jeffemmett
/
Aragon_Conviction_Voting
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Update README.md

This commit is contained in:
Andrew Clark 2020-08-11 10:29:18 -04:00 committed by GitHub
parent 1dc1276e44
commit 1517d88129
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
1 changed files with 1 additions and 1 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
README.md
View File

@ -12,7 +12,7 @@
## Background information & concepts addressed
### Conviction voting Algorithm
Conviction voting is based on a linear system akin to a capacitor which provides "charging up" like dynamic and proposals pass when a certain level of collective energy is charged up. 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/Deriving_Alpha_and_parameters.ipynb) and for more details on the trigger function see [Trigger Function Explainer](models/Trigger_Function_Explanation.ipynb).
Conviction voting is based on a linear system akin to a capacitor which provides "charging up" like dynamic and proposals pass when a certain level of collective energy is charged up. 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_and_parameters.ipynb) and for more details on the trigger function see [Trigger Function Explainer](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.