Establishing Open Token Engineering Tools & Standards

Open Sourcing cadCAD as a Cornerstone of Permissionless Public Infrastructure

Our purpose in launching the Commons Stack is to build an open-source, token engineered component library that people around the world can use to assemble their own highly customized, simulated & tested DAO ecosystems. This engineering infrastructure will underpin the stability of our cryptoeconomic networks of the future, a key piece of complex system design that is absent in the blockchain space today. In Iteration 0 of the Commons Stack, our focus is on staging the cultural and technical initialization components required to make these systems succeed, which includes (1) the launch of the Trusted Seed community, and (2) the open sourcing of cadCAD to support more rigorous token engineering processes. Our near term goal for Commons Stack infrastructure is to invite the token engineering community to come together as a DAO and hatch an ABC to provide continuous funding for open token engineering R&D — and that’s where we need your help!

As engineers building permissionless public infrastructure that could one day be widely used around the world, we have a professional social responsibility to ensure that those who use it can do so safely. To draw a comparison — a basic wooden bridge might suffice for a small village, but the Golden Gate Bridge required a much higher degree of engineering design and planning. Thousands of hours of mathematical and engineering rigor went into stress testing materials, simulating harmonic resonance and much more, all to ensure the safety of the generations of people who would later use it.¹

The Golden Gate Bridge, an engineering masterpiece (Image: Rich Niewiroski Jr)

If the token economies we are building today become an integral part of the world’s digital infrastructure, we need to ensure they are robust by design. Otherwise, unforeseen systemic failure modes could cause users serious harm. Unfortunately, our current token design processes are more like those of the village builders than the Golden Gate Bridge engineers; there are many knowledge gaps to fill before we can build societal-scale cryptocurrency networks that are worthy of the public’s full confidence. Our priorities as a community should be to close the gaps in our design processes and follow more rigorous engineering practices around design validation, simulation and testing.

This is why the Commons Stack supported the open sourcing of cadCAD at the Token Engineering Global Gathering in August 2019. cadCAD is the first open-source simulation and modeling tool for use in complex system design, built by the team at BlockScience.

“A natural path forward is to treat cryptoeconomic systems as cyber-physical systems, and to approach them with the diligence an engineer must afford to any public infrastructure.” - Voshmgir, Zargham, 2019

Understanding the Engineering Process

To understand the importance of a tool like cadCAD, let’s walk through the engineering design process:

Research: develop an understanding of the problem in narrative form Specify: rigorously define the requirements of your system that will resolve the problem Diagram: draw out your system components and interactions between them Formalize: model your system with equations Simulate: test and validate assumptions about system behavior Prototype: verify that your product does what you designed it to do

Of course, there are many cycles of iteration within and between each of these steps. All the while, the team is learning what works and what doesn’t, updating its assumptions based on new information, and perhaps even revisiting the design specification. Stephen Young dives deeper into the token engineering design process in this excellent article.

Good engineering abstracts complexity away from the end user, but the engineer is ultimately responsible for the safety of the infrastructure that is built. Therefore the token engineer must consider the complex interactions involved in its use, especially in economic system design. (image: Anyusha, Pixabay)

Many blockchain projects today jump from step 1 (writing a whitepaper with some narrative-based ideas on how to address a problem) to step 6 (deploying code on a testnet). This would be similar to building that village bridge: people decide they need an easy way across the river (step 1) and immediately begin fashioning a bridge out of materials at hand (step 6).

This isn’t the fault of any of these projects, most teams come from the computer science space where this isn’t necessary — with a single pull request, code can be easily modified. However, this is not the case with blockchain technology in cryptoeconomic systems; once deployed, it is very difficult to change. Standardized formalization, diagramming and simulation models have been non-existent in the world of token engineering — until now. cadCAD leverages the data science-friendly python stack to help projects test, simulate, and validate their expected system behaviors. It is a differential games engine with sophisticated agent modeling incorporating A/B testing, parameter sweeps and Monte Carlo analyses — a whole suite of tools for complex system analysis.

Of course, when we are building cyber-physical economic systems, we must not focus exclusively on technology and forget the people that are embedded in these networks. To complement cadCAD’s improvements to building robust technical components, the Commons Stack is pioneering education to foster a better understanding of how we should structure these DAO ecosystems, building a cultural playbook for community processes to enable these Commons to thrive, starting with the Trusted Seed. We believe that compiling these best practices into templates for successful launches will enable a flourishing of this new kind of commons-based community organization.

DAO ecosystems are made up of technical (code) and cultural (human) components, which means they can only succeed if both of these components are initialized and supported properly. (image: Art_To_Art_97, Pixabay)

A Cross-Disciplinary Effort

In the blockchain space, everyone is a trailblazer. Every project is experimenting wildly, building things that have never been built before, and there is little precedent to help us understand how these networks should actually be structured.

However, most of us have failed to notice that many academic disciplines have generated research that relates directly (albeit often in mind-bendingly complex ways) to the systems we are trying to build. The fields of Controls Engineering, Behavioral Economics, Swarm Robotics, Operations Research, and others provide us with a wealth of resources to draw on when it comes to the mathematics of optimization, decentralized decision-making and multi-agent coordination algorithms.² In other words, we don’t have to start from scratch! Rather than reinventing the wheel, we should consult such research and apply its findings to the real-world systems we’re trying to build. In fact, that’s what engineering is — synthesizing scientific knowledge into real-world applications.

“A couple of months in the laboratory can frequently save a couple of hours in the library.” - Frank Westheimer

If we can apply the insights that academia has to offer, we’ll be able to better flesh out steps 2–5 of the token engineering process. This will enable our community of builders to design more robust token ecosystems. As engineers and designers of public infrastructure, this is more than our objective — it’s our responsibility. That’s why $250,000 of the initial Commons Stack raise goes to further development of cadCAD as an industry-critical open-source tool, and it’s why continued support for cadCAD is one of our primary goals as we push to establish the token engineering discipline.

cadCAD is a foundational simulation tool in the future of token engineering design

Continuous Funding for Open Token Engineering R&D

The Commons Stack is so bullish on the future of Token Engineering that we are excited to see the first field test of our component library being put to use to create the Token Engineering Commons. We are inviting the token engineering community to come together as a DAO and hatch an Augmented Bonding Curve that will provide continuous funding for open token engineering R&D. We see this Commons as a collaborative solution for researchers who would otherwise be working on closed solutions for clients or doing their own R&D tinkering in their spare time. By creating a community pool of resources to fund proposals for open-source contributions to the token engineering literature, we are uniting previously siloed researchers to build up this discipline with a common incentive structure: one designed using the very techniques of token engineering!

Before we can support the Token Engineering community in launching this very first commons, we need to practice what we preach. Together with BlockScience we have researched, specified, diagrammed, formalized and simulated the first iteration of our codebase: the Augmented Bonding Curve. Now we need to prototype it, and for this we need your support. You can contribute to our fundraise right now, by applying to become a member of our Trusted Seed.

Get Involved in the Future of Token Engineering

Read more about the establishment of this emerging discipline from pioneers like Michael Zargham (BlockScience), Shermin Voshmgir (Cryptoeconomics Institute Vienna), and Trent McConaghy (Ocean Protocol).

Footnotes:

1. Modelling & simulating a system does not mean we will get all our assumptions right the first time — it means we have a consistent engineering process by which to continually improve our model in the face of new information. Think Tacoma Narrows, for example — from that point on, all engineered bridge designs included modelling for harmonic resonance.

2. For example, Token Curated Registries are just Decentralized Optimization Algorithms, which have a rich field of research examining how to resolve coordination problems and avoid systemic failure modes. With some time spent analyzing the existing literature on these topics, we will be more capable of rigorous design for smooth component operation.