2. The GET Protocol mandate

In earlier blogs we explained there are fundamental flaws in how the ticketing industry is set up. The sector is very ineffective(with 30% of tickets being resold above fair-value, with all re-sale profits exiting the ecosystem). The sectors current set-up pushes the costs of these inefficiencies on to the fans.

We believe a transparent ownership registry of tickets with an auditable value & revenue trail will ensure all actors will act morally and fair. For this ticketing use-case using blockchain is the perfect marriage as technology where it excels in processing & registering cryptographically secure transfers of digital asset ownership's based on certain criteria (like max re-sale margins, ticket validity and overall network state).

The GET Protocol will be used by ticketing companies, consumers, promoters and decision-makers in this industry all over the world. All these actors need to have a place in this protocol and need to be able to add their value without disrupting elements of the industry that work well. The protocol aims to decentralize only what benefits from decentralization & its inherent transparency. Maximizing for added value and minimizing usage costs for the actors using the protocol for what it is intended to facilitate; fair and transparent ticketing. Incentives and benefits of early stage GET holders (i.e. speculators) and later stage GET buyers/users (i.e. event organizers) should be fairly aligned. Adoption is only accomplished if GET usage is evenly beneficial for the users of the token as it is for the speculators of the token(read the Token Discount Model paper for context.) The protocol should be able to comply to any local law in which it is used. Meaning that the ticketing companies using the protocol in countries around the world should be able to only use the GET Protocol functionalities that are compliant to their applicable local banking, trade and consumer protection/ownership laws. In short; the protocol should offer a buffet of functionalities to ticketing companies to pick and choose from. Suiting all business models and jurisdictions.

Mandates and wish-lists are only useful if there is a quantitative metric to which they can be held accountable. In our eyes this mandate is best reflected in the goal for the GET Protocol to be predictable in its use by organizers and therefor considered a stable and reliable infrastructure to build value adding business on top of for ticketing companies. All over the world.

Learning from history

While building a decentral protocol has little to do with managing a central bank, we do share some mandate end-goals with these banks. While there are several schools of thought in monetarism there seems to be common ground in the premise that economies benefit most by offering stability for the actors within the economy(allowing entrepreneurs to take risks and innovate, create value, jobs & moons). Stability doesn’t mean that a currencies (relative) price will stagnate, or that overall production should plateau. It just mainly means the economic environment should stay within a predictable margin. While I am personally no fan of central banks it would be foolish not review their mandates(Mandate FED), their policies(ECB monetary policy), the results of these policies (Paper in the Journal of Monetary Economics) and external reflections on the long erm effects of these policies(Athanasios Orphanides FED mandate paper). Warning upfront; sources are dry as ****.

Scratching the surface of economic theory

Having a high velocity as the protocols native asset(i.e. actors in the protocol don’t hold on to GET after usage but immediately convert the crypto into FIAT). High token velocity creates an unstable, volatile and unpredictable ecosystem. The cause of this chaos is the fact that the average hold time is low and GET in circulation has a high variance/range. Controlling for velocity is therefor important (this was already established in the first blog on token economics). For more information on the velocity problem read up on this CoinDesk/Multicoin Capital article.

Luckily a ticketing protocol is less complex than controlling the currency of a regions economy. The whole point of using a protocol with a native token, as opposed to using only ether for blockchain ticketing, is that in a protocol with a native token (GET) the system is able to set certain rules for transacting and interacting. For example, a system can influence by white-listing actors, setting requirements of staking for usage of the protocols contracts or offering discounts of usage to actors assert control. There are many more of such mechanisms that assert control and steer outcomes.

It comes down to controlling how and when a ticket is transferred within the GET Protocol and who benefits of the transaction. This allows us to more effectively restrict the flow of GET into the protocol, creating a external(open market) and internal(event-cycles) environment. The value of these internal rules will be formalized further moving forward. Open market transfers of GET can’t be controlled or restricted in any way by these policies.

Modelling effects of mechanisms on the tokens velocity

As internal stability and predictability are paramount for any consumer facing service/protocol we need to tackle this velocity problem head on. In its conclusion the first blog listed 5 velocity reduction methods that would ensure actors within the GET Protocol would hold on to the GET after usage in a ticketing cycle.

In the first blog on token economics we introduced the concept of the equation of exchange and the importance of velocity control. In short we concluded that conversion from the token into FIAT should be controlled as without any control mechanisms risk-averse actors can create a token economy where the conversion-rate to FIAT is very high.

Unfortunately the model isn’t complete with just the equation of exchange. While the equation is correct from a high level point of view it is far from perfect when trying to quantify effects of certain protocol mechanisms practically(i.e. predict effectiveness of velocity reducing mechanisms). Its main flaw comes from the fact that when used in the context of modelling value crypto assets(and not FIAT money/economies) the equation becomes circular. Let’s quickly cover the formula again to refresh your memory.

The equation of exchange isn’t the golden bullet

The equation of exchange rephrased to a more utility-focused description of demand.

The Equation of Exchange was originally formulated by the economist Irving Fisher in the early 1900's. The equation describes the relationship between the demand side (M * V) and the supply side (P * Q) for the monetary base of a currency region.

It should be clear to the reader that we differentiate between the external market of the protocol(i.e. the open market, over which we have no control) and the internal market(i.e. the event-cycles, their ticket market and the actors) are two entirely different economies. While the external market is affected by the pull(demand) for GET coming from the internal market, this pull is controlled and set by the added value in the internal market. As the internal market of the GET Protocol has this control, this aspect of the protocol is most interesting to model and formally describe.

Figure X M*V = P*Q (Image source: Austere Capital). More background information about the equation of exchange(SOURCE).

For those that are a bit lost, lets go over the variables again in the context of ticketing in the GET Protocol.

M — The total asset base. In our case this is all the GET that is being used, staked or held by actors within the protocol .

— The total asset base. In our case this is all the GET that is being used, staked or held by actors . V — The velocity of GET within the protocol. How many times a unit of an asset is spent, used or staked/locked by an actor within the protocol.

— The velocity of GET within the protocol. How many times a unit of an asset is spent, used or staked/locked by an actor within the protocol. P — The amount of work/utility a token can perform for an actor within the protocol. The more work a single GET can perform, the higher its value for the holder.

The amount of work/utility a token can perform for an actor within the protocol. The more work a single GET can perform, the higher its value for the holder. Q—The total amount of tickets and event cycles occurring within the protocol.

Equation dynamics & assumptions

If one adopts a theory from a different field it is important to review the assumptions made by the creators of said theory to verify if these assumptions also hold up in our usecase. In the case of the equation of exchange we need to review if monetary policy is comparable with protocol policy.

When looking at the formula from a central bank point of view there is only one variable the bank can influence directly; the money supply(M). By printing extra money a central bank is able to trigger inflation (P) making the economy more competitive (i.e. cheaper) and thus increasing Q rather directly. Velocity in the context of both quantitative easing or ordinary monetary policy (i.e. printing more money) is deemed to stay unchanged in the short term. Oops. Conclusion: due to this ‘V doesn’t change’ assumption the model on its own is rather useless in predicting changes in velocity.

Figure 2. By increasing the money supply a central bank directly increases P due to inflation of the currency and thereby boosting Q. Central banks can’t directly influence V in the short term and assume it to be in-elastic as they have no direct mechanisms at hand to boost faster overall money circulation directly by actors in an economy.

Modelling effects on velocity

The equation (and the theory around it) seems to be built upon the assumption of inflation and thus the dynamic characteristics of the money supply(M). The problem is that crypto is generally built upon the idea that the ‘money/token/coin supply’ (M) is either capped(cannot change after minting, like with GET) or has a scheduled decreasing release (like Bitcoin). In other words crypto tends to prefer the deflationary train of thought.

Summary: Central banks make use of control tools that are not completely compatible with what we have at our disposal. Check this blog for a more complete analysis of this conclusion.

When using this model to quantify certain velocity reduction measures in the GET Protocol we run into another problem; circularity.