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Jeff Emmett 2020-08-11 15:12:43 -06:00
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## Simulations
* [V1 - Initial model](models/v1/Aragon_Conviction_Voting_Model.ipynb)
* [V2 - Full complexity model](models/v2/Aragon_Conviction_Voting_Model.ipynb)
* [V3 - 1Hive model](models/v3/Aragon_Conviction_Voting_Model.ipynb)
* [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)
## Background information & concepts addressed
@ -27,7 +27,7 @@ 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](models/v3/Deriving_Alpha.ipynb) and for more details on the trigger function see [Trigger Function Explainer](models/v3/Trigger_Function_Explanation.ipynb).
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).
### 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.