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Tokenomics is one of my favorite subject, and one I have written about extensively in the past. For example, I have promoted the use of agent based modelling, and I developed new methodologies (here and here) for valuing ICOs. Finally, I have criticised the use of token economies in cases where they are not really needed.
I recently published an article in the Journal of the British Blockchain Association, where I outline three different tokenomics use cases taken from my experience. These concern three different companies, where different tools had to be used in each scenario.
Qredo: The tokenomics analysis of Qredo is a great example of how agent based modelling can be used to answer questions when there is no theory to guide you. Qredo’s problem was in the area of both microtokenomics and macrotokenomics. As they were examining the development of a new consensus model, incentivisation mechanisms are an important part of the protocol. Agent based modelling allowed the exploration of different ways that nodes would be incentivised and how this would affect the system and the economy.
Kimlic: Kimlic is an interesting use case in the modelling of a token valuation and macrotokenomics. One of the main issues that Kimlic faced was around making sure that the token would appreciate in value on a steady rate. In the end, a mathematical model was used to forecast the token’s value, and then various staking mechanisms were proposed in order to make sure that the price would increase smoothly over time.
DOT: DOT is a very good example use case of macrotokenomics and structural modelling. DOT faced challenges such as how to reduce the volatility of the token, and how can liquidity be ensured in the face of increasing demand for the token.