Return to Investment/Stake
Participation in GoraNetwork as a Node Runner should be compensated as such work helps GoraNetwork to secure the data/computation demanded by consumers. The more node runners there are, the more decentralised GoraNetwork becomes. Node runners (aka validators) will be responsible for achieving consensus and proposing data/computation for consumers.
GoraNetwork rewards mechanism is designed to incentivize long term growth, as the more node runners that join, the more the system's capacity can grow to handle large number of use cases. The ecosystem rewards will be continuously increasing each month, so node runners that join after a year are still able to gain a good share of the rewards. In years 1-2, adoption incentives will be added for early adopters, at a time when ecosystem rewards are low. In years 2-5 , the consumer fees should be high enough to begin compensating feed providers.
The rewards model works by supplementing the consumer fees earned by node runners at the end of each month using the following formula:
where i is the ith node runner, a is a constant, m is the number of months since TGE, and C is the consumer fees earned by the ith node runner.
Additionally, to incentivize the adoption of GoraNetwork early on and light the fire for mass adoption, GoraNetwork will add a node runners 3 month apy on top of rewards earned by node runners if they stake early on and remained staked for at least 3 months. The return percentage depends on the month they join - 20% for the first 6 months, 15% for the subsequent 6 months and then 10% for the next 6 months. See initial distribution protocol for more info.
Return to investment mainly revolves around node runners expecting a return, over a certain period, to their stake. There are also those who delegate tokens to pools or operators of Nodes with large stakes to obtain a share of the rewards.
In order to assess how ROI could look over a year is tricky, especially for a new project and one built on Algorand due to the randomness of entering the committee. One approach to obtain a view of how a fixed system could perform is by using Monte Carlo simulations and survey data. The below plot is a simulation ran by randomly sampling the survey data weighted by stake and then passing them through Algorand's sortition process. After that, their voting behaviour is simulated using the binomial distribution and the rewards model hands out tokens to those who participated successfully in a request.
It is important to note that the above plot represents a fixed conclusion using a variety of assumptions. It is by no means a complete and accurate representation of real life but does give an indication of what returns could look like. It also does not mean if you break up your stake that you would receive significantly higher rewards due to the fact that the rewards pool is fixed and each of your broken stakes does not have the same probability or share of the committee size as the original stake.
What is interesting is the more equitable nature of the rewards model and this could result in a large number of nodes with what we can consider the average stake (~ 2000 to 3000 GORA) becoming the backbone of the node runner community.
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