FWIW @karmaticacid @Griff @mitch @Suga
I’ve been recycling the same $500 donation on Kiva since 2014.
I used to manually reloan the funds and at a certain point Kiva implemented a learning algorithm about my preferences for loaning and it looked something like this:
- If recipient is female/mother
- and loan is within 90% to funding
- then fully fund
I mention this because my giving style is to finalize and make opportunity real for the person seeking funds. I also know that the field of token engineering can be used for more than just an incentivize/bribe rewards. In fact I know token engineering can be used to model the flow of user behavior that optimizes for specific kinds of users/behavior.
I’m sharing this bc my hope is either through a governance process or smart contract implementation that GIVForwards can begin to inform a credit allocation algorithm for users who actually put their Giv to use rather than simply stake to reward themselves. Simply said too much thinking about what rewards degens instead of regen user types.
I scanned the GIVmatch link that @WhyldWanderer shared and found myself wanting to see the diagram/flows and outputs that are informing the parameters being considered.
I know Giveth as a brand has been around since 2017 but I don’t know that I’ve ever come across a simple white paper w/o the branding that maps out the token supply over the ecosystem and participants it’s intended to serve.
It’s possible I may have missed a miro board so please someone surface it for me. I’m basically trying to see the value flow and outputs of GIVmatch, GIVreward and GIVforward. To me they’re all a form of GIVpower and I wanna visually understand how the game theory behind each of those circulates when modeled out. Kinda like an electric circuit.
Lastly I wanna share that I admire all that everyone has done to steer the Giveth ship to this place because it is a remarkable platform/product. Stay epic ya’ll and ty @rainer.hoell @qqsong for opening this can of worms.
