The Aavegotchi MMT Metaverse
In this article we finish off where we started with Aavegotchi1. We discuss the GHST token, Tokenomics, Bonding Curve, Staking, and Rarity Farming from the perspective of a Market, Mechanism and Token (or) MMT (not MMT) design. This MMT framework is something I am experimenting with my project and trying it out on various existing projects as use cases for Metaverse Architecture Design. The framework is meant to be a bridge between Ecosystems Value Flows and the cadCAD Differential Specification in the Token Economic Engineering Process. Its based on two papers: one by Shermin Voshmgir and Michael Zargham2 and another by Kurt Dopfer, John Foster & Jason Potts3. Not all aspects of the framework will be here as it is meant to be a tool to model and simulate tokenized multi-scale ecosystems and as such, modeling goals need to be stated every time.
This framework exists to create digital twins for crypto economies before diving straight into solidity. While, currently most projects dive straight into solidity, as these decentralized projects get more complicated, creating and having a handy digital twin helps - its done in every engineering discipline with the exception of software. I am not modeling here nor am I engineering a token ecosystem but rather just breaking it down to understand the Aavegotchi Metaverse a bit better.
This is not a perfect representation and I may not have captured all actions involved within the Aavegotchi docs. So, please feel free to let me know if I’ve missed out any and I’m happy to update the article.
The Aavegotchi Metaverse consists of three (at the moment) “markets”:
The Aavegotchi Maall
The Aavegotchi Baazaar
I say ‘markets’ here and not ‘market’ because based on this MMT framework, the DAO in itself can be modeled within its own MMT framework. The same goes with the Maall and Baazar. In other words, we’re dealing with an Aavegotchi metaverse that is in itself a multiverse 🤯 in a multiverse inside Polygon 🤯🤯 which in itself is in a multiverse inside the Ethereum multiverse 🤯🤯🤯 which in itself is inside a multi-chain multiverse 🤯😵💀⚱️⚰️🔥🧟.
The markets broadly consists of three macro-economic action spaces or zones:
Internal Actions Space where actors within the markets interact with each other.
External Actions Space where actors within the markets interact with exogenous factors.
Data Actions Space where actors within the markets capture and store specific data based on their agent-level behaviors that show up in the markets. This last action space can overlap with the prior two but can also be isolated.
The meso-economic layer or mechanism design space is where policies and mechanisms are set and is further divided based on the macro-economic action spaces mentioned above:
Under the Internal Actions Space, we have Governance, Behavior and Signal mechanism design spaces all within the Aavegotchi Metaverse.
Under the External Actions Space, we have Dynamic and Static mechanisms design spaces in response to exogenous factors (outside the Aavegotchi metaverse).
Under the Data Actions Space, we have Recognition and Storage for parameters we either capture or store at the microeconomic level that emerge at the macroeconomic level or system-level of the Aavegotchi (metaverse) cryptoeconomic system.
The diagram below represents the multi-scale perspective of a typical metaverse (or) crypto-economic system as described by Voshmgir and Zargham4.
The internal actions zone consists of three meso-economic mechanism design spaces within a metaverse: governance mechanisms; behavioral mechanisms and; signaling mechanisms.
The governance mechanism design space relates to governance within the metaverse:
Voting Mechanism: The action involves voting in the AavegotchiDAO while the governance mechanism involves using the GHST token to vote, which gives the token its value.
The behavioral mechanism design space relates to behaviors occurring within the metaverse that do not associate with governance and can also be considered emergent.
Staking Mechanism: This action involves directly staking GHST tokens to earn FRENS token to redeem raffles.
Portals Commerce: This action involves buying and selling Portals within Aavegotchi Metaverse in either the primary (Maall) or secondary markets (Bazaar).
Claim an Aavegotchi: This action involves staking “Spirit Force” or the (interest-bearing) aToken into a purchased Portal.
Locking an Aavegotchi: This action is performed within the metaverse to ensure an Aavegotchi’s wearables are sold with it outside the metaverse (e.g. OpenSea).
Rarity Farming Mechanism: This involves a combination of multiple actions such as,
The action of selecting a rare Aavegotchi when summoning or claiming your Aavegotchi.
The action of buying your Aavegotchi with wearables that increase its rarity score.
The action(s) of engaging in minigames within the metaverse.
Consumable/ Transferable Items: The action(s) of using GHST to buy wearables sets, Drinks, Head, Body Items and Consumables.
Buy Raffle Tickets: The action of using FRENS to buy Raffle Tickets.
Realm Activities: This involves multiple actions, some mentioned above and some yet to be established within the metaverse.
The action of interacting with other Gotchis
The action(s) of playing mini-games within the metaverse.
The action of finding a Babysitter and more to increase Kinship scores
REALM parcels: The action of buying and selling Land. These parcels can also be enhanced once staking is introduced to these parcels.
The signals mechanism design space involve actors communicating important information within the metaverse that aligns with a modeling goal. They are dependent on the modeling goals. This article’s focus is only to delve into breaking down the Aavegotchi metaverse, not modeling and simulating. But, here is a list of some potential signals to watch out for in the metaverse. While these are not mechanisms, a designer could create mechanisms that accrue value to the token using these parameters if they do not overlap with the prior two mechanism design spaces already within the metaverse.
Communique frequency with DAO members via discord chats or another channel.
Interaction metrics with other actors within the metaverse related to buying and selling on the secondary market such as setting reserve prices, bid and auctioning, etc.
Interaction metrics within mini-games created by the Aavegotchi metaverse creators.
Participation frequency score like time spent in certain activities. Over time, certain activities can help increase rarity score but will not always be the same at every point of time.
The external actions zone consists of two meso-economic mechanism design spaces namely: dynamic mechanisms and; static mechanisms. In essence, this is to determine what exogenous factors outside the verse influence actors within the metaverse and which of these are dynamic (i.e. involve actors within the metaverse to interact with outside forces and back) and those that are static (i.e. involve the actors and/or metaverse only reacting to outside forces).
Portals Commerce: Buying and selling Portals on OpenSea can be considered a dynamic action here.
Aavegotchis Commerce: This involves buying and selling “locked” Aavegotchis on OpenSea.
These are the two I could find for the moment. Let me know if there are others.
In the case of static mechanism design space, the ones I could think off, are protocol related. Since most metaverses are prone to protocol risk rather than market risk. This therefore, would be aligned toward developers who operate at this intersection of the stack, rather than users/actors in the Aavegotchi metaverse.
Layer 2: The Aavegotchi metaverse is on the Polygon layer 2 solution. Therefore, receiving any updates related to the layer 2 solution used would be prioritized here.
Layer 1: And of course, in this case would be the Ethereum protocol. Would issues such as EIP-1559 affect the metaverse (probably not).
Web3 interfaces: This would involve wallets like Metamask. Reacting towards any updates or improvements within wallet interfacing with the metaverse.
Can we create mechanisms for the Static mechanism design space, from the perspective of the Aavegotchi team or is this something that is not really necessary?
The data actions zone consists of two meso-economic mechanism design spaces namely: recognition and storage mechanisms. These mechanisms would need to align with the modeling goal. The Recognition mechanism design space are for mechanisms that perceive/capture data that are not grouped in any of the prior mechanism design spaces but are important as per the stated modeling goal. Similarly, the Storage mechanism design space is for any data that needs to be remembered as per the stated modeling goal not grouped in the prior mechanism design spaces.
In layman terms, what mechanisms do we design as per the stated modeling goal that help us capture or store data at the micro-economic abstraction (agent-level) that show up in the macro-economic abstraction (system-level).
Since this is dependent on modeling goals, I’ll list a few I think may be important to capture value. Again, these are not mechanisms, a designer could create mechanisms that accrue value to the token using these parameters if they do not overlap with the prior mechanism design spaces already.
Frequency of voting (signing via Snapshot) invokes an actors participation within the metaverse, helping with their rarity score.
Quantity of Portals bought and sold.
Supply and Demand of wearables considered to increase rarity.
Quantity of FRENS.
REALM parcel uniqueness score via personalized decorations and staking quantities. This can be under both - where buying and selling decorations can be categorized under exchanges and staked quantity can be categorized under storage.
Base Rarity Score (BSR): There are two types based on Traits and Wearables. Both seem to depend on the user/actor’s ‘investment’ into their Aavegotchi and therefore, involves buying, selling and placing collateral.
Quantity or quality (?!) of Aavegotchi Improvement Proposals (AGIPs). I wasn’t exactly sure about this since, actors don’t pay a penalty for “spam” proposals. Can this actually happen?
Quantity of GHST token staked. I’ve placed staking here (even though it is a transaction in itself) because, staking quantity involves a form of storing within the metaverse to support its operations.
Quantity of aTokens/Spirit Force staked for the summoning of the Aavegotchi.
Timestamp of Aavegotchi “locked“.
Absolute Rarity Score (ARS): This seems more algorithmic based on the actions of all Aavegotchi owners within the metaverse so, I thought I’d place it here under the storage design space. But, maybe in the recognition design space if one can determine all ecosystem actors’ transactions?
The token design space involves the micro-economic perspective based on the mechanism(s) we’ve identified within the meso-economic layer (which is based on the action zones in the macro-economic layer). We start here with the Tokenomics finally refining down to the Bonding curve for that token. While Aavegotchi has the GHST as its primary token for all actions and mechanisms within the metaverse, it also has FRENS which is an internal token (of sorts). Just like Decentraland and The Sandbox has its utility tokens MANA and SAND, respectively and their internal tokens LAND. While, currently it is common to create a mono- or duo- token crypto-economies, I’d assume over time, each metaverse could have multiple tokens to capture value within that verse.
From deriving down the markets and mechanisms, the macro-economic actions zones and the meso-economic mechanisms, the next step is figuring out the important metrics within these meso-economic mechanisms (i.e. Governance, Behavioural, Signals, Dynamic, Static, Recognition and Storage) and associating a token (if necessary) for these metrics (this helps determine the value capture in the metaverse). This goes to the point I made earlier of a multi-token metaverse unlike the current mono- or duo- token crypto-economy.
Since, value capture of the token has been categorized and communicated really well in the Aavegotchi wiki, for the moment, I’m going to use that as a reference.
Token Value Distribution (Tokenomics)
The value of the GHST token is as shown in the figure. There are four aspects of value accrual namely by burning it, earning it, building it and voting in the metaverse. The GHST token represents the value transfer emerging into the macro-economic Actions zones of the Aavegotchi metaverse via meso-economic Mechanisms.
Transaction fees associated with using GHST to interact with users’ Gotchis, buy and selling items within the metaverse and more are accrued.
40% of those earnings from fees are re-distributed back to the community via rewards every two weeks.
There are various player rewards, all of which will amount to that 40% allocation re-distributed.
GHST will be burned as a form of deflationary counterbalancing from price rise by distributing earns (mentioned above) and the increase in price based on supply from bonding and burning via the curve.
33% of all GHST earned from sales of items and portals will be burned forever.
10% of the revenue accrued in GHST will move into the Aavegotchi DAO Treasury.
This will be used to fund any proposals, managing the treasury and eventually upgrading the protocol
17% of the revenue goes to the development team responsible for engineering the metaverse.
This is not to be confused with the bonding curve which uses a community-governed Tap mechanism to pay the lead developers in DAI every month to help them actively develop the metaverse.
A token bonding curve is a mathematical relationship between the token’s price and its supply.
The curve has three main aspects to it:
The Token Metrics
The Token Metrics
The token issued in the Aavegotchi metaverse that is used to transact, burn, earn, vote and build is based on the ERC-20 standard.
The total supply of the GHST token is uncapped and consists of mechanisms to ensure the price does not inflate or deflate too harshly and fast.
The bonded collateral is DAI. One can still buy GHST with ETH or ERC-20 tokens like USDT, BUSD, USDC, etc.
The Traded asset here is obviously, the GHST token.
The mathematical function used here to express the bonding curve is a polynomial function (y=mx^n).
Here, “y” is the Price of GHST; “x” is circulating supply of GHST, “m” is the gradient of the equation and “n” is based on the reserve ratio discussed below.
The pricing structure here is partially dynamic via the mechanisms placed within the metaverse and the manual voting (humans involved) on governance proposals regarding the treasury. It is automated rather than autonomous. In the future, the Aavegotchi team could use a controller for certain treasury spending/reserving policies that did not necessarily require voting.
DAOs cannot be fully autonomous, humans are still needed, so they will always be automated with some autonomous features to aid in governance minimization.
The curve mechanics is similar to the Bancor mechanics. The bonding curve has a conservation ratio (or) reserve ratio (or) invariant to manage the price rise with increased supply and price decrease with any burning activity.
This invariant is kept at 33%, pretty much the same as what the Bancor protocol does based on the integral derivation of the formula that takes an abstract scenario of a token purchase on the bonding curve.
Ideally, having a range between 0.1 and 0.33 ensures pump and dump attacks are less severe. But, having a range as your invariant is better if one decides to create an autonomous supply issuance policy rather than the current manual voting based supply policy changes. Saying this, there are several mechanisms in place within the metaverse that indirectly work as a way to regulate this issuance policy but this can also get more complicated with time if one had to find the root cause.
So, that’s it. That was a breakdown of the Aavogotchi metaverse from a high-level macro-economic abstraction where the markets exist to the micro-economic abstraction where the token exists using this MMT framework for metaverse design. Please feel free to let me know if I’ve missed out anything and I’m happy to update the article.
Voshmgir, Shermin and Zargham, Michael ORCID: https://orcid.org/0000-0001-5279-690X (2020) Foundations of Cryptoeconomic Systems. Working Paper Series / Institute for Cryptoeconomics / Interdisciplinary Research, 1. WU Vienna University of Economics and Business, Vienna.
Dopfer, K., Foster, J. & Potts, J. Micro-meso-macro. J. Evol. Econ.14, 263–279 (2004). https://doi.org/10.1007/s00191-004-0193-0
see Note  above.