasoning is similar to BlockScience’s in that any great decision comes from a well-informed source. Today, product managers conduct user interviews, research, outreach, and even paid time slots to get authentic feedback from customers. The Computer-Aided Governance Map and Process resembles a design thinking process of sorts. My main idea is what if you could bundle product feedback and iteration with the governance process?
DAOs must create value in order to survive. The largest DAOs today are those that help govern and maintain DeFi protocols with high TVL. The most successful DAOs are DAOs that have a product. Tying product management with governance seems almost inevitable when looking at many governance forums for large DAOs today. By tying in product management with governance, DAOs can make higher-quality decisions and design better products & iterations.
A product manager can enable this by taking advantage of the Observe and Ask phases of the CAG map. Governance sentiments should be the focus here, but product reviews and feedback can also be useful to gather. For this example, we can use Uniswap. There’s a feature right now that is still being debated about enabling a new fee tier to swaps. With CAG, we can observe the forums and ask users about what people feel regarding this decision. During this stage, a product manager can easily work with the governance researchers to ask product-focused questions. “Would you use a 10% fee tier for a swap? Why or why not?” “How would you want this option presented to you?” “How does the current fee tier mechanism make you feel? Is there anything you don’t like about it?” And so on.
This way, in the Map and Model phases, the product manager can propose changes to the UI or even a new version of the protocol. These can be presented, debated upon, enacted, and monitored just like any other governance decision. However, these decisions won’t come from the inherent politics you get with subjective options; rather, they’ll come from the data-driven approach of a product-minded person who can show the community that the given direction is the right direction to go toward.
One of my favorite observations in the article is the assumption in many web3 communities that a consensus decision is by default a good one simply because the majority agrees. The same applies to product managers. I do appreciate how computer-aided approaches can help us get closer to the right answer, but what constitutes a “good” decision in a DAO? Moreover, how do we find the best decision in a DAO?
This is the frontier I think will encompass the next cybernetics modality waves as stated in the article. Using mathematical tools like decision trees and graphs to optimize DAO decision-making will be a well-sought-after tool. Until we can mathematically represent the decision matrix and payoffs of each decision, we’ll be stuck with “best guesses.” I have confidence these will improve over time as well.