Abstract
Fractal Governance[1] is reviewed and it is argued that the dynamics of people's economic behavior governed according to the fractally White Paper obey equations of motion that can be tested by experimentation using a simulator and the scientific method. The creation of a Fractal Governance simulator is motivated and areas of application are discussed.
Introduction
Self-rule government was invented 2,500 years ago and called democracy, but all attempts throughout history to put self-rule governance into practice at scale have failed by evolving into tyrannies because there was no way to prevent it. The discovery of Fractal Governance and its practical application aims to change this.
The goal of Fractal Governance is lofty: incentivize people to collaborate in the production of public goods and services that eliminates corruption and graft in the process.
The principles of Fractal Governance are described in the book More Equal Animals by Daniel Larimer, and the technical specifications for how to implement Fractal Governance at scale is defined in the fractally White Paper.
Fractal Governance directly and consistently rewards individuals for their recent past contributions towards the creation of public goods and services that also avoids the formation of Pareto distributions due to corruption and graft found in all other known forms of governance. This incorruptibility is the defining feature of Fractal Governance. Gone are the days of rewarding collusion with illicit gains (such as currency) from dishonest behavior or other questionable means.
Analogous to Bitcoin's Proof of Work consensus algorithm which rewards people for transforming stored energy, in the form of electricity, into an incorruptible public ledger of account, a collaboration of people governed in a Fractal nature also uses a Proof of Work consensus algorithm to reward people for transforming stored energy, in the form of human collaboration, into public goods and services.
The fundamental difference between the two consensus algorithms is in how rewards are allocated. The Bitcoin model allocates rewards called BTC tokens to those who consume the most electricity most consistently. The Fractal model, on the other hand, allocates rewards called Respect tokens to those who contribute the most value most consistently, as judged by their peers.
The Bitcoin consensus algorithm is prone to corruption and graft because it rewards those that obtain the most consistent source of electricity by any means whatsoever, illicit or otherwise. The Fractal Governance consensus algorithm, on the other hand, prevents corruption and graft by eliminating opportunities for collusion.
Does Fractal Governance Work in Practice?
How do we prove that a system created according to these principles and specifications will prevent tyranny? We do this through verification and validation of the system's requirements, also known as V&V in Systems Engineering.
The V&V of a system is accomplished through the scientific method of hypothesis testing through experimentation. The hypothesis under test for a Fractal Governance system is:
All known self-rule governments always evolve into tyrannies because there is nothing to prevent collusion via the formation of an opaque parallel governance process not constrained by the rules of self-rule governance. Fractal Governance is self-rule governance with constraints to prevent this collusion.
The pioneers of Fractal Governance have built two experimental apparatus to test this hypothesis:
- The Genesis social experiment
- A Fractal Governance simulator currently in development that is parameterized with models of human economic behavior that explores how fractally governed people collaborate and distribute the value of their collaboration amongst themselves.
The article First Results from the Fractal Governance Experiments was recently published that described the Genesis social experiment (item #1 in the list). These results include detailed measurements over time that allow for the creation of dynamical models that lend themselves to Monte Carlo simulation.
A Fractal Governance simulator enables V&V of a Fractal Governance system using the principles and techniques of Systems Engineering. Is Fractal Governance really incorruptible? Are rewards allocated optimally? Are efforts aligned efficiently? When should a Fractal bifurcate? When should Fractals collaborate? Are there phase transitions toward tyranny? Systems Engineering can help answer these questions given a well designed simulator.
Other applications for a Fractal Governance simulator include:
- Guiding Fractals in the development and tuning of their smart contracts
- Penetration Testing
- Further research by data scientists, researchers and educators
All processes that evolve over time are governed by equations of motion, whether they be physical processes, chemical processes or economic processes. A Fractal Governance simulator enables the discovery of the equations of motion of Fractal Governance.
Conclusion
Fractal Governance was reviewed and the creation of a Fractal Governance simulator was motivated as an important tool in the deployment of a new system of self-government designed to be impervious to evolving into tyranny.
If the framers of the United States Constitution had access to a self-governance simulator to test their constitution before deploying it, then they would have discovered its susceptibility to opaque parallel governance processes not constrained by the constitution and its inevitable evolution into something different than what they designed. A Fractal Governance simulator will help the pioneers of Fractal Governance avoid this fate.
The creation of a Fractal Governance simulator parameterized by models created from real-world data of human economic behavior is an active area of research. An article describing the initial results from this effort is in preparation and will be published to the Modeling and Simulation topic on gofractally.com in the near future.
[1]: The word fractal is used throughput this article and is capitalized as Fractal depending on the context to distinguish between the usual mathematical meaning and its specific use when describing its application to governance.