Modeling Bitcoin
I’d like to start with a decentralized organization that has a lot of data to draw from. Given that Bitcoin has a long history, it’s a good candidate to figure out how to model it’s growth and health. Since Bitcoin is a part of Layer 1, this will give a good base to build upon as we look at more complex decentralized organizations in the future.
This article assumes the reader has a basic understanding of the blockchain technology behind Bitcoin and its Proof of Work consensus algorithm.
As of this writing Bitcoin has a market cap of $115B. How do we determine if $115B is an appropriate market cap for Bitcoin? Currently, we mainly look at the market cap as a function of total supply and price. Where price is a function of supply and demand on the open market. This demand can change greatly based off of recent news or other information online, but we’re missing a standard way to gauge the health and growth of such an organization.
Cash Flow vs. Transaction Flow
In a traditional financial model, an analyst looks at the cash flow of a company to judge it’s short term viability. Is the company bringing in enough cash to pay off it’s bills and continue its operations? With a Layer 1 decentralized organization, there is no cash flow since there are no products being sold. Instead we have Transaction Flow to determine short term viability.
The main purpose of Bitcoin is to store and transfer value. Therefore, if individuals are sending and receiving Bitcoin, then the technology is serving its purpose. The more transactions that are flowing through the decentralized ledger in a given period of time, the more benefit society is gaining from the technology.
Breaking Down the Transaction Flow
Now let’s take a step back and look at a simplified example of what happens when one address sends bitcoin to another address.
When address A sends 5 BTC to address B, A also includes a transaction fee so the transaction can be processed in a timely manner. While B received the 5 BTC, a miner, M, received the transaction fee as well as the block reward for successfully mining the block that contained the transaction. Miner M is happy and likely to continue mining because they gained value from mining the transaction. A notable effect of this is that the total supply of the BTC also increased by the block reward at the time of the transaction.
We know that Bitcoin (and other Proof of Work blockchains) become more secure as the number of different mining pools and hashing power increases. The more secure the network, the more likely people will utilize it. The more transactions, the more miners will benefit from mining on that network. Therefore, we can argue that at some level there is a positive correlation between the number of transactions flowing through a network and the security of said network. So we’ll need to keep track of number of transactions and total transaction fees at various intervals.
Another take away from this is that there is some bottom threshold of transactions where miners are no longer incentivized to mine Bitcoin and will fall off. As miners drop off of the network, the network becomes more vulnerable and eventually insecure. An insecure ledger is worthless, therefore we can use this theoretical threshold to determine at what level of usage the organization becomes unhealthy and worthless. It also means that number of miners and hashing power are data points we should keep track of.
Basic Model
It’s great that we can figure out some basic data points to determine the health of the organization, but at the end of the day we still want to know how those numbers will impact the market cap of the decentralized organization and ultimately the price of the underlying digital asset. The data points of the model are meant to be calculated at an arbitrary interval, so since these decentralized organizations change rapidly, lets use a month rather than the traditional quarter.
Thus far we’ve found that our line items in question are:
- Number of Transactions
- Total Transaction Fees
- Number of Miners
- Average Hashing Power
Needing to tie it back to market cap and ultimately the price of the digital asset, we must also look at the total supply of the digital asset as well as the market cap at the end of the arbitrary interval we set (end of month). Remember that the total supply may have changed since the last interval, so to be complete we should record the supply delta.
Entropy (just a concept)
We have a good base model to project and gauge health of the decentralized organization. Based on Bitcoins current functionality, the supply will asymptotically increase until it levels off at a circulating supply of 21 million BTC. Theoretically this is fine, but in practice this will not be the case. If we wanted to be very conservative, fine we can project out that the supply will always increase. However, this doesn’t feel right since it’s ultimately humans that are using this system and humans are prone to error.
There should be some way to account for entropy in the sense that users will lose their private keys and thus the digital asset belonging to that key is gone. I haven’t thought of a good way to determine the quantity lost to entropy aside from a time since the asset was last moved (i.e. if it hasn’t moved in an arbitrary amount of time, we can say the asset is no longer a part of the total supply as it is deemed lost). Again, this idea is not fully hashed out, but I think at some point in a decentralized organizations life it makes sense to assume that an asset not touched for a long period of time it’s ok to assume it will not circulate again.
Conclusion
These are some basic data points that allow us to model the Bitcoin network by looking at two significant stakeholders, Users and Miners. This is a simple model, but gives us a good basis as we look towards the other stakeholders and other layers in the decentralized ecosystem. This is also meant to be a fluid model, and in the future some of the more detailed data points may not exist or may be replaced by data deemed more influential. As we analyze layers higher in the stack, we will have more concrete data that will provide a model that is more clear. I welcome anyone to contribute ideas and feedback in comments or messages moving forward.
This is not trading advice. Ultimately, prices fluctuate due to external market forces. The thoughts and ideas presented here are merely an opinion and meant to be discussed.
