Unveiling Filecoin’s Economy
How scenario-based models can help us understand Crypto Economies.
At CryptoEconLab, we often get questions about how a given protocol change may impact the underlying economic systems that support it. This question may sound very broad. But, at these moments, we put on our economist hats and focus on a set of metrics that capture the health of the economy such as the circulating supply of its native token, the adoption growth rate, the profitability of providers, etc.
This is where scenario-based models can be incredibly valuable. By creating models based on hypothetical scenarios, we can gain insight into how changes in key economic parameters and user behaviors might impact metrics like its circulating supply. We can simulate different scenarios and analyze the results, thus gaining a deeper understanding of the potential risks associated with a given protocol change.
Ultimately, scenario-based models can help us make more informed decisions and better manage the risks associated with designing and updating the rules behind crypto economies.
To illustrate this point, we will discuss a scenario-based model our team at CryptoEconLab built for the Filecoin economy. But before going over the details, I need to mention Kiran Karra and Tom Mellan, who worked with me on this model.
Filecoin Econ 101
Filecoin is a decentralized storage network that aims to store humanity’s most important information. It organizes a network of storage providers that supply cloud storage services. In return, storage providers earn FIL tokens for maintaining their storage and the overall network consensus.
In Filecoin, the unit of storage is called a “sector”. When storage providers onboard new sectors into the network, they need to specify its duration, i.e., for how long they are committed to maintaining that sector. At the end of that period, the storage provider can decide to exit and let the sector expire or they can decide to remain and renew the sector. The storage provider will receive a penalty if their sectors are terminated before the stated expiration date.
Being its native utility token, FIL is the token to focus on when analyzing Filecoin’s economy. Four main mechanisms influence the circulating supply of FIL tokens:
- Minting — The process of creating new FIL tokens. There is a total fixed supply that is distributed based on a pre-defined schedule. This schedule follows a mix of the classic exponential decay minting and a new mechanism we introduced called Baseline Minting, which depends on the storage capacity growth rate of the entire network.
- Vesting — The process of gradually releasing FIL tokens based on a pre-defined schedule. Examples include the tokens attributed to Protocol Labs and the Filecoin Foundation, which are scheduled to vest linearly over 6 years.
- Burning — The process of “destroying” FIL tokens to account for the computation resources used by on-chain transactions. Every time a transaction is sent in the Filecoin blockchain, the sender needs to pay gas fees, and a part of these fees is permanently destroyed. The mechanism is similar to Ethereum’s gas fee mechanism proposed in EIP 1559.
- Locking — The process that temporarily removes FIL tokens from circulation to increase consensus security and storage commitment. Every storage provider needs to lock an amount of FIL tokens to onboard new sectors and start receiving rewards. Those locked tokens are released when the underlying sectors expire.
Minting and vesting increase the circulating supply while burning and locking decrease it. Thus, we only need to model each of these components and add them up to arrive at the final estimate of the circulating supply.
However, most of these mechanisms depend on human decisions, which is hard to model. For instance, burning depends on the demand for block space (or how many transactions need to be executed at any given time), while locking and minting ultimately depend on how much storage is being added to the network, for how long the storage is being committed, and how much storage is being removed.
Since predicting these human decisions is hard, we can instead boil them down to a set of parameters and model how those parameters impact each mechanism. This way, we can design some scenarios, translate them into the parameters and see how they impact circulating supply.
MechaFIL — our scenario-based model for Filecoin
MechaFIL is a Python package that contains our “mechanistic”, scenario-based model for Filecoin’s circulating supply. The model simulates the mechanisms we discussed previously (i.e., minting, vesting, locking, and burning) based on a set of parameters that need to be provided by the user running the simulation.
The first set of parameters is related to the time for the simulation. We need to specify the start date of the simulation, the current date, and the length of the simulation.
The second set of parameters corresponds to an “average” storage provider behavior. They include how many PiBs of storage are onboarded and renewed each day, the average sector duration, and the average percentage of storage being onboarded and renewed that contains FIL+ deals.
FIL+ deals are data deals with clients that are certified by a community of trusted notaries. Sectors that contain FIL+ deals get an additional multiplier that increases their share of rewards and how much FIL needs to be locked to secure the sector, so we need to consider it when modeling minting and locking.
You can find the code and model assumptions in our MechaFIL GitHub repository. You can also use our Google Colab notebook to run a quick example directly in your browser.
Looking forward
With MechaFIL, we can estimate how different regimes of storage provider behavior impact the main drivers of circulating supply. We can also test how specific changes to crypto-economic parameters may impact circulating supply for a set of scenarios representing storage provider behavior.
We have used this in the past for analyzing the impact of current storage onboarding on the Baseline Minting mechanism or for setting the parameters of a proposed protocol change for Filecoin that would introduce a new sector multiplier based on its duration.
The model works great for experimenting with simple scenarios such as “what if the storage onboarding continues at a given rate?” or “what if sector renewals decrease by 10%?”. Because the scenarios are simple, it is much easier to communicate them to a broader audience and get more engagement from the entire Filecoin community.
The model is also a useful tool for explaining Filecoin’s economic mechanisms and how they respond to concrete scenarios. Because the economy is complex and the different mechanisms have intricate relationships with each other, it is often hard to predict how a given shift in Storage Provider behavior will impact Filecoin’s economy.
However, the model cannot take into account different profiles of Storage Providers, such as providers that focus on finding FIL+ deals versus providers that focus on adding storage power without FIL+ deals. The model also does not consider the internal motivations of the participants in this economy. For instance, if, at some point during the simulation, the expected returns become negative, a rational Provider would stop onboarding new sectors since that would lead to financial losses.
Thus, we are currently working on the development of an agent-based model that will allow us to code different behavior profiles and take into account rational decisions. This model will provide a more detailed understanding of the behaviors and incentives of storage providers, clients, and other actors within the Filecoin ecosystem, allowing us to make more complex simulations of the network’s economy.
We are excited to share our progress with the community and to continue to work towards a better understanding of the complex dynamics of decentralized economies like Filecoin. Stay tuned for a follow-up blog post!!
If you want to read more about CryptoEconLab and our work, you can visit our website at cryptoeconlab.io or follow us on Twitter.
