Cosmos aims to be the Internet of Blockchains, offering a distinctive model that enables projects to build their own blockchains. This approach aims to deliver both interoperability and robust scalability across diverse blockchain networks.
The ATOM token is a core part of this vision, responsible for keeping the network safe and encouraging active involvement from key players, ensuring it grows self-sustainable over time.
In the following article we will explore the differences between four different models:
To examine the various models, we'll utilize our Cenit Tokenomics simulation engine, which employs agent-based modeling. Each model will be accessible at https://templates.cenit.finance/?template=07-cosmos for community feedback. This will help in pointing out potential vulnerabilities in the emerging economy and work together in areas to improve.
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The ATOM token is vital for motivating network participation, ensuring safety, and supporting the ecosystem financially. Recently, Cosmos has been tweaking its financial model to better serve these goals. Below, we outline the mechanisms for both ATOM 1.0 and 2.0 models.
Both models aim to strike a balance between having enough tokens staked for network security and redistributing tokens to support the project's long-term viability. They mainly differ in how they approach fee redistribution, emission of new tokens, and inflation.
Since the model of ATOM 2.0 actually has incorporated part of the ATOM 1.0 mechanics when it falls below the security ratio, we also introduce a third model, which we call “ATOM 2.0 (No rollback)”. It has the same properties as ATOM 2.0 except that it never falls back to the ATOM 1.0 configuration.
Note that the community pool in ATOM 1.0, even when not classified within the protocol as a treasury, is treated like one.
Before diving into the different tokenomics models of ATOM, it’s essential to have a good grasp of the current ecosystem and some assumptions to guide our simulation
The current stage of the ATOM ecosystem that can be obtained from https://www.mintscan.io/cosmos is the following:
In addition to the ecosystem data, for our modeling we will use the following assumptions:
We examined three key areas across the three financial models:
To understand the viability of the treasury, in our simulation the treasury is selling tokens at a constant rate of $15M/year in value. Here we represent the amount of tokens in the reserves for the different models:
We see that ATOM 1.0 might be problematic in the mid-term, since the protocol runs out of tokens in that time. This is something that does not happen for the ATOM 2.0 model.
Security levels are related to the value of the ATOM tokens staked into securing the network. The fact that the treasury is better funded in the 2.0 model as we saw in the previous section comes with a penalty for the stakers. Since in our simulations, the amount of tokens staked is the one that we would see in the equilibrium if the stakers look for a fixed APY, we can compare the staking percentage of the network to see the effects on each model.
In ATOM 1.0, the inflation mechanism achieves the stability sought around the security threshold. This is not the case for ATOM 2.0; here, we see that for the model with no rollback, we need ~70 months until the level of staking reaches the security ratio, since there are not enough incentives for stakers.
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As a result, we see that the model of ATOM 2.0 with rollback policy enabled is constantly switching from one model to another and, therefore, not achieving the expected reduction in inflation that was sought in the white paper.
The token price here reflects the organic token price with no speculation taken into account. With this in mind, we can see that the rewards for the validators lead to a higher token price, since the utility of the ecosystem is higher.
However, it is important to understand that a big part of the token utility depends on the token price because most of the staker profits come from the token-denominated incentive rewards. If the token price goes down, there could be a vicious cycle where fewer stakers are interested, the buying pressure decreases, prices go even lower, etc.
The simulation, of course, presents some limitations:
This posts has been made in collaboration with Jacobo Uribe, member of the TE Academy. Thanks for your proposal! For more information or analyses from Cenit Finance, contact us here.