diff --git a/README.md b/README.md index 4074031..2e0c34f 100644 --- a/README.md +++ b/README.md @@ -1,28 +1,51 @@ # Aragon_Conviction_Voting -[Conviction Voting](https://medium.com/commonsstack/conviction-voting-a-novel-continuous-decision-making-alternative-to-governance-62e215ad2b3d) is a novel decision making process used to estimate real-time collective preference in a distributed work proposal system. Voters continuously express their preference by staking tokens in support of proposals they would like to see approved, with the conviction (i.e. weight) of their vote growing over time. Collective conviction accumulates until it reaches a set threshold specified by a proposal according to the amount of funds requested, at which point it passes and funds are released so work may begin. Conviction voting improves on discrete voting processes by allowing participants to vote at any time, and eliminates the need for consensus on each proposal. This eliminates the governance bottleneck of large distributed communities, where a quorum of participants is required to vote on every proposal. +This cadCAD model and notebook series is a collaboration between [Aragon Project](aragon.org), [1Hive](1hive.org), [BlockScience](block.science), and [the Commons Stack](commonsstack.org). A brief table of contents follows to explain the file structure of the various documents produced in this collaboration. + +
+ +## Table of Contents + +### 1. Supporting documentation for context +* [Readme doc]() (you are here): For a high level exploration of CV and relevant topics, start here +* [Conviction Voting Algorithm Overview](https://github.com/BlockScience/Aragon_Conviction_Voting/blob/master/algorithm_overview.md): For a deeper understanding of the CV algorithm, including it's mathematical derivation, read this document +* [Deriving the Alpha Parameter](https://nbviewer.jupyter.org/github/BlockScience/Aragon_Conviction_Voting/blob/master/models/v3/Deriving_Alpha.ipynb): For an in-depth look at the specific considerations around the alpha parameter, which sets the half life decay of conviction, read this notebook +* [Explaining the Trigger Function](https://nbviewer.jupyter.org/github/BlockScience/Aragon_Conviction_Voting/blob/master/models/v3/Trigger_Function_Explanation.ipynb): For an in-depth look at the trigger function equation and how proposals pass from candidate to active status, read this notebook -## Simulations +### 2. Simulation Notebooks -* [V1 - Initial model](https://nbviewer.jupyter.org/github/BlockScience/Aragon_Conviction_Voting/blob/master/models/v1/Aragon_Conviction_Voting_Model.ipynb) -* [V2 - Full complexity model](https://nbviewer.jupyter.org/github/BlockScience/Aragon_Conviction_Voting/blob/master/models/v2/Aragon_Conviction_Voting_Model.ipynb) -* [V3 - 1Hive model](https://nbviewer.jupyter.org/github/BlockScience/Aragon_Conviction_Voting/blob/master/models/v3/Aragon_Conviction_Voting_Model.ipynb) +* [V3 - 1Hive model](https://nbviewer.jupyter.org/github/BlockScience/Aragon_Conviction_Voting/blob/master/models/v3/Aragon_Conviction_Voting_Model.ipynb): The latest notebook iteration of CV, modeling 1Hive's deployment +* [V2 - Increased complexity model](https://nbviewer.jupyter.org/github/BlockScience/Aragon_Conviction_Voting/blob/master/models/v2/Aragon_Conviction_Voting_Model.ipynb): a former version of the CV model with increased mechanism complexity over v1 +* [V1 - Initial model](https://nbviewer.jupyter.org/github/BlockScience/Aragon_Conviction_Voting/blob/master/models/v1/Aragon_Conviction_Voting_Model.ipynb): the simplest version of the CV model. Start here if you are looking to understand and replicate this model in cadCAD -## Current CV Deployments +
-### 1Hive +___ +
+ +# Current CV Deployments + +## 1Hive The [1Hive](https://www.1hive.org) community has been actively developing Conviction Voting contracts in collaboration with BlockScience and the Commons Stack since early 2019. They currently have a DAO live on the xDAI network at [1hive.org](https://www.1hive.org) that uses a native governance token (Honey) to allocate funds to proposals via Conviction Voting. To see Conviction Voting deployed in smart contracts with a basic user interface, check out the [1Hive Github](https://github.com/1Hive/conviction-voting-app). -### Commons Simulator +## Commons Simulator The [Commons Stack](https://www.commonsstack.org) has been working on a 'Commons Simulator' to facilitate user understanding of these novel governance tools. Progress on Conviction Voting can be viewed in [the Commons Stack Github repo](https://github.com/commons-stack/coodcad/tree/bigrewrite). -## Background information & concepts addressed +
+ +___ + +## Background information & concepts addressed +
+ +## Conviction Voting Basics + +[Conviction Voting](https://medium.com/commonsstack/conviction-voting-a-novel-continuous-decision-making-alternative-to-governance-62e215ad2b3d) is a novel decision making process used to estimate real-time collective preference in a distributed work proposal system. Voters continuously express their preference by staking tokens in support of proposals they would like to see approved, with the conviction (i.e. weight) of their vote growing over time. Collective conviction accumulates until it reaches a set threshold specified by a proposal according to the amount of funds requested, at which point it passes and funds are released so work may begin. Conviction voting improves on discrete voting processes by allowing participants to vote at any time, and eliminates the need for consensus on each proposal. This eliminates the governance bottleneck of large distributed communities, where a quorum of participants is required to vote on every proposal. -### Conviction Voting Basics ![](images/cv_background_1.PNG) @@ -36,10 +59,12 @@ As our governance toolkits continue to expand with novel tools like Conviction V Conviction Voting offers us new insight into the collective intent of our communities. It offers us a richer signal of the emergent and dynamic preferences of a group, such that we can better understand and discuss important issues as communities. It eliminates attack vectors of ad hoc voting such as last minute vote swings, and reduces user friction by not requiring set times to cast a vote. -### Conviction voting Algorithm -Conviction voting is based on a linear system akin to a capacitor which "charges up" dynamically and proposals pass when a certain level of collective energy is reached. The details are explained and demonstrated throughout this repo but the best place to start is [Algorithm_Overview](algorithm_overview.md). For more details on the charging up mechanics and the parameter $\alpha$ see [Alpha Parameter Explainer](https://nbviewer.jupyter.org/github/BlockScience/Aragon_Conviction_Voting/blob/master/models/v3/Deriving_Alpha.ipynb) and for more details on the trigger function see [Trigger Function Explainer](https://nbviewer.jupyter.org/github/BlockScience/Aragon_Conviction_Voting/blob/master/models/v3/Trigger_Function_Explanation.ipynb). +## Conviction Voting In-Depth +Conviction voting is based on a linear system akin to a capacitor which "charges up" dynamically and proposals pass when a certain level of collective energy is reached. The details are explained and demonstrated throughout this repo but the best place to start is the [Conviction Voting Algorithm Overview](algorithm_overview.md). For more details on the charging up mechanics and the parameter $\alpha$ see the [Deriving Alpha Parameter Explainer](https://nbviewer.jupyter.org/github/BlockScience/Aragon_Conviction_Voting/blob/master/models/v3/Deriving_Alpha.ipynb) notebook and for more details on the proposal passing mechanism, see the [Trigger Function Explainer](https://nbviewer.jupyter.org/github/BlockScience/Aragon_Conviction_Voting/blob/master/models/v3/Trigger_Function_Explanation.ipynb) notebook. -### What is cadCAD? +
+ +## What is cadCAD? cadCAD (complex adaptive dynamics Computer-Aided Design) is a python based modeling framework for research, validation, and Computer Aided Design of complex systems. Given a model of a complex system, cadCAD can simulate the impact that a set of actions might have on it. This helps users make informed, rigorously tested decisions on how best to modify or interact with the system in order to achieve their goals. cadCAD supports different system modeling approaches and can be easily integrated with common empirical data science workflows. Monte Carlo methods, A/B testing and parameter sweeping features are natively supported and optimized for. cadCAD links: @@ -47,12 +72,17 @@ cadCAD links: * https://community.cadcad.org/t/putting-cadcad-in-context/19 * https://github.com/cadCAD-org/demos -### Reproducibility +
+ +## Model Reproducibility In order to reperform this code, we recommend the researcher use the following link https://www.anaconda.com/products/individual to download Python 3.7. To install the specific version of cadCAD this repository was built with, run the following code: pip install cadCAD==0.4.18 Then run cd Aragon_Conviction_Voting to enter the repository. Finally, run jupyter notebook to open a notebook server to run the various notebooks in this repository. +
+ +## Further Background Reading ### Systems Thinking * https://community.cadcad.org/t/introduction-to-systems-thinking/18