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Aragon Conviction Voting Model.ipynb
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"source": [
"## Model Overview \n",
"\n",
"[Conviction Voting](https://medium.com/giveth/conviction-voting-a-novel-continuous-decision-making-alternative-to-governance-aa746cfb9475) is a novel decision making process where votes express their preference for which proposals they would like to see approved in a continuous rather than discrete way. The longer the community keeps a preference on an individual proposal, the “stronger” the proposal conviction becomes. In the conviction voting model, a graph structure is used to record the the introduction and removal of participants, candidates, proposals, and their outcomes.\n",
"[Conviction Voting](https://medium.com/giveth/conviction-voting-a-novel-continuous-decision-making-alternative-to-governance-aa746cfb9475) is a novel decision making process where votes express their preference for which proposals they would like to see approved in a continuous rather than discrete way. The longer the community keeps a preference on an individual proposal, the “stronger” the proposal conviction becomes. In the conviction voting model, a graph structure is used to record the introduction and removal of participants, candidates, proposals, and their outcomes.\n",
"\n",
"## cadCAD Overview\n",
"\n",
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"\n",
"At a more granular level, to setup a model, there are system conventions and configurations that must be [followed.](https://community.cadcad.org/t/introduction-to-simulation-configurations/34)\n",
"\n",
"The way to think of cadCAD modeling is analogous to machine learning pipelines which normally consist of multiple steps when training and running a deployed model. There is preprocessing, which includes segregating features between continuous and categorical, transforming or imputing data, and then instantiating, training, and running a machine learning model with specified hyperparameters. cadCAD modeling can be thought of in the same way as states, roughly translating into features, are fed into pipelines that have built-in logic to direct traffic between different mechanisms, such as scaling and imputation. Accuracy scores, ROC, etc are analogous to the metrics that can be configured on a cadCAD model, specifying how well a given model is doing in meeting its objectives. The parameter sweeping capability of cadCAD can be thought of as a grid search, or way to find the optimal hyperparameters for a system by running through alternative scenarios. A/B style testing that cadCAD enables is used in the same way machine learning models are A/B tested, except out of the box, in providing a side by side comparison of muliple different models to compare and contract performance. Utilizing the field of Systems Identification, dynamical systems models can be used to \"online learn\" by providing a feedback loop to generative system mechanisms. \n",
"The way to think of cadCAD modeling is analogous to machine learning pipelines which normally consist of multiple steps when training and running a deployed model. There is preprocessing, which includes segregating features between continuous and categorical, transforming or imputing data, and then instantiating, training, and running a machine learning model with specified hyperparameters. cadCAD modeling can be thought of in the same way as states, roughly translating into features, are fed into pipelines that have built-in logic to direct traffic between different mechanisms, such as scaling and imputation. Accuracy scores, ROC, etc. are analogous to the metrics that can be configured on a cadCAD model, specifying how well a given model is doing in meeting its objectives. The parameter sweeping capability of cadCAD can be thought of as a grid search, or way to find the optimal hyperparameters for a system by running through alternative scenarios. A/B style testing that cadCAD enables is used in the same way machine learning models are A/B tested, except out of the box, in providing a side by side comparison of muliple different models to compare and contrast performance. Utilizing the field of Systems Identification, dynamical systems models can be used to \"online learn\" by providing a feedback loop to generative system mechanisms. \n",
"\n",
"\n",
"## Differential Specification\n",
"![](images/Aragon_v1.png)\n",
"\n",
"## Schema of the states\n",
"The model consistes of a temporal in memory graph database called *network* containing nodes of type **Participant** and type **Proposal**. Participants will have *holdings* and Proposals will have *funds_required, status*(candidate or active), *conviction*. Edges in the network go from nodes of type Participant to nodes of type Proposal with the edges having the key *type*, off which all will be set to *support*. Edges from participant $i$ to proposal $j$ will have the following additional characteristics:\n",
"The model consists of a temporal in memory graph database called *network* containing nodes of type **Participant** and type **Proposal**. Participants will have *holdings* and Proposals will have *funds_required, status*(candidate or active), *conviction*. Edges in the network go from nodes of type Participant to nodes of type Proposal with the edges having the key *type*, of which all will be set to *support*. Edges from participant $i$ to proposal $j$ will have the following additional characteristics:\n",
"* Each pairing (i,j) will have *affinity*, which determines how much $i$ likes or dislikes proposal $j$.\n",
"* Each participant $i$, assigns it's $tokens$ over the edges (i,j) for all $j$ such that the summation of all $j$ such that ```Sum_j = network.edges[(i,j)]['tokens'] = network.nodes[i]['holdings']```\n",
"* Each participant $i$, assigns its $tokens$ over the edges (i,j) for all $j$ such that the summation of all $j$ such that ```Sum_j = network.edges[(i,j)]['tokens'] = network.nodes[i]['holdings']```\n",
"* Each pairing (i,j) will have *conviction* local to that edge whose update at each timestep is computed using the value of *tokens* at that edge.\n",
"* Each proposal *j* will have a *conviction* which is equal to the sum of the conviction on its inbound edges: ```network.nodes[j]['conviction'] = Sum_i network.edges[(i,j)]['conviction']```\n",
"* The \"trigger function\" will check whether each proposal $j$ has met the criteria for passing; if a proposal passes its *status* changes from *candidate* to *active*, and an amount of funds equal to it's *funds_required* will be decremented from *funds*.\n",
"* The \"trigger function\" will check whether each proposal $j$ has met the criteria for passing; if a proposal passes its *status* changes from *candidate* to *active*, and an amount of funds equal to its *funds_required* will be decremented from *funds*.\n",
"\n",
"\n",
"The other state variable in the model is *funds*, which is a numpy floating point. \n",