There are a lot of moving parts in the Computable protocol, and it can be hard to keep track of all the different actors in the protocol. As a quick summary, here are the core actors and their respective roles:
- Creator: The entity who initially deploys the contracts for a given market. The creator may also choose to transfer some amount of money into the reserve to help set the initial price. The creator may be motivated by altruism or by profit motive.
- DataTrust Operator: The entity who hosts the server (“datatrust”) that actually stores the listing data associated with the market.
- Patrons: External parties who hold ETH and wish to support a particular data market.
- Maker: A participant who gathers data and submits it in the market in return for being granted partial ownership of the market.
- Buyer: A third party who’s interested in gaining access to the data in the data market and is willing to pay to make the purchase.
At a very crude fashion, the economic life cycle of a market can be modeled as follows:
- Creator creates the market and seeds the reserve with initial funds
- Creator specifies a DataTrust operator
- Patrons come fund a market and gain partial ownership in the data market.
- Makers are drawn into the market and contribute labor to create new listings for the market.
- Buyers are drawn by the listing data and purchase data. The revenue from the purchase is split between the makers, the data market reserve, and the datatrust operator.
We’ve put together a simple IPython notebook that lays out this economic simulation. The IPython notebook allows you to adjust the market parameters up top, and run through the notebook to see the effect on economic outcomes for all parties involved. Reassuringly, the simulation shows that reasonable choices of parameters lead to healthy returns for market participants. You can find the notebook here
It’s worth emphasizing that this simulation makes assumptions about the presence of healthy demand for the gathered data. These assumptions may or may not be realistic for a given data market. If you’re considering launching your own data market, I’d highly recommend doing careful analysis to understand the market demand and making a call on whether it justifies the effort. The outcomes that occur in a real data market may differ considerably from outcomes that you might see in a simple model such as this one.