Tokens and why standard economics fails to model them

Journey to the “perfect” token model (Part 2.2)

This is the second of two articles in which we will trail the path of our train of thoughts from using traditional standard economics for modeling a token economy to a more modern and appropriate approach. Our first brain waves apparently didn’t match the needs of a solid modelization, so we kept on looking for alternatives. In case you missed it: check out the first article here.

Token Analysis

The application of the schema for classifying tokens to the top 20 tokens in relation to their capitalization provides important insights into the “token landscape”. The result of the analysis of the properties and characteristics is very clear for all dimensions examined. Over 90% of the analyzed tokens can be classified as non-native, they are implemented in the protocol level that sits on the blockchain.

This will change, however, and many of the projects under consideration are planning their own blockchain. After a successful main net launch and token swap, the number of native tokens will increase significantly. It also shows that the majority of tokens are network tokens. As far as possible, they provide the holder with access to a specific network or exclusive functions within it. Generally speaking, it can be assumed that the average token encountered is a non-native protocol token whose task is to provide access to a network or its functions. The token gets its value mainly from the value generated by the underlying network while granting access to it. This finding is of particular interest for further action, as it can be used as an entry point for modeling a first basic token economy. However, it is important to consider that many projects will move to their own blockchain after successful product development including the token so that the number of native tokens will be much larger in the long run than it is currently the case.

Stakeholders and motivation

The above analysis of the tokens provides a clear picture of the token structures. However, it also makes clear that there is not only one token with always the same properties. Following the concept of DAOStack, two general classes of investors for blockchain projects can be identified. However, their motivations are in contrast to each other.

On the one hand, there are rational investors, they are long-term oriented and have primarily the success of the project in mind. Their behavior and decision making are focused on a functioning end product, the opportunity to participate in the network is of benefit to them.

On the other hand, altruistic investors exist; they are not interested in the project itself but focus solely on their individual profits. Their time horizon can be regarded as comparatively short, volatile or strongly rising prices are an advantage for them. Decisions made by this class will always be based on this. Motivation and behavior also vary within the class, depending on the characteristics of the token. Usually, both classes of investors can be found in blockchain projects. This inevitably leads to conflicts of interest which also affect the ecosystem and its functionality. It is, therefore, an elementary part of the modeling process to depict this.

Issues of classical economics

The classical models of money theory, dating back to Keynes and Hicks, were developed at the end of the 1940s, at that time computer-assisted models and simulations were far from possible. As a result, these models contain interfering restrictions to make them manageable. Representative agents, perfect rationality and the perfect foresight of these can be cited here as examples. Everything introduced to depict the flow of money and the effects of monetary policy measures generally and under perfect conditions, however, they do not correspond to the conditions of the crypto economy.

Especially the use of representative agents is a big problem for the modeling of a token economy. The concept of a blockchain is based on a decentralized organizational structure, each node of the structure (agent or user) is therefore important for the network. As already mentioned, in such an environment typically different participants with different ambitions, behaviors and sometimes conflicting interests can be found. Modeling a single representative agent would produce a distorted, even false result and render the simulation of a token economy useless. Perfect foresight and perfect rationality of the participants are still assumptions that do neither blockchain projects nor the real economic justice.

If despite the aforementioned deficits of monetary theory, one still wanted to design a model that depicts the processes, fluxes and flows of a token ecosystem, further difficulties would arise. The modern interpretation of monetary theory focuses on the inflation target, money we regard as neutral in the long term and the only way to stabilize the financial market. As a result, controlling inflation through changes in the money supply is seen as a “panacea”. Past financial and economic crises and failed quantitative easing have repeatedly demonstrated that this is not the case.

Outcome

As a result, the use of a classical (Keynesian) model of money theory for the modeling of a token system is no longer an option. The restrictions on actors and the system itself are too contradictory to the concept of a blockchain and the lack of proximity to the reality of existing models remains problematic.

Despite these new findings, the MONIAC remains the model for the token simulation, only the procedure has to be revised. Instead of relying on an existing model of monetary theory, a new solution must be sought that makes it possible to map the various actors of a token economy and their behavior.

Agent-Based Modeling

Rather, a concept is needed that does not require any rigidity with regard to the actors and their environment. Enlightening conversations with various blockchain and token experts at the dAppCon from July 19 to 20, 2018 in Berlin drew attention to agent-based modeling. Agent-based modeling fulfills the criteria already mentioned, each economic actor can be modeled individually and is therefore only subject to restrictions imposed by the modeling individual.

Feel free to comment on our idea and approaches on this and on upcoming articles.

We from authentic.network have been looking for a token model which suits our use case — providing unique identities for goods and documents — for quite a while now but we did not find any. So we want to develop it by ourselves.

You are invited to follow us on our journey to develop a model to simulate token ecosystems. To figure out what the different motivations of the stakeholders are, how they can be incentivized to create a healthy token flow inside a system and point out where the bottlenecks and limits of the system are.

PS: you will find our journey also on steemit.com.