Token Engineering Hard Skills
The goals with this series of articles are to document my journey on the learning proccess of token engineering and to ease the path for aspiring individuals who also seek to become professionals in the field. I will briefly write on both the hard skills I am currently developing & applying them on my work experience and simple strategies one can follow to facilitate the proccess. If you would like to clear some doubts about my writings or simply chat, don’t mind sending a message on LinkedIn or on Discord.
A bit of an introduction to myself, I am a portuguese guy in his early 20s with a past background in mechanical engineering and currently studying business & economics. My curiousity for web3 started on the early 2021 bull market and today I can affirmly state that same interest turned into a mild obsession. I got fascinated by the space and the range of possibilities the technology provides to build a greater future. A good portion of my time since then has been around books & articles, Youtube videos and participating in communities regarding the industry, which ultimately led to my first internship as a Token Engineer at TAIKAI Labs.
Although I am initiating this blogpost, regard that I am not, in any sense of the word, an expert in the field, but a continuous learner who wishes to satisfy his curiosity. This is the way I found to publish my findings.
Without further a do, let’s get started.
Hard skills of Token Engineering
It might be counter intuitive to think it is a possibility to build viable economic models that display human interactions and incentives, specially the ones that only exist in the digital space and thus aren’t visible to the open eye. After all, how often does a professional economist get a chance to deploy a whole economy?
Take the example of a civil engineer who is working on a bridge project. As you may think, his job doesn’t require to be side-by-side with the construction workers on the building proccess, although he might be watchful if the protocol is being well followed. Instead, his primary goal is to authenticate the viability of the project by designing, modeling and simulating hypotheses prior the construction.
The token engineer’s goal is the same, but instead of a physical structure he verifies and optimizes a cryptoeconomic ecosystem. It doesn’t necessarily mean he is going to be writing the smart contracts in production, but that he validates that what is encoded will provide a sustainable and healthy closed-loop system. This requires the token engineer to have an extensive set knowledge branches, such as computer science, economics, math and complex systems. This accompanied with with DLTs and CAD, provide the required tools to verify the models and assumptions.
As it was already stated, the workflow of a token engineer must follow a proccess, just like any other engineering job. From my experience and knowledge acquired, the work of a Token Engineer can be divided into three chronological steps:
- System Requirements
This first step provides a clear view on the workpiece to both generalized public and the token engineer himself. You start-off by describing what the model does and to what questions it is trying to find a solution to, following certain assumptions and constraints. From here, one can analyze necessary building blocks that need to be in place and thus structure a roadmap. This includes creating visual simplifications of the closed-loop system in question, like entity relationship diagrams, stock & flow diagrams and mathematical specifications that can accurately describe its dynamics. Machinations and Lucid are tools that support such purpose.
2. System Design
The next step defines both the conceptual flow (system inputs, logic and state) and the code-level flow of a model (policy functions, state update functions and state variables). This includes CAD modeling and simulation engines the allow to run several types of tests, such as hypotheses tests, parameter sweeps and A/B testing. Programming language knowledge will surely come in handy, specially the ones that allow data analysis, like R and Python. Personaly, I have been using a simulation engine package for Python: cadCAD, developed by BlockScience. cadCAD is a complex systems simulation framework and is helpful for a variety of validation tests, such as POS incentives, stress scenarios, understanding dispute resolution mechanisms and more. This facilitates the proccess for analytics, since Python already contains lybraries like Pandas and Matplotlib.
3. System Validation
The final step is regarded to synthetise and document the final results of the previous 2 steps, as well as answering the questions that were previously made: “What is the optimal X parameter for the ecosystem?”, “How many time units are needed to achieve Y metric?”, “What is the best strategy to achieve Z?”. Further on, a What-If Model Framework is going to be helpful to identify valuable stress tests that weren’t included in the first questions prior to the making of the model.
One of the most important takes I got from engineering practices is that the proccess rarely or never reaches it’s end. Even when completing the previous three steps, questioning the viability of a model itself is yet valid, since, like every other model, it is a simplification of the real-life product it is trying to describe. Contrary to traditional engineering disciplines that model deterministic behaviours from physics and chemistry, token engineering models human agents like atomic units and their respective behaviours are analyzed through the scope of game theory. This charecteristic demands the token engineer to be working with rather unpredictable results, since human behaviours are volatile per se.
“Is the model robust?”
“Does the model validate the final product?”
“Is the model’s simplicity vs validation trade-off sustainable?”
This set of questions is an example of the ones that can only be answered with the given time and experience with the product. Once you find them, you go back to the first step and create a better model.
Tips to get into the field
There are not a lot professionals specializing in the field at the current date, so finding valuable resources and communities that are investing in the discipline is very valuable. These are some of the platforms you can visit and depthen your knowledge on TE in case you might be interested:
Economics Design
Token Economics Resources List
Token Engineering Academy
Token Engineering Commons
Token Engineering Study Group (created by myself)
In case you are interested embarking in this path, you will find out that this space is a very transparent and open-minded one, full of people with ambitions. Share your thoughts, learn with others and join projects that create a sparkle in you. Most of all, build something: your network, your knowledge branch, you career, your mindset. The dots will eventually connect.
LinkedIn: https://www.linkedin.com/in/tiagosantanasilva/
Discord: https://discordapp.com/users/593885652693549067