Exploring Bonding Curve Collateral
The implications of bonding stable vs unstable collateral on a token bonding curve
A recent hackathon gave the Protea team the opportunity to implement a Dai backed stable bonding curve. In this post I am going to explore the implications of bonding stable vs unstable collateral on a token bonding curve (TBC). The concept of TBCs in token engineering is relatively new, so I’ll start by giving a background to TBCs, and direct you to resources to learn more. I’ll discuss some of the effects unstable collateral can have on curation markets and TBCs, as well as how they can be mitigated.
In practice, a TBC acts as an automated market maker, by regulating the buying and selling of tokens along a curve, with the curve having a pool of collateral to back it up. You’re essentially bonding collateral when buying tokens (increasing the supply), and burning collateral when selling tokens (decreasing the supply). These relationships are defined by a smart contract.
While TBC design is still in its infancy, lots of experimentation and innovation is taking place, and TBCs have become a core primitive of token engineering. There are signs of TBC design gradually maturing — some projects like Convergent and PoWH3D have launched experiments on main-net. Design parameters are being developed to create a common language, and align innovation and research. A monthly online conference call has been set up to connect the global TBC token engineering community.
A TBC acts to provide liquidity, allowing you to curate a market for a particular item, and it controls if not reduces volatility. As a result of applying TBCs to curation markets, a core benefit is that it acts as an attention signal — as a basic general rule, assuming an upwards slope, the further up the curve the market is the more attention there is in the market.
However, there is one fundamental design parameter that can distract from this pure signal. The collateral.
The negative effect of unstable collateral
Something important to keep in mind when thinking about TBCs during this article is that the graph is independent of time! This tends to be confused quite often, because the curve can end up looking like a traditional stock market value curve. In this next example we’ll take two snapshots in time to demonstrate the effect of bonding against unstable collateral.
Take the example of a bonding curve, as shown below, with a gradient of 1/1000 (i.e. price of 1 token = 1 ETH at a supply of 1000 tokens, and 2 ETH at a supply of 2000 tokens). In January the value of 1 ETH was $1000, and later that year the value of 1 ETH was $100. Despite the market attention increasing from a distribution of 1000 tokens to a distribution of 2000 tokens, the fiat value of the bond decreased. This skews the perceived gain in market attention, and the real world utility of these tokens decreases.
The core values of TBCs are crippled by fluctuations in the bonding curve collateral. In most cases the TBCs collateral has real world value too, and just like with fluctuations in a stock market, holders of the token will be concerned about fluctuations in the value of their tokens.
The instability of the collateral might also cause users, especially those not necessarily familiar with the world of blockchain and cryptocurrencies, to be hesitant to enter into a bonding curve. Trading in the traditional sense causes anxiety. The attention signal can then be skewed by non-adoption.
While it can sometimes make sense to use unstable collateral — perhaps in an economy where there is no specific metric attached to the TBC, and instead you’d like to support the growth and liquidity of a specific market by bonding against the project token — in most implementations bonding stable collateral makes the most sense and provides the best attention signal.
Bonding stable collateral
Bonding stable collateral such as good old US dollars brings not only stability, but also familiarity. Maker DAO’s Dai provides this in the form of an ERC20 token tethered to the value of the US dollar. Until such time that cryptocurrencies are adopted more widely, we just have to accept that fiat currency options will be more stable.
Gregory DiPrisco wrote a great article about how Dai actually works: “Maker for Dummies: A Plain English Explanation of the Dai Stablecoin”.
Take the example of a bonding curve, as shown below, with a gradient of 1 (i.e. price of 1000 tokens = 1000 Dai at a supply of 1000 tokens, and 2000 Dai at a supply of 2000 tokens). In this case, where the collateral is stable, the bonding curve accurately represents the gain in market attention. As the token supply increases, the real world utility also increases.
It makes sense to use stable collateral in cases where the TBC value is coupled to some specific metric . With Protea we are working on signalling value and commitment in communities using TBCs, and believe that stable collateral such as Dai will provide the most apt signal. We’ll explore this more in a future post.
This is exciting! The theory of TBC design has come to a stage where a common language is being developed, and we can start experimenting with design parameters, and building around TBCs to engineer crypto economic solutions for real-world problems. The community around this idea is growing quickly, and we need to start sharing knowledge to keep pushing the boundaries of what we can achieve. We are working on an open-source Dai backed TBC SDK, if this could be of value to you or you’d like to contribute — get in touch! A big thanks to the shoulders we are standing on to develop these ideas!
In summary ~ the collateral of a TBC is a critical design parameter to consider, and unless you have a specific reason to bond unstable collateral against your curve, stable makes the most sense.