Although this thread is primarily about dApp Staking and Tokenomics, Tokenomics itself has hardly been discussed so far, so I’d like to make a proposal on that as well.
Under Tokenomics 3.0, a maximum supply is planned, and the amount of ASTR issued per block is expected to decrease over time.
What if we add one additional “coefficient” on top of this?
At a fundamental level, the price of an asset fluctuates based on the balance between supply and demand. In simple terms, the reason ASTR’s price has continued to decline is that supply exceeds demand. Here, we can think of supply as inflation and demand as buyers.
This applies to the tokens of most projects: because issuance ignores the supply–demand balance, oversupply becomes the norm and prices collapse. That said, having someone arbitrarily adjust token issuance runs strongly counter to the ethos of this industry.
What I therefore propose is a mechanism that autonomously reduces the inflation rate by evaluating demand conditions based on on-chain metrics. This is what I referred to earlier as the “one coefficient.” For convenience, let’s call it the Demand Factor (DF).
The calculation itself is simple:
- Evaluate demand over a fixed period and calculate DF (MAX = 1).
- Use the Tokenomics 3.0 inflation rate as the maximum, and if a certain level of demand is not met, reduce issuance accordingly (Inflation rate × DF).
The most important part is how DF is calculated. The following on-chain indicators seem suitable:
- Transaction volume
- Gas fees spent
- Staked amount
Regarding staked amount, it may be better not to use it, since it would overlap with the dynamic staking rewards portion of the current inflation allocation.
By using the first two indicators and evaluating how demand changes over a given period, the inflation rate can be adjusted dynamically.
Below is an example calculation:
I downloaded one year of parameters from Subscan and applied them to an actual calculation.
- Evaluate changes in transaction volume and gas fees month-over-month
- Smooth volatility using a square root
- Multiply the combined evaluation value (Total Evaluation) by the previous month’s DF
- Then multiply DF by the inflation rate
- Use the Tokenomics 3.0 inflation rate as the base
This calculation is presented purely as an example, so there is plenty of room for discussion around the parameters. For instance, we could use longer-term averages to smooth changes further, or adjust the weighting between transactions and gas fees. The starting value is set to 1 here, but it could also start from a lower value.
The goal of this proposal is not to define every detail, but rather to fundamentally rethink how the inflation rate itself is determined.
So far, inflation adjustments have mainly been considered in terms of staking ratios. However, staking does not necessarily represent real demand and largely ignores actual activity. This approach, by contrast, places emphasis on real on-chain activity and introduces a different way of modulating inflation.
Moreover, if stakers want to maximize their returns, they would need to help increase on-chain activity, which rationalizes cooperation that reflects the growth of the ecosystem.
That said, this would clearly be a complex mechanism, and there are likely practical challenges on the implementation side. I’m not a developer, so I’ve intentionally set those concerns aside for now.
First, I’d like to hear what people think about this kind of approach. After that, we can consider the feasibility of implementation.
(Depending on how the discussion goes, this topic might also deserve its own separate thread.)