From 6c1ba7ad275931c6114e6cc1b0fbcd1069bf478c Mon Sep 17 00:00:00 2001 From: Michael Zargham Date: Tue, 11 Aug 2020 00:01:56 -0700 Subject: [PATCH] Update README.md --- README.md | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 0df799d..321c0c7 100644 --- a/README.md +++ b/README.md @@ -6,9 +6,14 @@ ## Simulations * [Initial model](v1/Aragon_Conviction_Voting_Model.ipynb) -* [Full complexity model](v2/Aragon_Conviction_Voting_Model.ipynb) +* [Intermediate model](v2/Aragon_Conviction_Voting_Model.ipynb) +* [Current model](v3/Aragon_Conviction_Voting_Model.ipynb) + ## 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](Deriving Alpha and parameters.ipynb) and for more details on the trigger function see [Trigger Function Explainer](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.