Tokenomics Jobs Tools and Skills #5: Python, cadCAD, datascience

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(scroll down for video sections)

How do you model and simulate a token economy in Python?
Why would you want to do it that way over other modelling alternatives?
What are the potential risks and pitfalls of this modelling approach?

When it comes to tokenomics modelling we have a variety of tools to choose from.

Whether we choose to specialise in a specific toolset or train as a generalist, we need to learn about the strengths and weaknesses of each one. That’s how we make sure they are fit for the task at hand, and then applied in ways that yield the best results.

This week we take a look at what tokenomic modelling looks like in Python.

Specifically, we'll look at how we can use Python to create causal models which define how components in an economic system interact and affect each other.

We then use Python to define a simulation and evaluation process that explores how these systems behave and perform under different circumstances.

Working within a Python environment gives convenient access to datascience and optimization libraries that greatly expand the possibilities for empirical validation and model development.

New Python-based tools like Block Science’s cadCAD are also creating standardised frameworks which help make this kind of model development and evaluation more efficient and repeatable.

Modelling in Python yields categorical benefits over alternatives when an effective evaluation of the system cannot be reduced to a “small” set of events that can be determined ex-ante (as is typical for spreadsheet-based financial modelling). A very large event space has to be explored instead.

As the video explains, this tends to apply to systems with resource flows and KPIs that are dominated by serially-dependent actions of diversely motivated, autonomous agents.

+++ Video Sections +++
00:00 - Intro
01:04 - How to start from scratch
02:41 - Creating model specification
04:36 - Creating the simulation process
05:04 - Importance of feedback and path-dependence
07:31 - Creating the evaluation process
08:15 - How to incorporate real data
08:39 - Model optimization
09:04 - Introducing cadCAD
09:58 - Introducing Token Spice
10:51 - Risks to simulation modelling in Python
14:41 - Concluding summary

+++ Links +++
Token Engineering Academy - tokenengineering.net/
TE Discord - discord.com/invite/gxdyfRdYeC
Blockscience - block.science/
cadCAD - cadcad.org/
Token Spice - medium.com/tokenspice/introducing-tokenspice-fb4da…