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{
"cells": [
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"source": [
"# Aragon Conviction Voting Model - Version 3\n",
"\n",
"New to this version 3 model are the following elements:\n",
"\n",
"* Adding the realism that not all participant tokens are being allocated to proposals at each timestep.\n",
"* Refactored parameters and system initialization to make more readable and consistent.\n",
"* Changed file structure and file names to align with emerging cadCAD standards.\n",
"* Making the distinction between effective and total supply.\n",
"* Refining alpha calculations to more accurately reflect the 1Hive implementation. Discussion of alpha and its relation to alpha in the contract and how it relates to the timescales\n",
"* Updated differential specification and write-up to respect new state variables\n",
"* Moved all unit denominations to Honey, the 1Hive governance token.\n",
"* Added system health metrics\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# An Introduction to Conviction Voting\n",
"\n",
"Conviction Voting is an approach to organizing a communities preferences into discrete decisions in the management of that communities resources. Strictly speaking conviction voting is less like voting and more like signal processing. Framing the approach and the initial algorithm design was done by Michael Zargham and published in a short research proposal [Social Sensor Fusion](https://github.com/BlockScience/conviction/blob/master/social-sensorfusion.pdf). This work is based on a dynamic resource allocation algorithm presented in Dr. Zargham's PhD Thesis.\n",
"\n",
"The work proceeded in collaboration with the Commons Stack, including expanding on the python implementation to makeup part of the Commons Simulator game. An implemention of Conviction Voting as a smart contract within the Aragon Framework was developed by 1Hive.org and is currently being used for community decision making for allocations of their community currency, Honey.\n",
"\n",
"\n",
"## The Word Problem\n",
"\n",
"Suppose a group of people want to coordinate to make a collective decision. Social dynamics such as discussions, signaling, and even changing ones mind based on feedback from others input play an important role in these processes. While the actual decision making process involves a lot of informal processes, in order to be fair the ultimate decision making process still requires a set of formal rules that the community collecively agrees to, which serves to functionally channel a plurality of preferences into a discrete outcomes. In our case we are interested in a procedure which supports asynchronous interactions, an provides visibility into likely outcomes prior to their resolution to serve as a driver of good faith, debate and healthy forms of coalition building. Furthermore, participations should be able to show support for multiple initiatives, and to vary the level of support shown. Participants have a quantity of signaling power which may be fixed or variable, homogenous or heterogenous. For the purpose of this document, we'll focus on the case where the discrete decisions to be made are decisions to allocate funds from a shared funding pool towards projects of interest to the community.\n",
"\n",
"## Converting to a Math Problem\n",
"\n",
"Let's start taking these words and constructing a mathematical representation that supports a design that meets the description above. To start we need to define participants.\n",
"\n",
"### Participants\n",
"Let $\\mathcal{A}$ be the set of participants. Consider a participant $a\\in \\mathcal{A}$. Any participant $a$ has some capacity to participate in the voting process through their token holdings $h[a]$. In a homogenous fixed token quantity system (like you might see in a democratic allocation of equal tokens per each participant), $h[a] = h$ for all $a\\in \\mathcal{A}$ where $h$ is a constant. The access control process managing how one becomes a participant determines the total supply of \"votes\" $S = \\sum_{a\\in \\mathcal{A}} = n\\cdot h$ where the number of participants is $n = |\\mathcal{A}|$. In a smart contract setting, the set $\\mathcal{A}$ is a set of addresses, and $h[a]$ is a quantity of tokens held by each address $a\\in \\mathcal{A}$. \n",
"\n",
"### Proposals & Shared Resources\n",
"Next, we introduce the idea of proposals. Consider a proposal $i\\in \\mathcal{C}$. Any proposal $i$ is associated with a request for resources $r[i]$. Those requested resources would be allocated from a constrained pool of communal resources currently totaling $R$. The pool of resources may become depleted because when a proposal $i$ passes the reserve is decremented by $R^+= R-r[i]$. Therefore it makes sense for us to consider what fraction of the shared resources are being request $\\mu_i = \\frac{r[i]}{R}$, which means that thre resource depletion from passing proposals can be bounded by requiring $\\mu_i < \\mu$ where $\\mu$ is a constant representing the maximum fraction of the shared resources which can be dispersed by any one proposal. In order for the system to be sustainable a source of new resources is required. In the case where $R$ is funding, new funding can come from revenues, donations, or in some DAO use cases minting tokens.\n",
"\n",
"### Participants Preferences for Proposals\n",
"\n",
"Most of the interesting information in this system is distributed amongst the participants and it manifests as preferences over the proposals. This can be visualized as a matrix $W\\in \\mathbb{R}^{n \\times m}$, with participants holding randomized affinities from -1 to +1 over all proposals.\n",
"![](https://i.imgur.com/Rk2BYKd.png)\n",
"\n",
"These private hidden signals drive discussions and voting actions. Each participant individually decides how to allocate their votes across the available proposals. Participant $a$ supports proposal $i$ by setting $x[a,i]>0$ but they are limited by their total token holdings $\\sum_{k\\in \\mathcal{C}} x[a,k] \\le h[a]$. Assuming each participant chooses a subset of the proposals to support, a support graph is formed.\n",
"![](https://i.imgur.com/KRh8tKn.png)\n",
"\n",
"## Aggregating Information\n",
"\n",
"In order to break out of the synchronous ad hoc voting model, a dynamical systems model of this system is introduced, which is explored further below.\n",
"\n",
"### Participants Allocate Voting Power\n",
"![](https://i.imgur.com/DZRDwk6.png)\n",
"\n",
"In the above diagram, we examine the participant view. Participant $a$ with holdings $h$ at time $t$ supports proposals $i$ and $j$ with $x$ conviction. The sum of all conviction asserted by participant $a$ is between 0 and the total holdings of participant $a$.\n",
"\n",
"### System Accounts Proposal Conviction\n",
"![](https://i.imgur.com/euAei5R.png)\n",
"\n",
"In the above diagram, we examine the proposal view. Proposal $j$ with total conviction $y$ at time $t$ is supported by participants $a$, $b$ and $c$ with $x$ conviction. The total conviction $y$ at time $t+1$ is equal to the total conviction at time $t$ decremented by an exponential decay $\\\\alpha$ plus the sum of all conviction from $k$ agents in time step $t$.\n",
"\n",
"### Understanding Alpha\n",
"Below are some graphs used to demonstrate, play with, and understand the shapes and choices for the $\\\\alpha$ parameter, which regulates the half life decay rate of the agent preference conviction growth and decay. Alpha from solidity code is defined as: \n",
"```uint256 _decay = 9999599; // 3 days halftime. halftime_alpha = (1/2)**(1/t)```\n",
"Half life associated with solidity code alpha (in number of blocks on xDai). \n",
"\n",
"The half-life of this system is defined by $T$ such that $y_T = y_0/2$ which satisfies the equation\n",
"\n",
"$\\frac{1}{2} = \\alpha^T$\n",
"\n",
"thus the Half life in epochs for a given $\\alpha$ is\n",
"\n",
"$T = \\log_\\alpha \\frac{1}{2} = -\\log_\\alpha 2$\n",
"\n",
"and conversely the $\\alpha$ which achieves a desired half-life $T$ is\n",
"\n",
"$\\alpha = 2^{-1/T} = \\frac{1}{\\sqrt[T]{2}}$\n",
"\n",
"Further note that the relationship between $T$ and $\\alpha$ is sensative to timescaling. Suppose we wanted a half-life of 3 days but our discrete time scale simulation has 1 day timesteps, then we must use $T = 3$ in the above equation to equal:\n",
"\n",
"$\\alpha = 2^{-1/3}$\n",
"\n",
"\n",
"See the [Deriving_Alpha](Deriving_Alpha.ipynb) notebook for more details around alpha and how it is derived.\n",
"\n",
"\n",
"## Converting Signals to Discrete Decisions\n",
"\n",
"Conviction can be considered like a fluctuating kinetic energy, with the Trigger function acting as a required activation energy for proposals to pass. This is the mechanism by which a continuous community preference turns into a discrete action event: passing a proposal. See [Trigger Function Explanation](Trigger_Function_Explanation.ipynb) for more details around the trigger function and how it works."
]
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"cell_type": "markdown",
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"### Resolving Passed Proposals\n",
"\n",
"![](images/stockflow_cv_trigger.png)\n",
"\n",
"This diagram shows the trigger function logic, which depends on token supply $S$, total resources available $R$ and total conviction $y$ at time $t$, as well as the proposal's requested resources $r$, the maximum share of funds a proposal can take ($\\beta$) and a tuning parameter for the trigger function ($\\rho$). Essentially, this function controls the maximum amount of funds that can be requested by a proposal ($\\beta$), using an equation resembling electron repulsion to ensure conviction increases massively beyond that point.\n",
"\n",
"## Social Systems Modeling\n",
"\n",
"In the conviction voting model, multiple graph structures are used to represent participants and proposals to represent a subjective, exploratory modeling of the social system interacting.\n",
"\n",
"### Sentiment\n",
"\n",
"Global Sentiment denotes the outside world appreciating the output of the community.\n",
"Local Sentiment denotes the agents within the system feeling good about the community.\n",
"Sentiment increases when proposals pass and work is completed in the community, and decreases when proposals fail and community progress stalls.\n",
"\n",
"### Relationships between Participants\n",
"\n",
"Edges from participant to participant denote influence (to represent subjective social influence) and are assigned randomly as mixing processes.\n",
"\n",
"### Relationships between Proposals\n",
"\n",
"Edges from proposal to proposal represent conflict, either positive or negative.\n",
"Some proposals are synergistic (passing one makes the other more desirable).\n",
"Some proposals are (partially) substitutable (passing one makes the other less desirable).\n",
"\n",
"\n",
"### Notion of Honey supply\n",
"#### Total supply = $S$\n",
"#### Effective supply = $E$, honey committed towards votes (whether for proposals or abstain)\n",
"#### Funding Pool = $F$, community funding pool where proposals are funded from\n",
"#### Other supply = $L$, effectively slack. Funds could be in cold storage, in liquidity pools or otherwise in any address not actively participating in conviction voting.\n",
"$$S = F + E + L$$ \n",
"\n",
"System has the right to do direct mints:\n",
"$$F^+ = F + minted tokens$$\n",
"$$S^+ = S + minted tokens$$\n",
"\n",
"The system may also see the arrival of new funds which come from outside supply and are donated to the funding pool:\n",
"$$L^+ = L - donated tokens$$\n",
"$$F^+ = F + donated tokens$$\n",
"\n",
"When tokens are added to a liquidity pool or cold wallet and removed from staking on proposals:\n",
"$$L^+ = L + tokens$$ \n",
"$$E^+ = E - tokens$$ \n",
"\n",
"When tokens are removed from a liquidity pool or cold wallet and staked towards proposals:\n",
"$$L^+ = L - tokens$$ \n",
"$$E^+ = E + tokens$$\n",
"\n",
"Tokens in $L$ or $E$ are defined at the level of the account holding them.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## cadCAD Overview\n",
"\n",
"In the cadCAD simulation [methodology](https://community.cadcad.org/t/differential-specification-syntax-key/31), we operate on four layers: **Policies, Mechanisms, States**, and **Metrics**. Information flows do not have explicit feedback loop unless noted. **Policies** determine the inputs into the system dynamics, and can come from user input, observations from the exogenous environment, or algorithms. **Mechanisms** are functions that take the policy decisions and update the States to reflect the policy level changes. **States** are variables that represent the system quantities at the given point in time, and **Metrics** are computed from state variables to assess the health of the system, essentially views on a complex data structure. Metrics can often be thought of as KPIs, or Key Performance Indicators. \n",
"\n",
"\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 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",
"cadCAD models are micro founded with metrics being at the macro or the institutional level. If you are interested in insitutional dynamics, see Dr. Zargham's recent paper: [Voshmgir, Shermin and Zargham, Michael (2019) Foundations of Cryptoeconomic Systems. Working Paper Series / Institute for Cryptoeconomics / Interdisciplinary Research, 1. Research Institute for Cryptoeconomics, Vienna.](https://epub.wu.ac.at/7309/)\n",
"\n",
"\n",
"## Differential Specification \n",
"![](images/Aragon_v3.png)\n",
"\n",
"## File structure\n",
"* ```Aragon_Conviction_Voting_Model.ipynb```\n",
"* model\n",
"\n",
"In the model folder there exist 4 files and folder, the [```config.py```](model/config.py), [```partial_state_update_block.py```](model/partial_state_update_block.py), [```run.py```](model/run.py), and [```state_variables.py```](model/state_variables.py). The [```config.py```](model/config.py) contains the simulation configurations, aggregating the partial states, and the state variables. The [```partial_state_update_block.py```](model/partial_state_update_block.py) contains the partial state update blocks and how they update the state variables. [```state_variables.py```](model/state_variables.py) defines the state variables and [```run.py```](model/run.py) actually runs the simulation.\n",
"\n",
"The mechanisms of the model live within the parts subfolder as:\n",
"* [```system.py```](model/parts/system.py)\n",
"* [```participants.py```](model/parts/participants.py)\n",
"* [```proposals.py```](model/parts/proposals.py)\n",
"\n",
"The initial parameters and hyperparameters of the system are defined in [```sys_params.py```](model/sys_params.py) and helper functions, plots, trigger function, etc are in the [```utils.py```](model/utils.py).\n",
"\n",
"### Note:\n",
"When running this notebook simulation, be sure to run from \"Kernal\" -> \"Restart & Run All\"\n",
"\n",
"## Schema of the states \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 *sentiment* and Proposals will have *funds_required, status* (candidate or active), and *conviction* The model as three kinds of edges:\n",
"* (Participant, participant), we labeled this edge type \"influencer\" and it contains information about how the preferences and sentiment of one participant influence another.\n",
"* (Proposal, Proposal), we labeled this edge type \"conflict\" and it contains information about how synergistic or anti-synergistic two proposals are; basically people are likely to support multiple things that have synergy (meaning once one is passed there is more utility from the other) but they are not likely to pass things that have antisynergy (meaning once one is passed there is less utility from the other).\n",
"* The edges between Participant and Proposal, which are described below.\n",
" \n",
"\n",
"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 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']```. This value of tokens for participants on proposals must be less than or equal to the total number of tokens held by the participant.\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",
"\n",
"\n",
"The other state variables in the model are *funds*, *sentiment*, *effective_supply*, *total_supply*, and the metrics variables of: *fractionOfSupplyForVoting*, *fractionOfSupplyInPool*, *fractionOfProposalStages*, *fractionOfFundStages*.\n",
"\n",
"The system consists of 100 time steps without a parameter sweep or monte carlo.\n",
"\n",
" \n",
"## Partial State Update Blocks \n",
"\n",
"Each partial state update block is kind of a like a phase in a phased based board game. Everyone decides what to do and it reconciles all decisions. One timestep is a full turn, with each block being a phase of a timestep or turn. We will walk through the individaul Partial State update blocks one by one below."
]
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"cell_type": "markdown",
"metadata": {},
"source": [
"```\n",
"{\n",
"# system.py: \n",
"'policies': { \n",
" 'random': driving_process\n",
"},\n",
"'variables': {\n",
" 'network': update_network,\n",
" 'effective_supply':increment_supply,\n",
"}\n",
"```\n",
"\n",
"To simulate the arrival of participants and proposal into the system, we have a driving process to represent the arrival of individual agents. We use a random uniform distribution generator, over [0, 1), to calculate the number of new participants. We then use an exponential distribution to calculate the particpant's tokens by using a loc of 0.0 and a scale of expected holdings, which is calculated by .1*supply/number of existing participants. We calculate the number of new proposals by \n",
"```\n",
"proposal_rate = 1/median_affinity * (1+total_funds_requested/funds)\n",
"rv2 = np.random.rand()\n",
"new_proposal = bool(rv2<1/proposal_rate)\n",
"```\n",
"The network state variable is updated to include the new participants and proposals, while the *effective_supply* state variable is updated for the addition of new particpant's funds. \n",
"```\n",
" {\n",
" 'policies': { \n",
" 'random': minting_rule\n",
" },\n",
" 'variables': {\n",
" 'total_supply': mint_to_supply,\n",
" 'funds':mint_to_funds,\n",
"\n",
" }\n",
"},\n",
"```\n",
"A behavior called *minting_rule* is included to record the general expansion of system supply every timestep. The *total_supply* and *funds* state variables are incrased with these minted values.\n",
"[To see the partial state update's code, click here](model/parts/system.py)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```\n",
"{\n",
" # participants.py \n",
" 'policies': {\n",
" 'completion': check_progress \n",
" },\n",
" 'variables': { \n",
" 'sentiment': update_sentiment_on_completion, #not completing projects decays sentiment, completing bumps it\n",
" 'network': complete_proposal\n",
" }\n",
"},\n",
"```\n",
"\n",
"In the next phase of the turn, [to see the logic code, click here](model/parts/participants.py), the *check_progress* behavior checks for the completion of previously funded proposals. The code calculates the completion and failure rates as follows:\n",
"\n",
"```\n",
"likelihood = 1.0/(base_completion_rate+np.log(grant_size))\n",
"\n",
"failure_rate = 1.0/(base_failure_rate+np.log(grant_size))\n",
"if np.random.rand() < likelihood:\n",
" completed.append(j)\n",
"elif np.random.rand() < failure_rate:\n",
" failed.append(j)\n",
"```\n",
"With the base_completion_rate being 100 and the base_failure_rate as 200. \n",
"\n",
"The mechanism then updates the respective *network* nodes and updates the sentiment variable on proposal completion. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```\n",
" # proposals.py\n",
" 'policies': {\n",
" 'release': trigger_function \n",
" },\n",
" 'variables': { \n",
" 'funds': decrement_funds, \n",
" 'sentiment': update_sentiment_on_release, #releasing funds can bump sentiment\n",
" 'network': update_proposals \n",
" }\n",
"},\n",
" ```\n",
" \n",
"The [trigger release function](model/parts/proposals.py) checks to see if each proposal passes or not. If a proposal passes, funds are decremented by the amount of the proposal, while the proposal's status is changed in the network object."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```\n",
"{ \n",
" # participants.py\n",
" 'policies': { \n",
" 'participants_act': participants_decisions\n",
" },\n",
" 'variables': {\n",
" 'network': update_tokens \n",
" }\n",
"}\n",
"```\n",
"\n",
"The Participants decide based on their affinity if which proposals they would like to support,[to see the logic code, click here](model/parts/participants.py). Proposals that participants have high affinity for receive more support and pledged tokens than proposals with lower affinity and sentiment. We then update everyone's holdings and their conviction for each proposal.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```\n",
" {\n",
" # metrics.py\n",
" 'policies': {\n",
" 'calculations': kpi_calculations\n",
" },\n",
" 'variables':{\n",
" 'fractionOfSupplyForVoting': kpi_fractionOfSupplyForVoting,\n",
" 'fractionOfSupplyInPool': kpi_fractionOfSupplyInPool,\n",
" 'fractionOfProposalStages':kpi_proposal_stages,\n",
" 'fractionOfFundStages': kpi_fractionOfFundStages\n",
" }\n",
" }\n",
"```\n",
"\n",
"In the Metrics section we create KPI's to calculate the system's health. To see the logic, [click here](model/parts/metrics.py)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Model next steps\n",
"\n",
"The the model described above is the third iteration model that covers the core mechanisms of the Aragon Conviction Voting model. Below are next additional dynamics we can attend to enrich the model, and provide workstreams for subsequent iterations of this lab notebook.\n",
"\n",
"* Add a uniswap instance\n",
" * A next step to model the 1Hive ecosystem would be to model the Uniswap interface. With this interface, agents would be able to add or remove liquidity, buy or redeem Honey for more voting power, and ultimately enter or leave the system. \n",
"* Mixing of token holdings among participants\n",
" * Introducing heterogeneous token holdings would be another next step in creating a model more representative of the live system.\n",
"* Proposals which are good or no good together\n",
" * Introducing conflict \n",
"* Affects of outcomes on sentiment"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Simulation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Configuration\n",
"Let's factor out into its own notebook where we review the config object and its partial state update blocks. We will initialize the network x object and pull out the simulation configuration, state_variables, and partial state update blocks from the [config.py](model/config.py)."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/aclarkdata/anaconda3/lib/python3.7/site-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
" import pandas.util.testing as tm\n"
]
}
],
"source": [
"from model import config\n",
"from model.parts.sys_params import initial_values\n",
"from model.parts.utils import *"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"\n",
"# pull out configurations to illustrate\n",
"sim_config,state_variables,partial_state_update_blocks = config.get_configs()\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[{'policies': {'random': <function model.parts.system.driving_process(params, step, sL, s)>},\n",
" 'variables': {'network': <function model.parts.system.update_network(params, step, sL, s, _input)>,\n",
" 'effective_supply': <function model.parts.system.increment_supply(params, step, sL, s, _input)>}},\n",
" {'policies': {'random': <function model.parts.system.minting_rule(params, step, sL, s)>},\n",
" 'variables': {'total_supply': <function model.parts.system.mint_to_supply(params, step, sL, s, _input)>,\n",
" 'funds': <function model.parts.system.mint_to_funds(params, step, sL, s, _input)>}},\n",
" {'policies': {'completion': <function model.parts.participants.check_progress(params, step, sL, s)>},\n",
" 'variables': {'sentiment': <function model.parts.participants.update_sentiment_on_completion(params, step, sL, s, _input)>,\n",
" 'network': <function model.parts.participants.complete_proposal(params, step, sL, s, _input)>}},\n",
" {'policies': {'release': <function model.parts.proposals.trigger_function(params, step, sL, s)>},\n",
" 'variables': {'funds': <function model.parts.proposals.decrement_funds(params, step, sL, s, _input)>,\n",
" 'sentiment': <function model.parts.proposals.update_sentiment_on_release(params, step, sL, s, _input)>,\n",
" 'network': <function model.parts.proposals.update_proposals(params, step, sL, s, _input)>}},\n",
" {'policies': {'participants_act': <function model.parts.participants.participants_decisions(params, step, sL, s)>},\n",
" 'variables': {'network': <function model.parts.participants.update_tokens(params, step, sL, s, _input)>}},\n",
" {'policies': {'calculations': <function model.parts.metrics.kpi_calculations(params, step, sL, s)>},\n",
" 'variables': {'fractionOfSupplyForVoting': <function model.parts.metrics.kpi_fractionOfSupplyForVoting(params, step, sL, s, _input)>,\n",
" 'fractionOfSupplyInPool': <function model.parts.metrics.kpi_fractionOfSupplyInPool(params, step, sL, s, _input)>,\n",
" 'fractionOfProposalStages': <function model.parts.metrics.kpi_proposal_stages(params, step, sL, s, _input)>,\n",
" 'fractionOfFundStages': <function model.parts.metrics.kpi_fractionOfFundStages(params, step, sL, s, _input)>}}]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"partial_state_update_blocks"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Parameters\n",
"\n",
"Initial values are the starting values for the simulation."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'sentiment': 0.6,\n",
" 'n': 30,\n",
" 'm': 7,\n",
" 'funds': 4867.21,\n",
" 'supply': 22392.22,\n",
" 'params': {'beta': 0, 'rho': 0.0025, 'alpha': 0.7937005259840998}}"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"initial_values"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$n$ is initial participants, whereas $m$ is initial proposals.\n",
"\n",
"Sim_config holds the global hyperparameters for the simulations"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'beta': 0,\n",
" 'rho': 0.0025,\n",
" 'alpha': 0.7937005259840998,\n",
" 'gamma': 0.001,\n",
" 'sensitivity': 0.75,\n",
" 'tmin': 1,\n",
" 'min_supp': 1,\n",
" 'base_completion_rate': 45,\n",
" 'base_failure_rate': 180,\n",
" 'base_engagement_rate': 0.3,\n",
" 'lowest_affinity_to_support': 0.3}"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sim_config[0]['M']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Initial state variable values"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'network': <networkx.classes.digraph.DiGraph at 0x7ff3182bc090>,\n",
" 'funds': 4867.21,\n",
" 'sentiment': 0.6,\n",
" 'effective_supply': 14020.008000000002,\n",
" 'total_supply': 22392.22,\n",
" 'fractionOfSupplyForVoting': 0,\n",
" 'fractionOfSupplyInPool': 0,\n",
" 'fractionOfProposalStages': 0,\n",
" 'fractionOfFundStages': 0}"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"state_variables"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Hyperparameter explanations:\n",
"* $\\beta$ = .2 Upper bound on share of funds dispersed in the example Trigger Function\n",
"* $\\rho$ = 0.002 Scale Parameter for the example Trigger Function\n",
"* $\\alpha$ : 0.79370 timescale set in days with 3 day halflife\n",
"* $\\gamma$: 0.001 The expansion of supply per per day\n",
"* sensitivity of participant decisions to changes in affinity \n",
"* tmin = 1 unit days; minimum periods passed before a proposal can pass\n",
"* min_supp = 50 number of tokens that must be stake for a proposal to be a candidate\n",
"* base_completion_rate: 45, expected number of days to complete a proposals.\n",
"* base_failure_rate: 180, expected number of days until a proposal will fail\n",
"* base_engagement_rate: 0.3, probability of being active on a certain day \n",
"* lowest_affinity_to_support: 0.3, lowest affinity to required to support a proposal\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Exploring the State Data Structure\n",
"\n",
"A graph is a type of temporal data structure that evolves over time. A graph $\\mathcal{G}(\\mathcal{V},\\mathcal{E})$ consists of vertices or nodes, $\\mathcal{V} = \\{1...\\mathcal{V}\\}$ and is connected by edges $\\mathcal{E} \\subseteq \\mathcal{V} \\times \\mathcal{V}$.\n",
"\n",
"See *Schema of the states* above for more details\n",
"\n",
"\n",
"Let's explore!"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"# To explore our model prior to the simulation, we extract key components from our networkX object into lists.\n",
"proposals = get_nodes_by_type(state_variables['network'], 'proposal')\n",
"participants = get_nodes_by_type(state_variables['network'], 'participant')\n",
"supporters = get_edges_by_type(state_variables['network'], 'support')\n",
"influencers = get_edges_by_type(state_variables['network'], 'influence')\n",
"competitors = get_edges_by_type(state_variables['network'], 'conflict')"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'type': 'participant',\n",
" 'holdings': 234.3841073015196,\n",
" 'sentiment': 0.6109858547634346}"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#sample a participant\n",
"state_variables['network'].nodes[participants[0]]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0, 0.5, 'Count of Participants')"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Let's look at the distribution of participant holdings at the start of the sim\n",
"plt.hist([ state_variables['network'].nodes[i]['holdings'] for i in participants])\n",
"plt.title('Histogram of Participants Token Holdings')\n",
"plt.xlabel('Amount of Honey')\n",
"plt.ylabel('Count of Participants')\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/aclarkdata/anaconda3/lib/python3.7/site-packages/networkx/drawing/nx_pylab.py:563: MatplotlibDeprecationWarning: \n",
"The iterable function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use np.iterable instead.\n",
" if not cb.iterable(width):\n",
"/home/aclarkdata/anaconda3/lib/python3.7/site-packages/networkx/drawing/nx_pylab.py:660: MatplotlibDeprecationWarning: \n",
"The iterable function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use np.iterable instead.\n",
" if cb.iterable(node_size): # many node sizes\n"
]
},
{
"data": {
"text/plain": [
"Text(0.5, 1.0, 'Participants Social Network')"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"nx.draw_spring(state_variables['network'], nodelist = participants, edgelist=influencers)\n",
"plt.title('Participants Social Network')"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'type': 'proposal',\n",
" 'conviction': 0,\n",
" 'status': 'candidate',\n",
" 'age': 0,\n",
" 'funds_requested': 2147.7938983247623,\n",
" 'trigger': inf}"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#lets look at proposals\n",
"state_variables['network'].nodes[proposals[0]]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Proposals initially start without any conviction, and with the status of a candidate. If the proposal's amount of conviction is greater than it's trigger, then the proposal moves to active and it's funds requested are granted. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"All initial proposal start with 0 conviction and state 'candidate'we can simply examine the amounts of funds requested"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"funds_array = np.array([state_variables['network'].nodes[i]['funds_requested'] for i in proposals])\n",
"conviction_required = np.array([trigger_threshold(r, initial_values['funds'], initial_values['supply'], sim_config[0]['M']['alpha'],sim_config[0]['M']) for r in funds_array])"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0, 0.5, 'Amount of Honey requested(as a Fraction of Funds available)')"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.bar( proposals, funds_array/initial_values['funds'])\n",
"plt.title('Bar chart of Proposals Funds Requested')\n",
"plt.xlabel('Proposal identifier')\n",
"plt.ylabel('Amount of Honey requested(as a Fraction of Funds available)')"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0, 0.5, 'Amount of Conviction')"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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Iu7shFjMzW0FFrsL6XZWyiV2dcUQsBY4DbgYeAa6JiJmSxks6KFW7CFhP0hzgBKDtUt/jgK2A70makT4bdjUmMzMrrqNzINsB2wP9K86DrAus0R0zj4g/AX+qKPtervt14NNVxjsNOK07YjAzs3I6OgeyLXAAMIDlz4O8AvxHLYMyM7PG19E5kBuAGyTtGhH39GBMZmbWBIqcAzk2nbQGQNJASRfXMCYzM2sCRRLI+yPixbaeiHgB2LF2IZmZWTMokkD6SBrY1pPeRljkEShmZrYSK5IIzgbukXQt2bOwDgVOr2lUZmbW8Io8C+sySa1A2yPTD4mIhzsax8zMVn4d3QeybkS8nA5ZPQ1cmRs2KCIW90SAZmbWmDraA7mS7D6Q6WTPvmqj1L9lDeMyM7MG19F9IAekv93x3CszM1vJdHoVlqRJkkZLWrMnAjIzs+ZQ5DLes4GPAI9ImijpUEnd8iwsMzNrXkWuwroTuDO9w3wPsudgXUz2UEUzM+ulCt0QKKkf2QMVPwvsBFxay6DMzKzxdZpAJF0D7AzcBJwH3BkRb9c6MDMza2xF9kAuAkZHxLJaB2NmZs2jyDmQmyXtJmlYvn5EXFbDuMzMrMEVOYR1OTAcmAG07YUE4ARiZtaLFTmE1QKMiIjotKaZmfUaRe4DeQjYqNaBmJlZcymyB7I+8LCkqcAbbYURcVDNojIzs4ZXJIGMq3UQZmbWfArdiS5pMPChVDQ1Ip6tbVhmZtboijxM8TPAVODTwGeAeyUdWuvAzMyssRU5hHUy8KG2vQ5JGwC3ARNrGZiZmTW2Ildh9ak4ZLWo4HhmZrYSK7IHcpOkm4Hfpv7PAn+uXUhmZtYMipxEP0nSIcDuqWhCRFxf27DMzKzRtZtAJG0FDI6IuyPiOuC6VL67pOER8VhPBWlmZo2no3MZ5wIvVyl/KQ0zM7NerKMEMjgiHqwsTGXDahaRmZk1hY4SyIAOhvXrjplL2k/SLElzJI2tMnx1SVen4femR8q3DftOKp8lad/uiMfMzIrrKIG0SvqPykJJRwPTuzrj9I71nwP7AyOA0ZJGVFT7EvBCRGwFnAOcmcYdARwGbA/sB5yfpmdmZj2ko6uwjgeul3Q47yaMFmA14JPdMO+dgTkRMRdA0lXAKODhXJ1RvPssronAeZKUyq+KiDeAxyXNSdO7pxviMjOzAtpNIBHxDLCbpI8B70vFN0bE7d00702Bebn++cAu7dWJiKWSXgLWS+V/qxh302ozkXQMcAzA0KFDuyVwMzMrdh/IHcAdPRBLTUTEBGACQEtLi1+KZWbWTer5SJIFwGa5/iGprGodSX2B/mSPUikyrpmZ1VC7CUTS6jWe9zRga0lbSFqN7KT4pIo6k4AxqftQ4Pb0at1JwGHpKq0tgK3JnhhsZmY9pKM9kHsAJF1eixlHxFLgOOBm4BHgmoiYKWm8pLa3HV4ErJdOkp8AjE3jzgSuITvhfhPwtYhYVos4zcysOmUb9FUGSA8BPwBOBU6qHJ4eb9JUWlpaorW1td5hmJk1FUnTI6Klsryjk+jHAoeT3VB4YMWwID0by8zMeqeOLuP9K/BXSa0RcVEPxmRmZk2gyPtALpf0DeCjqf9O4BcR8VbtwjIzs0ZXJIGcD6ya/gIcAVwAHF2roMzMrPEVSSAfiogP5Ppvl/RArQIyM7PmUORGwmWShrf1SNoS8CWzZma9XJE9kJOAOyTNBQRsDhxV06jMzKzhFXkW1mRJWwPbpqJZ6Sm4ZmbWixXZAyEljL/XOBYzM2si9XyYopmZNTEnEDMzK6XTBCJpcpEyMzPrXdo9ByJpDWBNYH1JA8muwAJYl3be/mdmZr1HRyfRv0z2XvRNyN6J3pZAXgbOq3FcZmbW4Dp6mOJPgJ9I+npE/KwHYzIzsyZQ5D6Qn0naDRiWrx8Rl9UwLjMza3CdJpD0RsLhwAzefYRJAE4gZma9WJEbCVuAEdHeqwvNzKxXKnIfyEPARrUOxMzMmkuRPZD1gYclTQXeeQZWRBxUs6jMzKzhFUkg42odhJmZNZ8iV2Hd2ROBmJlZcylyFdYrZFddAaxG9nrbVyNi3VoGZmZmja3IHsg6bd2SBIwCPlzLoMzMrPGt0NN4I/N7YN8axWNmZk2iyCGsQ3K9fcjuC3m9ZhGZmVlTKHIV1oG57qXAE2SHsczMrBcrcg7kqJ4IxMzMmkuRF0oNkXS9pGfT53eShvREcGZm1riKnET/NTCJ7L0gmwB/SGVmZtaLFUkgG0TEryNiafpcAmzQlZlKGiTpVkmz09+B7dQbk+rMljQmla0p6UZJj0qaKemMrsRiZmblFEkgiyR9XtIq6fN5YFEX5zsWmBwRWwOTU/9yJA0CTgF2AXYGTsklmh9FxHbAjsC/Sdq/i/GYmdkKKpJAvgh8BngaWAgcCnT1xPoo4NLUfSlwcJU6+wK3RsTiiHgBuBXYLyJei4g7ACLiTeA+wOdkzMx6WJGrsJ4EuvvJu4MjYmHqfhoYXKXOpsC8XP/8VPYOSQPILjP+STfHZ2ZmnShyI+EWwNd57yttO0wqkm6j+ntETs73RERIWuGXVUnqC/wW+GlEzO2g3jHAMQBDhw5d0dmYmVk7itxI+HvgIrKrr94uOuGI2Ku9YZKekbRxRCyUtDHwbJVqC4CRuf4hwJRc/wRgdkSc20kcE1JdWlpa/FZFM7NuUiSBvB4RP+3m+U4CxgBnpL83VKlzM/CD3InzfYDvAEg6DegPHN3NcZmZWUFFTqL/RNIpknaVtFPbp4vzPQPYW9JsYK/Uj6QWSb8CiIjFwKnAtPQZHxGL002MJwMjgPskzZDkRGJm1sOK7IHsABwB7MG7h7Ai9ZcSEYuAPauUt5Lbq4iIi4GLK+rMB1R23mZm1j2KJJBPA1umS2bNzMyAYoewHgIG1DoQMzNrLkX2QAYAj0qaBrzRVtjZZbxmZrZyK5JATql5FGZm1nSK3Il+Z75f0u7AaODO6mOYmVlvUGQPBEk7Ap8jO6H+OPC7WgZlZmaNr90EImkbsj2N0cDzwNWAIuJjPRSbmZk1sI72QB4F7gIOiIg5AJK+2SNRmZlZw+voMt5DyB7ffoekCyXtiW/gMzOzpN0EEhG/j4jDgO2AO4DjgQ0lXSBpn54K0MzMGlOnNxJGxKsRcWVEHEj2RNz7gW/XPDIzM2toRe5Ef0dEvBAREyLiPc+xMjOz3mWFEoiZmVkbJxAzMyvFCcTMzEpxAjEzs1KcQMzMrBQnEDMzK8UJxMzMSnECMTOzUpxAzMysFCcQMzMrxQnEzMxKcQIxM7NSnEDMzKwUJxAzMyvFCcTMzEpxAjEzs1KcQMzMrBQnEDMzK8UJxMzMSqlLApE0SNKtkmanvwPbqTcm1ZktaUyV4ZMkPVT7iM3MrFK99kDGApMjYmtgcupfjqRBwCnALsDOwCn5RCPpEGBJz4RrZmaV6pVARgGXpu5LgYOr1NkXuDUiFkfEC8CtwH4AktYGTgBO64FYzcysinolkMERsTB1Pw0MrlJnU2Bern9+KgM4FTgbeK2zGUk6RlKrpNbnnnuuCyGbmVle31pNWNJtwEZVBp2c74mIkBQrMN0PAsMj4puShnVWPyImABMAWlpaCs/HzMw6VrMEEhF7tTdM0jOSNo6IhZI2Bp6tUm0BMDLXPwSYAuwKtEh6giz+DSVNiYiRmJlZj6nXIaxJQNtVVWOAG6rUuRnYR9LAdPJ8H+DmiLggIjaJiGHA7sA/nDzMzHpevRLIGcDekmYDe6V+JLVI+hVARCwmO9cxLX3GpzIzM2sAiug9pwVaWlqitbW13mGYmTUVSdMjoqWy3Heim5lZKU4gZmZWihOImZmV4gRiZmalOIGYmVkpTiBmZlaKE4iZmZXiBGJmZqU4gZiZWSlOIGZmVooTiJmZleIEYmZmpTiBmJlZKU4gZmZWihOImZmV4gRiZmalOIGYmVkpTiBmZlaKE4iZmZXiBGJmZqU4gZiZWSlOIGZmVooTiJmZleIEYmZmpSgi6h1Dj5H0HPBkveNYQesDz9c7iB7mNvcObnPz2DwiNqgs7FUJpBlJao2IlnrH0ZPc5t7BbW5+PoRlZmalOIGYmVkpTiCNb0K9A6gDt7l3cJubnM+BmJlZKd4DMTOzUpxAzMysFCeQBiBpkKRbJc1Ofwe2U29MqjNb0pgqwydJeqj2EXddV9osaU1JN0p6VNJMSWf0bPQrRtJ+kmZJmiNpbJXhq0u6Og2/V9Kw3LDvpPJZkvbtybi7omybJe0tabqkB9PfPXo69jK6so7T8KGSlkg6sadi7hYR4U+dP8BZwNjUPRY4s0qdQcDc9Hdg6h6YG34IcCXwUL3bU+s2A2sCH0t1VgPuAvavd5vaaecqwGPAlinWB4ARFXW+CvwidR8GXJ26R6T6qwNbpOmsUu821bjNOwKbpO73AQvq3Z5atjc3fCJwLXBivduzIh/vgTSGUcClqftS4OAqdfYFbo2IxRHxAnArsB+ApLWBE4DTeiDW7lK6zRHxWkTcARARbwL3AUN6IOYydgbmRMTcFOtVZG3Pyy+LicCekpTKr4qINyLicWBOml6jK93miLg/Ip5K5TOBfpJW75Goy+vKOkbSwcDjZO1tKk4gjWFwRCxM3U8Dg6vU2RSYl+ufn8oATgXOBl6rWYTdr6ttBkDSAOBAYHItguwGnbYhXycilgIvAesVHLcRdaXNeZ8C7ouIN2oUZ3cp3d608fdt4Ps9EGe361vvAHoLSbcBG1UZdHK+JyJCUuFrqyV9EBgeEd+sPK5ab7Vqc276fYHfAj+NiLnlorRGJGl74Exgn3rHUmPjgHMiYknaIWkqTiA9JCL2am+YpGckbRwRCyVtDDxbpdoCYGSufwgwBdgVaJH0BNn63FDSlIgYSZ3VsM1tJgCzI+Lcbgi3VhYAm+X6h6SyanXmp6TYH1hUcNxG1JU2I2kIcD3whYh4rPbhdllX2rsLcKiks4ABwNuSXo+I82ofdjeo90kYfwLghyx/QvmsKnUGkR0nHZg+jwODKuoMo3lOonepzWTne34H9Kl3WzppZ1+yk/9b8O4J1u0r6nyN5U+wXpO6t2f5k+hzaY6T6F1p84BU/5B6t6Mn2ltRZxxNdhK97gH4E5Ad+50MzAZuy/1ItgC/ytX7ItmJ1DnAUVWm00wJpHSbybbwAngEmJE+R9e7TR209ePAP8iu1Dk5lY0HDkrda5BdgTMHmApsmRv35DTeLBr0SrPubDPwXeDV3HqdAWxY7/bUch3nptF0CcSPMjEzs1J8FZaZmZXiBGJmZqU4gZiZWSlOIGZmVooTiJmZleIEYislScskzZD0kKRrJa1Z75jaSLpE0qFVysdLes/Nl5JGSvpjF+b33xX9/5Pr/mF6ovEPJR0r6Qtl52O9jy/jtZWSpCURsXbq/g0wPSJ+nBveN7JnEtUjtkuAP0bExIL1R5LdH3BAyfm9syyqDHuJ7B6cZSWmW7dlaI3BeyDWG9wFbJW25O+SNAl4WNIakn6d3j1xv6SPAUg6UtINkqak95Cc0jYhSSekvZqHJB2fytZK7yd5IJV/NpV/T9K0VDah7emr7cnvmaT3Szwq6T6yR/W31VlL0sWSpqaYR+Vivk7STSnms1L5GWRPtJ2REimSlqS/k4C1gemSPitpXNv7KCQNT9OanpbZdrkYfyHpXrJH8lsv5mdh2UotPXdof+CmVLQT8L6IeFzSt8ie5bhD+oG8RdI2qd7OZO+jeA2YJulGsrvfjyJ7fpGAeyXdSfYeiKci4hNpnv3TNM6LiPGp7HLgAOAPBWJeA7gQ2IPszuWrc4NPBm6PiC+mJxFPTQ+tBPgg2fs03gBmSfpZRIyVdFxEfLByPhFxUNo7+WCa77jc4AnAsRExW9IuwPkpHsieBLBbmb0WW7l4D8RWVv0kzQBagX8CF6XyqZG9WwNgd+AKgIh4FHgSaEsgt0bEooj4F3Bdqrs7cH1EvBoRS1L5R4AHgb0lnSnpIxHxUprGx5S9fe5Bsh/f7QvGvh3weETMjuwY8xW5YfsAY1PbppA9ImNoGjY5Il6KiNeBh4HNC85vOcoeMb4bcG2azy+BjXNVrnXyMPAeiK28/lW51Z2OIL1acPzKk4PtniyMiFSrdQEAAAFdSURBVH9I2onseUinSZpMdnjnfKAlIualrfs1Cs67IwI+FRGzlivM9hLy781YRvnvdx/gxWp7LUnRZWgrOe+BWG92F3A4QDp0NZTsoYWQ7VEMktSP7G2Jd6f6Byt7J/tawCeBuyRtArwWEVeQPWV4J95NFs+nLfr3XHXVgUeBYZKGp/7RuWE3A19vO58iaccC03tL0qpFZx4RLwOPS/p0mockfaDo+NZ7eA/EerPzgQvSIaalwJER8Ub6bZ5K9rj4IcAVEdEK71xBNTWN/6uIuF/SvsAPJb0NvAV8JSJelHQh8BDZGxenFQ0qIl6XdAxwo6TXyBLXOmnwqcC5wN8l9SF7xH1nV2dNSPXvi4jDC4ZxONmy+S6wKtlrWh8o2gbrHXwZr1kFSUeSHXo6rt6xmDUyH8IyM7NSvAdiZmaleA/EzMxKcQIxM7NSnEDMzKwUJxAzMyvFCcTMzEr5X/lYVVBlNA9pAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.bar( proposals, conviction_required)\n",
"plt.title('Bar chart of Proposals Conviction Required')\n",
"plt.xlabel('Proposal identifier')\n",
"plt.ylabel('Amount of Conviction')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Conviction is a concept that arises in the edges between participants and proposals in the initial conditions there are no votes yet so we can look at that later however, the voting choices are driven by underlying affinities which we can see now."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 55.73999999999998, 'Participant_id')"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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mWwwl9/5v/2QouctttOrAMxfcds/AMwGWf+lw/v9Z8N//OZRc7l/sefSWyNxXfKmbT9ezxNxu5jwYilldPJAkSZIkaWnVVc+DYbB4IEmSJElSByweSJIkSZKkvrqYMHFYLB5IkiRJktSBuaNTO7B4IEmSJElSF0ap58EIzf0oSZIkSZK6YM8DSZIkSZI64ISJkiRJkiSpr1EatmDxQJIkSZKkDjhhoiRJkiRJ6sueB5IkSZIkqS/nPJAkSZIkSX1ZPJiCJMsBrwFeCGzUPn0d8H3g+Kq6v6tsSZIkSZKGbe6cYbdg5nS5K/8GbAccDuzT3j4APB748mQvSnJwknlJ5h339bM7bJ4kSZIkSd2Zm0z7Nlt1OWxhh6raetxz1wJnJfnNZC+qquOA4wDqiiOrw/ZJkiRJktSZUZowscueB7cleXGSP2ckmZPkpcAfOsyVJEmSJGno7HkwNS8DjgSOTXJ7+9xawGntMkmSJEmSRtYozXnQWfGgqq5McgxwNHAF8BjgycBFVfW7rnIlSZIkSZoNZnNPgunq8moLhwF7txmnAjsBpwOHJtm+qo7oKluSJEmSpGEbpTkPuhy2sB/N1RZWBG4ENq6qO5McBZwNWDyQJEmSJI0sex5MzQNVtQCYn+SKqroToKruTrKww1xJkiRJkobOOQ+m5r4kq1TVfGCHsSeTrAlYPJAkSZIkjTR7HkzNrlV1L0BV9RYLlgcO6DBXkiRJkiTNoC6vtnDvJM/fAtzSVa4kSZIkSbPB3NHpeNBpzwNJkiRJkpZZcxy2IEmSJEmS+rHngSRJkiRJ6muOxQNJkiRJktSPPQ8GZNdzzx945ll7P3fgmQDMv30osW/4zcVDyb326ycNPPOqE4bzu/3ar24eSu4/r776UHKv+9NlA888/93PG3gmwFNO+uZQcs/+u2cNJXef735r4JkXvmW/gWcCvOaUM4aS+5vbfzyU3Dcf/auh5K7wrX8aeObqnzp24JkAO6wzdyi5jz7lK0PJffqnnjLwzFcce+7AMwFWfdVQYrnw2OH8f7vZUzcYeObP37/zwDMBtqwbhpJ7wUE/G0ruJpsM51Pu9q8YSuzAzBmhrgezunggSZIkSdLSyp4HkiRJkiSprxHqeGDxQJIkSZKkLtjzQJIkSZIk9TUno1M9sHggSZIkSVIH7HkgSZIkSZL6cs4DSZIkSZLU11yHLUiSJEmSpH7seSBJkiRJkvpyzgNJkiRJktTXnDnDbsHMGaFdkSRJkiRJXbDngSRJkiRJHXDCREmSJEmS1NcoTZg4lGELSY4bRq4kSZIkSYMyN9O/zVadFQ+SrDPJ7WHAPn1ed3CSeUnm3fijy7tqniRJkiRJnZqT6d9mqy57HvwemAec23Ob197Wm+xFVXVcVe1YVTtusOeWHTZPkiRJkqTuzE2mfZuKJHsluTTJ5UkO7bPeXyepJDsu6b50OefBb4E9qurq8QuSXNNhriRJkiRJQ9dFT4Ikc4FjgWcC1wLnJDmpqi4at97qwFuBs2cit8ueBx8H1p5k2Uc7zJUkSZIkaeg6mvNgJ+DyqvptVd0HfB3Yd4L1PgQcCdwzE/vSWfGgqo4FVkzyRIAk2yQ5JMk+VfWprnIlSZIkSZoN5iTTvk3BI4De3vzXts/9WZInAJtU1X/O1L50NmwhyWHA3sBySU4FdgZOAw5Nsn1VHdFVtiRJkiRJw7Y4V09IcjBwcM9Tx1XVlK9YmGQOcAxw4PTTJ9flnAf7AdsBKwI3AhtX1Z1JjqIZc2HxQJIkSZI0sqbYk+Ah2kJBv2LBdcAmPY83bp8bszrwOOD0NPkbACcleX5VzZt2g1pdFg8eqKoFwPwkV1TVnQBVdXeShR3mSpIkSZI0dItTPJiCc4CtkmxGUzR4GfCKsYVVdQew7tjjJKcD71iSwgF0Wzy4L8kqVTUf2GHsySRrAhYPJEmSJEkjrYviQVU9kORNwA+BucAXqurCJB8E5lXVSTMeSrfFg12r6l6AquotFiwPHNBhriRJkiRJQzcn3VyjoKpOBk4e99z7J1l395nI7Kx4MFY4mOD5W4BbusqVJEmSJGk26GjYwlB02fNAkiRJkqRllsUDSZIkSZLU1ygVD7oZgCFJkiRJkkaGPQ8kSZIkSerAnBH6vn5WFw/uvn/BwDN/84wjBp4JsOkLthxK7nnves5Qcje+c/D/E214yi8Gnglw1b1zh5I7d79dhpK7xTkXDjzzXzd4YOCZAGdmhaHk3rvmXw0l97S7Lxh45nIrbzbwTICfX/6doeSee/VwzhcffvczhpK78IzvDzzzyCc9bOCZAIesMZw/uTZZfa2h5O566V0Dz7zp6FcOPBNgjbcfN5TcOXuuM5Tcf3ndYwee+evf3jbwTIDn7/e9oeS+4pCNh5L7469cN5TcUTdKwxZmdfFAkiRJkqSllcUDSZIkSZLU15w4bEGSJEmSJPVhzwNJkiRJktSXxQNJkiRJktSXxQNJkiRJktSXcx5IkiRJkqS+5mDPA0mSJEmS1IfDFiRJkiRJUl8OW5AkSZIkSX3Z80CSJEmSJPVl8UCSJEmSJPXlsAVJkiRJktTXKPU86KwMkmRuktcl+VCSp45b9r6uciVJkiRJmg3mkGnfZqsu+1B8DtgNuBX4ZJJjepa9aLIXJTk4ybwk837/kys6bJ4kSZIkSZqKLosHO1XVK6rq48DOwGpJvpNkRZi8nFJVx1XVjlW148OfsUWHzZMkSZIkqTtzkmnfZqsuiwcrjN2pqgeq6mDgfOAnwGod5kqSJEmSNHRzMmfat9mqy5bNS7JX7xNV9QHgBOBRHeZKkiRJkjR0o9TzYJFXW0jyKaAmW15Vb5nk+Vcm2SnJE6vqnCTbAHsBl1TV8ovdYkmSJEmSlgKZxT0Jpmsql2qc1/58KrAN8I328YuBiyZ7UZLDgL2B5ZKcSjPvwWnAoUm2r6ojFrvVkiRJkiTNcnM67ew/WIssHlTVFwGSvAHYpaoeaB9/Fjizz0v3A7YDVgRuBDauqjuTHAWcDVg8kCRJkiSNrGWt58GYtYE1gNvax6u1z03mgapaAMxPckVV3QlQVXcnWbhYrZUkSZIkaSkxmydAnK7pFA8+AvwyyWk0l1rcFTi8z/r3JVmlquYDO4w9mWRNwOKBJEmSJGmkZVkatjCmqk5I8l80cxcAvLuqbuzzkl2r6t72tb3FguWBA6bdUkmSJEmSliLLVM+DJI+pqkuSPKF96pr250ZJNqqqX0z0urHCwQTP3wLcslitlSRJkiRpKbGs9Tw4BDgYOHqCZQU8Y0ZbJEmSJEnSCFimeh5U1cHtz6f3Wy/JM6vq1JlqmCRJkiRJS7NRutrCTO7JkTO4LUmSJEmSlmpzFuO/2SpVNTMbSn5ZVdvPyMZaP9ng0TPTuGn473/fbdCRAHzk/lWGksttdwwl9oYTLhh45h1f3G/gmQCXP/ILQ8nd4Ir9h5K74arrDTxzpdd+aeCZALvv/Yih5J571T1Dyb3yzbsOJfczv/rNwDOvvGPCaXs6984nPmooubus+ldDyb11hcFffOmxn/vqwDMBXv2EDYaS+5SNVh1K7nYP33zgmZfcds2iV+rAtutuNZTch3/q5KHkPmmTFQeeecSbzht4JsC21759KLkb/eTnQ8m96mPnDCV3s9POz1CCB+Tmu7807c+066386ll5TKZzqcZFGfgHfUnSaBtG4UCSJGmmLFNzHkiSJEmSpOkLc4fdhBkzk8WDK2dwW5IkSZIkLdWWqZ4HSV7Ub3lVfaf92Xc9SZIkSZK0dJpKz4Pn9VlWwHdmqC2SJEmSJI2MzOKrJ0zXIosHVXXQIBoiSZIkSdIoWaaGLfRK8hzgr4CVxp6rqg/OdKMkSZIkSVraZVksHiT5LLAK8HTg88B+wHAuQipJkiRJ0iw3Z4SGLUxnT55SVa8G/lBVHwCeDGzdTbMkSZIkSVq6JXOmfZutpjNs4e725/wkGwG3AhvOfJMkSZIkSVr6LatzHvwgyVrAPwO/oLnSwuc7aZUkSZIkSUu5UbrawpT3pKo+VFW3V9W/A5sCj6mqf+iuaZIkSZIkLb3mZM60b1ORZK8klya5PMmhEyxfMck32uVnJ3nUEu/LVFdM8uIkq7cP3wmckGT7JW2AJEmSJEmjKMyZ9m2R20zmAscCewPbAC9Pss241V5DM1/hlsDHgCOXdF+m04fiH6rqriS7AHsCxwOfXdIGSJIkSZI0ijrqebATcHlV/baq7gO+Duw7bp19gS+2978N7JEkS7Qv01h3QfvzOcBxVfWfwAqTrZxklSTvSvLOJCslOTDJSUk+mmS1JWm0JEmSJEmz3eJcbSHJwUnm9dwOHrfZRwDX9Dy+tn1uwnWq6gHgDuBhS7Iv0ykeXJfkc8BLgZOTrLiI158IrA9sBvwnsCPNZIsBPjPZi3oP1A/m3z6N5kmSJEmSNHukpn+rquOqasee23HD3g+Y3tUWXgLsBRxVVbcn2ZBm7oPJbF1VL2m7RtwA7FlVleR/gPMne1F7YI4D+MkGj65ptE+SJEmSpNmjFk7/NYseXHAdsEnP443b5yZa59okywFrArdOvzEPms7VFuYDVwDPTvImYL2qOmUKryvg5Pbn2GOLApIkSZKk0VYLp39btHOArZJslmQF4GXASePWOQk4oL2/H/CTsc/ki2s6V1t4K/AVYL329uUkb+7zknljcxtU1d/0bGcL4K7Fa64kSZIkSUuJDooH7RwGbwJ+CFwMfLOqLkzywSTPb1c7HnhYksuBQ4C/uJzjdE1n2MJrgJ2r6k8ASY4EfgZ8aqKVq+q1SXZKUlV1TnvpiL2AS4GnLWG7JUmSJEma3RZn2MJUNlt1MnDyuOfe33P/HuDFM5k5neJBePCKC7T3Jx2NkeQwmutOLpfkVGBn4DTg3cB2wBHTbq0kSZIkSRq46RQPTgDOTvJdmqLBvjRdISazH02RYEXgRmDjqrozyVHA2Vg8kCRJkiSNsoXd9DwYhikXD6rqmCSnA7vQTHh4UFX9ss9LHqiqBcD8JFdU1Z3tdu5OMjpHUJIkSZKkiXQ0bGEYptPzYExoigeLuoDEfUlWaa/SsMOfX5ysCYzOEZQkSZIkaSIjVDyYztUW3g98EVgbWBc4Icn7+rxk17ZwQNVDjtjyPHjJCEmSJEmSRlM3l2ociun0PNgfeHw7ayNJPgKcB3x4opWr6t5Jnr8FuGWa7ZQkSZIkaemyLM55AFwPrATc0z5eEbhuxlskSZIkSdIomMU9CaZrOsWDO4AL28suFvBM4OdJPglQVW/poH2SJEmSJC2dltHiwXfb25jTZ7YpkiRJkiSNkBEqHqSqpr5ysgKwdfvw0qq6v5NWtVZ93zOm3rgZMv/W+YOOBGD1DVYfSu5Ka600lNz/fsXOA89c9dlfGXgmwOZv3G4ouatdMpxRRZdf8MeBZz5su/UHngmw8hv2GEruretvMJTc15xyysAzr75zwulzOnflNXcMJfe2g14wlNz/eszHhpJ78U+ePvDMQzbcduCZALetvfZQcte5+KKh5PKITQYe+fHrfzHwTICXf/i8oeTef+KBQ8nd+K7BfxCav+4jBp4J8Kut3z2U3F2eu85Qcu9/zuOHkjtnr88s6ip+S7W6/cvT/kybtV45K4/JlHseJNmd5moLV9JcpnGTJAdU1U+7aZokSZIkSUuxZXTCxKOBZ1XVpQBJtga+BuzQRcMkSZIkSVqqjdCwhekUD5YfKxwAVNVvkizfQZskSZIkSVr6LaPFg3OTfB74cvt4f2DezDdJkiRJkqQRsIwWD14P/C0wdknGM4F/mfEWSZIkSZI0Cpa14kGSucD5VfUY4JhumyRJkiRJ0ggYoQkT50xlpapaAFya5JEdt0eSJEmSpNFQC6d/m6WmM2xhbeDCJD8H/jT2ZFU9f8ZbJUmSJEmSZo3pFA/+obNWSJIkSZI0amZxT4LpWmTxIMlKNJMlbgn8Gji+qh7oumGSJEmSJC3VlqXiAfBF4H6aqyvsDWwDvLXLRkmSJEmStNQboQkTp1I82Kaq/h9AkuOBn3fbJEmSJEmSRsAy1vPg/rE7VfVAkg6bI0mSJEnSiFjGigePT3Jnez/Ayu3jAFVVa0w1LMlvqmrrxWinJEmSJLSyVsIAABdvSURBVElLl2Vp2EJVzV2cDSe5C6ixh+3PVcaen6zokORg4GCAFfZ+NMs9YaPFiZckSZIkabgW1qLXWUrM6XDbJwDfA7aqqtWranXg6vb+pL0Vquq4qtqxqna0cCBJkiRJWmotXDj92yw1lWELi6Wq3pJkB+BrSb4HfJoHeyJIkiRJkjTaZnExYLq67HlAVZ0L7Nk+PANYqcs8SZIkSZJmjYU1/dss1VnPA4AkO9HMb/DJJL8Enp5kn6o6uctcSZIkSZKGboR6HnRWPEhyGLA3sFySU4GdgNOBQ5NsX1VHdJUtSZIkSdLQWTyYkv2A7YAVgRuBjavqziRHAWcDFg8kSZIkSaNrFg9DmK4uiwcPVNUCYH6SK6rqToCqujvJ6JRfJEmSJEmaiD0PpuS+JKtU1Xxgh7Enk6wJjM4RlCRJkiRpIvY8mJJdq+pegKrqLRYsDxzQYa4kSZIkScNnz4NFGyscTPD8LcAtXeVKkiRJkqSZ1emlGiVJkiRJWmbZ80CSJEmSJPVTNf05D9JBO2aCxQNJkiRJkrpgzwNJkiRJktSXxYPBuPzCPw08c/137z7wTIBT9vneUHKf/dt/GEru2fdcPvDMm68czv+4Wz9x66Hk3vOcpw0ll+VWGHjkw77w7wPPBLj22B8NJXft7dYbSu7XL7514JkXvvlJA88E+OZl1wwl98fbfmwoubv9/qPDyX3XpweeecgLFww8E2DejYP/mwbgyKdtNZTcPT/+tYFnnnfqTQPPBDjrG88eSu5LD//qUHL/+ys3Dzzz6Vf808AzAXY+dveh5D7r3uGcL+bsvs9Qckeel2qUJEmSJEl92fNAkiRJkiT1ZfFAkiRJkiT15bAFSZIkSZLUlz0PJEmSJElSXyNUPJgz7AZIkiRJkjSSFtb0b0sgyTpJTk1yWftz7T7rrpHk2iRTuvyRxQNJkiRJkrqwcOH0b0vmUODHVbUV8OP28WQ+BPx0qhu2eCBJkiRJUhcGXzzYF/hie/+LwAsmWinJDsD6wClT3bDFA0mSJEmSujDgYQvA+lV1Q3v/RpoCwUMkmQMcDbxjOht2wkRJkiRJkrqwGD0JkhwMHNzz1HFVdVzP8h8BG0zw0vf2PqiqSjJRNeKNwMlVdW2SKbfL4oEkSZIkSR2oBdPvSdAWCo7rs3zPyZYluSnJhlV1Q5INgZsnWO3JwNOSvBFYDVghyR+rqt/8CBYPJEmSJEkaEScBBwAfaX9+f/wKVbX/2P0kBwI7LqpwAM55IEmSJElSNwY/58FHgGcmuQzYs31Mkh2TfH5JNtxZz4Mk21bVr9r7ywPvBnYCLgA+XFXzu8qWJEmSJGnoFmPYwpKoqluBPSZ4fh7w2gmePxE4cSrb7rLnQW8DPgJsSTOj48rAZyd7UZKDk8xLMu/LV040PEOSJEmSpNmvFta0b7NVl3Me9E7buAfwxKq6P8lPgfMne1Hv5BA3vHDn2XvkJEmSJEnqZ8A9D7rUZfFgzSQvoikirFhV90Pfy0VIkiRJkjQ6Fkz/Uo2zVZfFgzOA57X3z0qyflXdlGQD4JYOcyVJkiRJGrrZPAxhujorHlTVQUl2BhZW1TlJtkmyP3BJVf3FBA6SJEmSJI0Uhy0sWpLDgL2B5ZKcSnOlhdOBQ5NsX1VHdJUtSZIkSdLQ2fNgSvYDtgNWBG4ENq6qO5McBZwNWDyQJEmSJI2ssufBlDxQVQuA+UmuqKo7Aarq7iSjM2uEJEmSJEkTWTg6H327LB7cl2SVqpoP7DD2ZJI1gdE5gpIkSZIkTcSeB1Oya1XdC1BVvcWC5YEDOsyVJEmSJGnovNrCFIwVDiZ4/ha8VKMkSZIkadTZ80CSJEmSJPVl8UCSJEmSJPXjsAVJkiRJktTfgtG5VoDFA0mSJEmSOmDPgwHZdLMVBp75P5utOvBMgKe9cpOh5GbuikPJXXufbww8c5XLDhx4JsA9x5w5lNxnPX6toeSeetmfBp551bUTzs/aub990WZDyT31d7cNJfeYj1w+8MyXvOOVA88E2GGztYeSy2kbDyV2o389YSi5N3zo9QPPfNFj3zvwTICP/e4TQ8m9/t5rhpL7p7e9fOCZdz/wnYFnAjzlu78ZSu7du249lNyvH33TwDN/8acLB54J8P77B/83DcCXv/O7oeSe9Kp3DCX3+X983lByNX2zunggSZIkSdJSywkTJUmSJElSXw5bkCRJkiRJ/ZQ9DyRJkiRJUl/2PJAkSZIkSX15qUZJkiRJktSPl2qUJEmSJEn9OeeBJEmSJEnqx54HkiRJkiSpL6+2IEmSJEmS+rLngSRJkiRJ6muhPQ8kSZIkSVI/9jyQJEmSJEl91cKFw27CjJnT1YaTvCnJuu39LZP8NMntSc5O8v+6ypUkSZIkaTaoBTXt22zVWfEAeENV3dLe/wTwsapaC3g38NnJXpTk4CTzksxb+KsbO2yeJEmSJEndqYU17dts1WXxoHdIxHpV9V2AqjodWH2yF1XVcVW1Y1XtOGfbDTpsniRJkiRJ3bHnwdR8O8mJSTYHvpvkbUk2TXIQcHWHuZIkSZIkaQZ1NmFiVb03yYHA14AtgBWBg4HvAft3lStJkiRJ0mwwm4chTFfXV1u4CHhTVZ2T5K+AvYCLq+qOjnMlSZIkSRqqhRYPFi3JYcDewHJJTgV2Ak4HDk2yfVUd0VW2JEmSJEnDNpvnMJiuLnse7AdsRzNc4UZg46q6M8lRwNmAxQNJkiRJ0shy2MLUPFBVC4D5Sa6oqjsBquruJAs7zJUkSZIkaegsHkzNfUlWqar5wA5jTyZZE7B4IEmSJEkaaQ5bmJpdq+pegKrqLRYsDxzQYa4kSZIkSUNXC0fne/MuL9V47yTP3wLc0lWuJEmSJEmzgT0PJEmSJElSX855IEmSJEmS+lpo8UCSJEmSJPXjsAVJkiRJktTXKA1bSNXo7EyvJAdX1XHmjlamuaObae7oZpo7upnmjm6muaObae5o5y5L+7q0uPzJj5v2B+4tf3ZBumjLkpoz7AZ06GBzRzLT3NHNNHd0M80d3UxzRzfT3NHNNHe0c5elfV0q1MKa9m1JJFknyalJLmt/rj3Jeh9NcmGSi5N8MskiCxajXDyQJEmSJGloBl08AA4FflxVWwE/bh8/RJKnAE8FtgUeBzwR2G1RG7Z4IEmSJElSB2pBTfu2hPYFvtje/yLwgomaBawErACsCCwP3LSoDY9y8WBYY26WpdxlaV+XtdxlaV+XtdxlaV+XtdxlaV+XtdxlaV+XtdxlaV/NHd3MYeaOpCQHJ5nXc5vOsJD1q+qG9v6NwPrjV6iqnwGnATe0tx9W1cWLbNeoTpgoSZIkSdIwXbzdY6b9gfux513Sd/6BJD8CNphg0XuBL1bVWj3r/qGqHjLvQZItgU8AL22fOhV4V1Wd2S/XSzVKkiRJktSBhQtnfptVtedky5LclGTDqrohyYbAzROs9kLgrKr6Y/ua/wKeDPQtHozcsIUkeyW5NMnlSf5icogOc7+Q5OYkFwwwc5MkpyW5qJ0p860Dyl0pyc+TnN/mfmAQuW323CS/TPKDAWZemeTXSc5LMm+AuWsl+XaSS9pZUJ88gMxHt/s5drszydu6zm2z/659P12Q5GtJVhpA5lvbvAu73M+Jzg9TnQm3g9wXt/u7MMmOM53ZJ/ef2/fyr5J8N8la/bYxg7kfajPPS3JKko26zuxZ9vYklWTdmcycLDfJ4Umu6/n/d59B5LbPv7n9/V6Y5KODyE3yjZ59vTLJeQPI3C7JWWP/HiTZaSYz++Q+PsnP2n+L/iPJGh3kTvg3RZfnqj6ZnZ6n+uR2ep7qk9v1earv34tdnKv67Gun56l++9rlearP/nZ9npost7NzVZ/Mzs9TS6uFC6d/W0InAQe09w8Avj/BOlcDuyVZLsnyNJMlLnLYAlU1MjdgLnAFsDnN5A/nA9sMKHtX4AnABQPc3w2BJ7T3Vwd+M4j9BQKs1t5fHjgbeNKA9vkQ4KvADwZ4nK8E1h1UXk/uF4HXtvdXANYacP5cmnFSmw4g6xHA74CV28ffBA7sOPNxwAXAKjS9sH4EbNlR1l+cH4CPAoe29w8FjhxQ7mOBRwOnAzsOcH+fBSzX3j9ygPu7Rs/9twCf7TqzfX4T4IfAVV2cPybZ18OBd3TxO11E7tPb/39WbB+vN4jcccuPBt4/gH09Bdi7vb8PcPqAjvE5wG7t/b8BPtRB7oR/U3R5ruqT2el5qk9up+epPrldn6cm/Xuxq3NVn33t9DzVJ7fT81S/Y9yzThfnqcn2t7NzVZ/Mzs9TS+vtvMc+uqZ7W8Lf0cNorrJwWfu+X6d9fkfg8+39ucDnaAoGFwHHTGXbo9bzYCfg8qr6bVXdB3ydZrbJzlXVT4HbBpHVk3lDVf2ivX8XzS//EQPIrWq7uNAUD5anmbGzU0k2Bp4DfL7rrGFLsibNH5DHA1TVfVV1+4CbsQdwRVVdNaC85YCVkyxH84H++o7zHgucXVXzq+oB4AzgRV0ETXJ+mMpMuDOeW1UXV9WlM501hdxT2uMMcBaw8YBy7+x5uCozfK7qc+7/GPCumc6bQm6nJsl9A/CRqrq3XWei7pFd5AKQJMBLgK8NILOAsW/T1qSD89QkuVsDP23vnwr8dQe5k/1N0dm5arLMrs9TfXI7PU/1ye36PNXv78VOzlVD/Bt1stxOz1OL2t8Oz1OT5XZ2ruqT2fl5amm1sKZ/WxJVdWtV7VFVW1XVnlV1W/v8vKp6bXt/QVW9rqoeW1XbVNUhU9n2qBUPHgFc0/P4WgZwopoNkjwK2J6mF8Ag8ua2Xa9uBk6tqkHkfpzmH7gORg71VcApSc7N9GY6XRKbAb8HTkgzTOPzSVYdUPaYlzHD/8hNpqquA46i6UJ1A3BHVZ3ScewFwNOSPCzJKjSV+U06zuy1yJlwR9jfAP81qLAkRyS5BtgfeP8A8vYFrquq87vOmsCb2u7PX5jJ7uWLsDXN/0tnJzkjyRMHlDvmacBNVXXZALLeBvxz+346CnjPADIBLuTBL0NeTMfnqnF/UwzkXDXov2OmkNvpeWp87qDOU725gzpXTXCMB3KeGpc7sPPUJO+pzs9T43IHcq4alznQ89TSZAjDFjozasWDZVKS1YB/B942rnrdmbZatR1NVX6nJI/rMi/Jc4Gbq+rcLnMmsUtVPQHYG/jbJLsOIHM5mm6rn6mq7YE/0XQXHYgkKwDPB741oLy1af7B2QzYCFg1ySu7zKzmcjRH0nTt+2/gPGBBl5l92lIMoPfObJDkvcADwFcGlVlV762qTdrMN3WZ1Rai/p4BFCkm8BlgC2A7miLc0QPKXQ5YB3gS8E7gm+23bIPycgZU6KT59vLv2vfT39H2DhuAvwHemORcmm7C93UV1O9viq7OVcP4O6ZfbtfnqYlyB3Ge6s2l2b/Oz1UT7OtAzlMT5A7kPNXnvdzpeWqC3M7PVRNkDuw8tbSxeDB7XcdDq1wbt8+NrHaCi38HvlJV3xl0ftuV/jRgr46jngo8P8mVNMNRnpHkyx1nAn/+Vnysi9t3aYbHdO1a4NqeHh3fpikmDMrewC+q6qYB5e0J/K6qfl9V9wPfAZ7SdWhVHV9VO1TVrsAfaMbtDcpNaWbAJZPPhDtSkhwIPBfYv/0QMmhfoftulFvQFMHOb89XGwO/SDLR5ZRmVFXd1BZ2FwL/ymDOVdCcr77TDmn7OU3vsBmfJHIi7TCnFwHfGEQezcRTY//WfosBHeOquqSqnlVVO9B8ALmii5xJ/qbo9Fw1rL9jJsvt+jw1hf3t5Dw1QW7n56qJ9nUQ56lJjnHn56k+76lOz1OT5HZ6rprkdzuQ89TSyOLB7HUOsFWSzdpvTl9GM9vkSGorpscDF1fVMQPMfXjaGYiTrAw8E7iky8yqek9VbVxVj6L5vf6kqjr9ZhogyapJVh+7TzOZUudX1KiqG4Frkjy6fWoPmslMBmWQ3+RBM1zhSUlWad/XezCVGV+XUJL12p+PpPmH/atdZ/aYyky4IyPJXjTDjp5fVfMHmLtVz8N96f5c9euqWq+qHtWer66lmVjqxi5z4c8f7Ma8kAGcq1rfo5mMjCRb00zwesuAsvcELqmqaweUdz3NjNQAz6CZjKpzPeeqOcD7gM92kDHZ3xSdnauG+HfMhLldn6f65HZ6npoot+tzVZ997fQ81ec91el5ahHv5c7OU31yOztX9fnddn6eWlqNUvEgw/nypztpLvnycZoZJL9QVUcMKPdrwO40VcybgMOqqtPujEl2obkW5695cB6Av6+qkzvO3ZZm0qS5NAWob1bVB7vMHJe/O81Mvc8dQNbmNL0NoOny9tUBvqe2o5kccgXgt8BBVfWHAeSuSvNhfvOquqPrvJ7cDwAvpelK+UuaK03c23HmmTQz0t4PHFJVP+4o5y/ODzR/yHwTeCTNLNcvqXZCm45zbwM+BTwcuB04r6qePYDc9wArAre2q51VVa8fQO4+NLO2L6Q5zq8f603UVWbvub/9Rm/HqprRD9OT7OvuNF2Bi+YqMa/rGaveZe6/AV9os++jOT//pOvcqjo+yYk076UuPkxPtK+XAp+g+ffgHuCNMz2cbpLc1YC/bVf5DvCemf5WfLK/KWjGMndyruqTuSIdnqf65H6SDs9TfXJfQ7fnqUX+vTjT56o++/pyOjxP9cn9ER2ep/od447PU5Pt7510dK7qk7kVHZ+nllb/u+ljpn0cnnrVJYMc/jdlI1c8kCRJkiRpNjhzk+kXD552zewsHiw37AZIkiRJkjSKZvMwhOmyeCBJkiRJUgcsHkiSJEmSpL5GaZoAiweSJEmSJHXAngeSJEmSJKmvUSoezBl2AyRJkiRJ0uxm8UCSpFaSBUnOS3JBkm8lWWUar90uyT49j5+f5NBFvOb/lqS9k2xz9yRPWcQ6r0/y6gmef1SSC2a6TZIkLasWLpz+bbayeCBJ0oPurqrtqupxwH3A66fyoiTLAdsBfy4eVNVJVfWRfq+rqr4f8hfT7kDf7VbVZ6vqSx1kS5KkHqNUPHDOA0mSJnYmsG2S5wHvA1YAbgX2r6qbkhwObAFsDlwNPBVYOckuwD8BKwM7VtWbkqwPfLZdF+ANVfV/Sf5YVasl2R34IHAXsCVwGvDGqlqY5DPAE9vtfbuqDgNIciXwReB5wPLAi4F7aAoeC5K8EnhzVZ05fsfatv+xqo5KsgPwhXbRKTNw3CRJUms2FwOmy54HkiSN0/Yk2Bv4NfA/wJOqanvg68C7elbdBtizql4OvB/4Rttz4RvjNvlJ4IyqejzwBODCCWJ3At7cbnML4EXt8++tqh2BbYHdkmzb85pbquoJwGeAd1TVlTRFio+17fiLwsEETqApMjx+CutKkqRpGKWeBxYPJEl60MpJzgPm0fQmOB7YGPhhkl8D7wT+qmf9k6rq7ils9xk0H/CpqgVVdccE6/y8qn5bVQuArwG7tM+/JMkvgF+22dv0vOY77c9zgUdNoR0PkWQtYK2q+mn71L9NdxuSJGlyo1Q8cNiCJEkPuruqtut9IsmngGOq6qR2eMHhPYv/NIPZNf5xks2AdwBPrKo/JDkRWKlnnXvbnwvw33RJkmadheP/dV+K2fNAkqT+1gSua+8f0Ge9u4DVJ1n2Y+ANAEnmJllzgnV2SrJZkjnAS2mGS6xBU6C4o503Ye8ptLdfOx6iqm4Hbm/naQDYfyqvkyRJUzNKPQ8sHkiS1N/hwLeSnAvc0me904Bt2ks9vnTcsrcCT2+HPpzLQ4cejDkH+DRwMfA74LtVdT7NcIVLgK8C/zuF9v4H8MK2HU+bwvoHAce2wzUyhfUlSdIUjVLxIFUj1I9CkqSlUDsc4h1V9dxht0WSJM2cr8999LQ/cL9swaWzspjv+EhJkiRJkjowm3sSTJc9DyRJGlFJ3gu8eNzT36qqI4bRHkmStPSyeCBJkiRJkvpywkRJkiRJktSXxQNJkiRJktSXxQNJkiRJktSXxQNJkiRJktSXxQNJkiRJktTX/w9PaaphGVLkLgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 1440x360 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"m = len(proposals)\n",
"n = len(participants)\n",
"\n",
"affinities = np.empty((n,m))\n",
"for i_ind in range(n):\n",
" for j_ind in range(m):\n",
" i = participants[i_ind]\n",
" j = proposals[j_ind]\n",
" affinities[i_ind][j_ind] = state_variables['network'].edges[(i,j)]['affinity']\n",
"\n",
"dims = (20, 5)\n",
"fig, ax = plt.subplots(figsize=dims)\n",
"\n",
"sns.heatmap(affinities.T,\n",
" xticklabels=participants,\n",
" yticklabels=proposals,\n",
" square=True,\n",
" cbar=True,\n",
" cmap = plt.cm.RdYlGn,\n",
" ax=ax)\n",
"\n",
"plt.title('Affinities between participants and proposals')\n",
"plt.ylabel('Proposal_id')\n",
"plt.xlabel('Participant_id')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Run simulation\n",
"\n",
"Now we will create the final system configuration, append the genesis states we created, and run our simulation."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
" ___________ ____\n",
" ________ __ ___/ / ____/ | / __ \\\n",
" / ___/ __` / __ / / / /| | / / / /\n",
"/ /__/ /_/ / /_/ / /___/ ___ |/ /_/ /\n",
"\\___/\\__,_/\\__,_/\\____/_/ |_/_____/\n",
"by cadCAD\n",
"\n",
"Execution Mode: local_proc\n",
"Configuration Count: 1\n",
"Dimensions of the first simulation: (Timesteps, Params, Runs, Vars) = (100, 11, 1, 9)\n",
"Execution Method: local_simulations\n",
"SimIDs : [0]\n",
"SubsetIDs: [0]\n",
"Ns : [0]\n",
"ExpIDs : [0]\n",
"Execution Mode: single_threaded\n",
"Total execution time: 159.91s\n"
]
}
],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"from model import run\n",
"pd.options.display.float_format = '{:.2f}'.format\n",
"\n",
"%matplotlib inline\n",
"\n",
"rdf = run.run()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"After the simulation has run successfully, we perform some postprocessing to extract node and edge values from the network object and add as columns to the pandas dataframe. For the rdf, we take only the values at the last substep of each timestep in the simulation."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"df= run.postprocessing(rdf,0)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>network</th>\n",
" <th>funds</th>\n",
" <th>sentiment</th>\n",
" <th>effective_supply</th>\n",
" <th>total_supply</th>\n",
" <th>fractionOfSupplyForVoting</th>\n",
" <th>fractionOfSupplyInPool</th>\n",
" <th>fractionOfProposalStages</th>\n",
" <th>fractionOfFundStages</th>\n",
" <th>simulation</th>\n",
" <th>...</th>\n",
" <th>age_all</th>\n",
" <th>conviction_all</th>\n",
" <th>triggers_all</th>\n",
" <th>conviction_share_of_trigger_all</th>\n",
" <th>percentageOfActiveProposals</th>\n",
" <th>percentageOfCompletedProposals</th>\n",
" <th>percentageOfKilledProposals</th>\n",
" <th>percentageOfActiveFundsRequested</th>\n",
" <th>percentageOfCompletedFundsRequested</th>\n",
" <th>percentageOfKilledFundsRequested</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...</td>\n",
" <td>4889.60</td>\n",
" <td>0.60</td>\n",
" <td>14020.01</td>\n",
" <td>22414.61</td>\n",
" <td>0.63</td>\n",
" <td>4.58</td>\n",
" <td>{'percentageOfActive': 0.0, 'percentageOfCompl...</td>\n",
" <td>{'percentageOfActiveFundsRequested': 0.0, 'per...</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>[1, 1, 1, 1, 1, 1, 1]</td>\n",
" <td>[127.28769625755204, 251.62005074526414, 267.8...</td>\n",
" <td>[inf, inf, inf, inf, inf, inf, inf]</td>\n",
" <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.14</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.34</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...</td>\n",
" <td>4912.02</td>\n",
" <td>0.60</td>\n",
" <td>14020.01</td>\n",
" <td>22437.03</td>\n",
" <td>0.62</td>\n",
" <td>4.57</td>\n",
" <td>{'percentageOfActive': 0.0, 'percentageOfCompl...</td>\n",
" <td>{'percentageOfActiveFundsRequested': 0.0, 'per...</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>[2, 2, 2, 2, 2, 2, 2, 1]</td>\n",
" <td>[255.16782502065337, 1727.5799200074293, 696.3...</td>\n",
" <td>[inf, inf, inf, inf, inf, nan, inf, inf]</td>\n",
" <td>[0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0]</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.25</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.34</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...</td>\n",
" <td>4934.45</td>\n",
" <td>0.60</td>\n",
" <td>14020.01</td>\n",
" <td>22459.46</td>\n",
" <td>0.62</td>\n",
" <td>4.55</td>\n",
" <td>{'percentageOfActive': 0.0, 'percentageOfCompl...</td>\n",
" <td>{'percentageOfActiveFundsRequested': 0.0, 'per...</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>[3, 3, 3, 3, 3, 3, 3, 2, 1]</td>\n",
" <td>[515.2498845075063, 2899.0500445722328, 1036.3...</td>\n",
" <td>[inf, inf, inf, inf, inf, nan, inf, nan, inf]</td>\n",
" <td>[0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0]</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.22</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.34</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...</td>\n",
" <td>4956.91</td>\n",
" <td>0.60</td>\n",
" <td>14020.01</td>\n",
" <td>22481.92</td>\n",
" <td>0.62</td>\n",
" <td>4.54</td>\n",
" <td>{'percentageOfActive': 0.0, 'percentageOfCompl...</td>\n",
" <td>{'percentageOfActiveFundsRequested': 0.0, 'per...</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>[4, 4, 4, 4, 4, 4, 4, 3, 2]</td>\n",
" <td>[721.6771519212494, 3828.8464986139766, 1306.3...</td>\n",
" <td>[inf, inf, inf, inf, inf, nan, inf, nan, inf]</td>\n",
" <td>[0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0]</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.22</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.34</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...</td>\n",
" <td>4979.40</td>\n",
" <td>0.60</td>\n",
" <td>14020.01</td>\n",
" <td>22504.41</td>\n",
" <td>0.62</td>\n",
" <td>4.52</td>\n",
" <td>{'percentageOfActive': 0.0, 'percentageOfCompl...</td>\n",
" <td>{'percentageOfActiveFundsRequested': 0.0, 'per...</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>[5, 5, 5, 5, 5, 5, 5, 4, 3, 1, 1]</td>\n",
" <td>[1044.718620106594, 4750.256628320954, 1562.01...</td>\n",
" <td>[inf, inf, inf, inf, inf, nan, inf, nan, inf, ...</td>\n",
" <td>[0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, ...</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.18</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.34</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 43 columns</p>\n",
"</div>"
],
"text/plain": [
" network funds sentiment \\\n",
"6 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 4889.60 0.60 \n",
"12 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 4912.02 0.60 \n",
"18 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 4934.45 0.60 \n",
"24 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 4956.91 0.60 \n",
"30 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 4979.40 0.60 \n",
"\n",
" effective_supply total_supply fractionOfSupplyForVoting \\\n",
"6 14020.01 22414.61 0.63 \n",
"12 14020.01 22437.03 0.62 \n",
"18 14020.01 22459.46 0.62 \n",
"24 14020.01 22481.92 0.62 \n",
"30 14020.01 22504.41 0.62 \n",
"\n",
" fractionOfSupplyInPool fractionOfProposalStages \\\n",
"6 4.58 {'percentageOfActive': 0.0, 'percentageOfCompl... \n",
"12 4.57 {'percentageOfActive': 0.0, 'percentageOfCompl... \n",
"18 4.55 {'percentageOfActive': 0.0, 'percentageOfCompl... \n",
"24 4.54 {'percentageOfActive': 0.0, 'percentageOfCompl... \n",
"30 4.52 {'percentageOfActive': 0.0, 'percentageOfCompl... \n",
"\n",
" fractionOfFundStages simulation ... \\\n",
"6 {'percentageOfActiveFundsRequested': 0.0, 'per... 0 ... \n",
"12 {'percentageOfActiveFundsRequested': 0.0, 'per... 0 ... \n",
"18 {'percentageOfActiveFundsRequested': 0.0, 'per... 0 ... \n",
"24 {'percentageOfActiveFundsRequested': 0.0, 'per... 0 ... \n",
"30 {'percentageOfActiveFundsRequested': 0.0, 'per... 0 ... \n",
"\n",
" age_all \\\n",
"6 [1, 1, 1, 1, 1, 1, 1] \n",
"12 [2, 2, 2, 2, 2, 2, 2, 1] \n",
"18 [3, 3, 3, 3, 3, 3, 3, 2, 1] \n",
"24 [4, 4, 4, 4, 4, 4, 4, 3, 2] \n",
"30 [5, 5, 5, 5, 5, 5, 5, 4, 3, 1, 1] \n",
"\n",
" conviction_all \\\n",
"6 [127.28769625755204, 251.62005074526414, 267.8... \n",
"12 [255.16782502065337, 1727.5799200074293, 696.3... \n",
"18 [515.2498845075063, 2899.0500445722328, 1036.3... \n",
"24 [721.6771519212494, 3828.8464986139766, 1306.3... \n",
"30 [1044.718620106594, 4750.256628320954, 1562.01... \n",
"\n",
" triggers_all \\\n",
"6 [inf, inf, inf, inf, inf, inf, inf] \n",
"12 [inf, inf, inf, inf, inf, nan, inf, inf] \n",
"18 [inf, inf, inf, inf, inf, nan, inf, nan, inf] \n",
"24 [inf, inf, inf, inf, inf, nan, inf, nan, inf] \n",
"30 [inf, inf, inf, inf, inf, nan, inf, nan, inf, ... \n",
"\n",
" conviction_share_of_trigger_all \\\n",
"6 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] \n",
"12 [0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0] \n",
"18 [0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0] \n",
"24 [0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0] \n",
"30 [0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, ... \n",
"\n",
" percentageOfActiveProposals percentageOfCompletedProposals \\\n",
"6 0.00 0.00 \n",
"12 0.00 0.00 \n",
"18 0.00 0.00 \n",
"24 0.00 0.00 \n",
"30 0.00 0.00 \n",
"\n",
" percentageOfKilledProposals percentageOfActiveFundsRequested \\\n",
"6 0.14 0.00 \n",
"12 0.25 0.00 \n",
"18 0.22 0.00 \n",
"24 0.22 0.00 \n",
"30 0.18 0.00 \n",
"\n",
" percentageOfCompletedFundsRequested percentageOfKilledFundsRequested \n",
"6 0.00 0.34 \n",
"12 0.00 0.34 \n",
"18 0.00 0.34 \n",
"24 0.00 0.34 \n",
"30 0.00 0.34 \n",
"\n",
"[5 rows x 43 columns]"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head(5)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7ff2d3134c50>"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"df.plot('timestep','sentiment')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The above plot demonstrates system sentiment changing over time as proposals pass or fail."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7ff2d30460d0>"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"df.plot('timestep',['funds', 'candidate_funds'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the above graph, funds represent the total available funds, whereas candidate funds represent total funds requested by candidate proposals."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 612x792 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"affinities_plot(df)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The above matrix represents participant affinities towards proposals, ranging from -1 to +1."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x7ff2efd65d90>"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"df.plot(x='timestep',y=['candidate_count','active_count','completed_count', 'killed_count', 'failed_count'],\n",
" kind='area')\n",
"plt.title('Proposal Status')\n",
"plt.ylabel('count of proposals')\n",
"plt.legend(ncol = 3,loc='upper center', bbox_to_anchor=(0.5, -0.15))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The above graph shows the number of various types of proposals at a range of timesteps. Ecosystems with more completed proposals will have higher overall agent sentiment than systems with more failed and killed proposals."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x7ff2d2e83d50>"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"df.plot(x='timestep',y=['candidate_funds','active_funds','completed_funds', 'killed_funds', 'failed_funds'], kind='area')\n",
"plt.title('Proposal Status weighted by funds requested')\n",
"plt.ylabel('Funds worth of proposals')\n",
"plt.legend(ncol = 3,loc='upper center', bbox_to_anchor=(0.5, -0.15))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The above graph shows the amount of funds requested by various types of proposals at a range of timesteps."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"nets = df.network.values"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/aclarkdata/anaconda3/lib/python3.7/site-packages/networkx/drawing/nx_pylab.py:563: MatplotlibDeprecationWarning: \n",
"The iterable function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use np.iterable instead.\n",
" if not cb.iterable(width):\n",
"/home/aclarkdata/anaconda3/lib/python3.7/site-packages/networkx/drawing/nx_pylab.py:569: MatplotlibDeprecationWarning: \n",
"The iterable function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use np.iterable instead.\n",
" and cb.iterable(edge_color) \\\n",
"/home/aclarkdata/anaconda3/lib/python3.7/site-packages/networkx/drawing/nx_pylab.py:579: MatplotlibDeprecationWarning: \n",
"The iterable function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use np.iterable instead.\n",
" for c in edge_color]):\n",
"/home/aclarkdata/anaconda3/lib/python3.7/site-packages/networkx/drawing/nx_pylab.py:660: MatplotlibDeprecationWarning: \n",
"The iterable function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use np.iterable instead.\n",
" if cb.iterable(node_size): # many node sizes\n",
"/home/aclarkdata/repos/Aragon_Conviction_Voting/models/v3/model/parts/utils.py:356: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
" plt.tight_layout()\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"K = 0\n",
"N = 1\n",
"\n",
"snap_plot(nets[K:N], size_scale = 1/300,savefigs=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"On the left side are participants, with the right side of the graph being the proposals. With this graph, we can see the links between the participants and the proposals that they support. The percentage on the right hand are the the amount of the required amount to pass that has been fulfilled.\n",
"\n",
"You can move the K and N to different points within the 100 timesteps, 0 indexed, to see how the model evolves overtime. \n",
"\n",
"As you can see with the plot above at the start of the simulation, no proposals have been formally supported yet. Below we can see a many interactions between agents and proposals."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"snap_plot(nets[80:81], size_scale = 1/300,savefigs=True)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7ff2d13dd110>"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"df.plot('timestep',['total_supply','funds'])"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7ff2d1420090>"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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urGyWmRWa2argdUFQfo6ZrTSztcHPM8OWWWpmG8KWyQ7Ku5jZfDPbaGbLzWx49HdTRGJp14EqAPqqn6CE1ZIzg4eB8xoov9vdc4PX80HZTuAidz8BmA48Vm+ZK8KWKQ7KZgC73P0o4G7gV63eCxGJq7LyUBiomShxNRsG7r4MKGvJytz9XXffFnxcD3Qzs+bOG6cAjwTvnwbOMt2oLJJQyvYHZwYKg4TVlmsG15rZmqAZqU8D078KvOPulWFlvw+aiG4J+8IfDGwFcPeDwB6gX0MbNLOZZrbCzFaUlJS0oeoiEk2l5QqDRBdpGMwBRgC5wHbg1+ETzex4Qs093w0rviJoPjoteH29tRt19wfdPc/d87KysiKsuohEW5nCIOFFFAbuXuTuNe5eCzwEjK+bZmZDgGeBK919U9gyhcHPfcDjYcsUAkODZdOBXkBpJPUSkfgoLa/CDPpkKgwSVURhYGaHh328GFgXlPcG/gLc5O6vh82fbmb9g/cZwIV1ywCLCV1sBrgEeMXdP98Hroh0WGXllfTulkFaJ13uS1TN9lpqZk8A+UB/MysAbgPyzSwXcGAL/2oOuhY4CrjVzG4NyiYD5cCSIAjSgJcInVEAzAUeM7ONhC5UT237bolIe9LTx4mv2TBw92kNFM9tZN47gDsaWdVJjSxTAVzaXD1EpOMq3V9FPz1wltD0BLKItJnODBKfwkBE2qysvEpPHyc4hYGItEltrbPrgDqpS3QKAxFpk92fVlPresYg0SkMRKRNyspDnQwoDBKbwkBE2qR0f10ndbqbKJEpDESkTdQVRXJQGIhIm9R1UtdPdxMlNIWBiLRJ3ZmB+iVKbAoDEWmTsvIqenRNp3O6vk4SmY6eiLRJabmeMUgGCgMRaZOy8kpdPE4CCgMRaZPS/VX01W2lCU9hICJtUqZmoqSgMBCRiLmH+iVSJ3WJT2EgIhHbW3GQ6hrXmUESUBiISMT09HHyUBiISMTUSV3yUBiISMTUSV3yUBiISMQONRPpAnLCa1EYmNk8Mys2s3VhZbPMrNDMVgWvC8Km/dTMNprZBjM7N6z8vKBso5ndFFaeY2bLg/L5Zqb/WSIJYPnHZXTLSKO/wiDhtfTM4GHgvAbK73b33OD1PICZjQKmAscHy9xvZmlmlgbcB5wPjAKmBfMC/CpY11HALmBGpDskIu1jy85yFq0q5OsTj6BLelq8qyNt1KIwcPdlQFkL1zkFeNLdK939Y2AjMD54bXT3ze5eBTwJTDEzA84Eng6WfwT4civ2QUTi4P6lG8lI68S3T8uJd1UkCtp6zeBaM1sTNCP1CcoGA1vD5ikIyhor7wfsdveD9cpFpIPaWnaAZ94pZNr4YWT36Brv6kgUtCUM5gAjgFxgO/DrqNSoCWY208xWmNmKkpKSWG9ORBox57VNdDLjqjNGxLsqEiURh4G7F7l7jbvXAg8RagYCKASGhs06JChrrLwU6G1m6fXKG9rmg+6e5+55WVlZkVZdRNpg+55PeXpFAZfmDWFgL50VJIuIw8DMDg/7eDFQd6fRYmCqmXUxsxxgJPAW8DYwMrhzqDOhi8yL3d2BV4FLguWnA4sirZeIxNbsJR9S68738nVWkEzSm58FzOwJIB/ob2YFwG1AvpnlAg5sAb4L4O7rzWwB8B5wELjG3WuC9VwLLAHSgHnuvj7YxI3Ak2Z2B/AuMDcqeyciUfXKB0UsfKeAaycdxZA+mfGujkSRhf4wTzx5eXm+YsWKeFdDJGXsOVDNOXe/Rp/Mziz+/hd0O2mCMrOV7p5Xv7xFZwYiIrf/eT2l5VXM+8bJCoIkpO4oRKRZL79fxDPvFnJN/ghGD+4V7+pIDCgMRKRZ817/mGF9M7n2zJHxrorEiMJARJq050A1b24u48Ixh9M5XV8ZyUpHVkSa9MqGImpqncnHD4x3VSSGFAYi0qQX1heR3aMLY3StIKkpDESkURXVNbz2YQnnjBpAp04W7+pIDCkMRKRRb2zayYGqGjURpQCFgYg06oX1RfToks7EI/vFuyoSYwoDEWlQTa3z0vtF5B+brbuIUoCOsIg0aNXWXezcX8XkUQPiXRVpB+qOQlLappL93PnXDzhYUxvvqnQ4W3d9SkaakX+MuotPBQoDSWlPLP+EVz8oZtSgnvGuSoeT2TmN754+gh5dM+JdFWkHCgNJaa9uKGbiiH48NmNCvKsiEle6ZiApa2vZATaVlJN/THa8qyISdwoDSVlLNxQDMElt4iIKA0ldSzeUMKxvJjn9D4t3VUTiTmEgKamiuobXN+1k0jFZmKmbBRGFgaSk5R+XUVFdS/6xul4gAgoDSVFLNxTTJb2TulkQCSgMJCUt3VDCxBH96JqhsXxFoAVhYGbzzKzYzNY1MO3HZuZm1j/4/O9mtip4rTOzGjPrG0zbYmZrg2krwtbR18xeNLOPgp99ormDIvVt2VnOxzvLmaRbSkUOacmZwcPAefULzWwoMBn4pK7M3e9y91x3zwV+Crzm7mVhi00KpueFld0EvOzuI4GXg88iMfPUyq0A6mZBJEyzYeDuy4CyBibdDdwAeCOLTgOeaEEdpgCPBO8fAb7cgmVEIvK/H5Vw/9JNfDl3EEf00y2lInUiumZgZlOAQndf3cj0TEJnEwvDih14wcxWmtnMsPIB7r49eL8DUBeJEhPbdn/K9U+uYmR2d/7zKyfEuzoiHUqr+yYKvuhvJtRE1JiLgNfrNRGd6u6FZpYNvGhmHwRnHYe4u5tZY2caBCEyE2DYsGGtrbokKXen8mDTvY4erHWuefwdKqtrmPNvJ5HZWd1yiYSL5DdiBJADrA4e1hkCvGNm4919RzDPVOo1Ebl7YfCz2MyeBcYDy4AiMzvc3beb2eFAcWMbdvcHgQcB8vLyGg0NSS3f+8M7/G39juZnBO772omMyOoe4xqJJJ5Wh4G7rwUO3YZhZluAPHffGXzuBZwB/FvYPIcBndx9X/B+MvCzYPJiYDpwZ/BzUUR7Iilp+55PWfLeDs4+bgAnHdH0jWhHZXfnHA3UItKgZsPAzJ4A8oH+ZlYA3Obuc5tY5GLgBXcvDysbADwbnEmkA4+7+9+CaXcCC8xsBvBP4LJW74WkrD+9uw13+L9fPI7h6mNIJGLNhoG7T2tm+vB6nx8mdDtqeNlmYGwjy5cCZzVXD5H63J1n3ingpCP6KAhE2khPIEvCWle4l4+K9/OVEwfHuyoiCU9hIAlr4TsFdE7rxIUnDIp3VUQSnsJAElJ1TS2LV2/j7FHZ9MrUGL0ibaUwkIT02oYSysqr+OqJQ+JdFZGkoDCQhPTMuwX0O6wzpx+t/oVEokFhIAlnb0U1L71fzEVjB5GRpv/CItGg3yRJOH9bu4Oqg7V8eZzuIhKJFoWBJJxFqws5ol8mY4f0indVRJKGwkASStHeCt7YVMqU3MEayF4kihQGklD+vDrU/cSUXD1bIBJNCgNJKItWbeOEwb3U86hIlCkMJGFsKtnP2sI9OisQiQGFgSSMRau20cngS2MVBiLRpjCQhPH82u1MHNGP7J5d410VkaSjMJCEUFvrbNlZztghveNdFZGkpDCQhFBaXsXBWmdgL50ViMSCwkASQtHeCgAGqIlIJCYUBpIQduxRGIjEksJAEkLRvlAYDFQYiMSEwkASQtGeCjoZ9O/eOd5VEUlKCgNJCDv2VtC/exfS1WW1SEy06DfLzOaZWbGZrWtg2o/NzM2sf/A538z2mNmq4HVr2LznmdkGM9toZjeFleeY2fKgfL6Z6c8/+YyivZW6k0gkhlr6Z9bDwHn1C81sKDAZ+KTepP9199zg9bNg3jTgPuB8YBQwzcxGBfP/Crjb3Y8CdgEzWrsjktyK9lbo4rFIDLUoDNx9GVDWwKS7gRsAb8FqxgMb3X2zu1cBTwJTLNQP8ZnA08F8jwBfbkm9JHXs2FvBgJ5d4l0NkaQVcQOsmU0BCt19dQOTJ5rZajP7q5kdH5QNBraGzVMQlPUDdrv7wXrlIgBUVNew+0C17iQSiaH0SBYys0zgZkJNRPW9Axzh7vvN7ALgT8DIyKv4me3OBGYCDBs2LBqrlARQvLcS0DMGIrEU6ZnBCCAHWG1mW4AhwDtmNtDd97r7fgB3fx7ICC4uFwJDw9YxJCgrBXqbWXq98s9x9wfdPc/d87KysiKsuiSaHXr6WCTmIgoDd1/r7tnuPtzdhxNq2jnR3XeY2cDgOgBmNj7YRinwNjAyuHOoMzAVWOzuDrwKXBKsfjqwqE17JUmlrisK3U0kEjstvbX0CeAfwDFmVmBmTd3tcwmwzsxWA/cAUz3kIHAtsAR4H1jg7uuDZW4EfmRmGwldQ5gb2e5IMjrUL1EPhYFIrLTomoG7T2tm+vCw978DftfIfM8DzzdQvpnQ3UYin7NjTwVdMzrRs1tEl7hEpAX0OKd0eEX7KhnYsytB66OIxIDCQDq8oj0VGt1MJMYUBtLh7dhboWcMRGJMYSAdmrtTtLdCdxKJxJjCQDq0PZ9WU3mwluwe6opCJJYUBtKh7dAzBiLtQmEgHVpR0BWFrhmIxJbCQDq0Io19LNIuFAbSodU1E2Wr+2qRmFIYSIdWtLeCvod1pkt6WryrIpLUFAbSoRXtrdCdRCLtQGEgHdoOPWMg0i7U85fERU2t8/TKreyvrGlyvk9KD3D84b3aqVYiqUthIHHx4ntF3LhwbYvmHT24Z4xrIyIKA4mLP71bSP/uXXjpR6c32RtpJ4MeXTPasWYiqUlhIO1uz4FqXvmgmK9PPILemZ3jXR0RQReQJQ7+snY7VTW1XDxucLyrIiIBhYG0uz+9W8hR2d05fpCuBYh0FAoDaVdbyw7w1pYyLh43WCOXiXQgCgNpV4tXbwPgS2MHxbkmIhJOYSDtxt155p0Cxg/vy9C+mfGujoiEafZuIjObB1wIFLv76HrTfgzMBrLcfaeZXQHcCBiwD/ieu68O5t0SlNUAB909LyjvC8wHhgNbgMvcfVc0dk6i4+4XP2T+21vbvB7HKdpbyYxTj4xCrUQkmlpya+nDwO+AR8MLzWwoMBn4JKz4Y+AMd99lZucDDwITwqZPcved9dZ/E/Cyu99pZjcFn29s1V5ITD37biGZndM4eXjfNq8rs0saU3LVRCTS0TQbBu6+zMyGNzDpbuAGYFHYvG+ETX8TGNKCOkwB8oP3jwBLURh0GHsOVPNJ2QFuOO8Yrs4/Kt7VEZEYieiagZlNAQrrmoAaMQP4a9hnB14ws5VmNjOsfIC7bw/e7wAGRFIniY312/cAMHqQ+gcSSWatfgLZzDKBmwk1ETU2zyRCYXBqWPGp7l5oZtnAi2b2gbsvC1/O3d3MvIn1zgRmAgwbNqy1VZcIrC/cC6BnAkSSXCRnBiOAHGB1cFF4CPCOmQ0EMLMxwP8AU9y9tG4hdy8MfhYDzwLjg0lFZnZ4sOzhQHFjG3b3B909z93zsrKyIqi6tNa6bXsY1Ksr/bprTAGRZNbqMHD3te6e7e7D3X04UACc6O47zGwY8AzwdXf/sG4ZMzvMzHrUvSd0VrEumLwYmB68n07YNQiJv3WFexg9WE1EIsmu2TAwsyeAfwDHmFmBmc1oYvZbgX7A/Wa2ysxWBOUDgL+b2WrgLeAv7v63YNqdwDlm9hFwdvBZOoD9lQfZvLNcYSCSAlpyN9G0ZqYPD3v/beDbDcyzGRjbyPKlwFnN1UPa3/vb9+Ku8QREUoGeQJZGrSvUnUQiqUJhII1aV7iXrB5dyO6pMYhFkp3CQBq1ftseRuuWUpGUoDCQBlVU1/BR8X5dPBZJEQoDadAHO/ZRU+scr+sFIilBYSANOnTxWHcSiaSEVndHIR3b1rID7K882Ob1/GNzKX0yMxjcu1sUaiUiHZ3CIInc9+pG7lqyIWrrO+PoLA1NKZIiFAZJ4sFlm7hryQYuHHM4F445PCrrzB3aJyrrEZGOT2HQAVVU17Rq/j8u/4T/fP4DLhxzOL+5PJf0NF0KEpHWURh0IFvLDvDvT6/mzc1lrV72/NEDuVtBICIRUhh0AO7OUysL+Nmf3wPg+2ceRWbnlh+aXt0yuOSkIWQoCEQkQgqDKDtYU8vUB99kS2l5i5epqXV2HahmQk5fZl86lqF9M2NYQxGRz1MYRDCr6QsAAAjTSURBVNn/btzJin/u4rzjB9Kve+cWL3fc4T352vhhdOqku3dEpP0pDKJs4coCemdm8NtpuXRJT4t3dUREWkSNzFG059NqXniviC+NHaQgEJGEojCIoufXbqfqYC1fPXFIvKsiItIqCoMoWriygKOyuzNmiDp3E5HEojCIki07y1nxz1189cQh6sJBRBKOwiBKnnm3EDO4eNzgeFdFRKTVdDdRCxXsOsA/NpWyumA31Qf9c9Nf/qCYU4/qz8BeGiJSRBJPi8LAzOYBFwLF7j663rQfA7OBLHffaaE2kt8CFwAHgG+4+zvBvNOB/xsseoe7PxKUnwQ8DHQDngeud/fPf+NGwb0vf8Ti1dtatUx55UG27akAoEfXdA5r4OngLumdmHFqTlTqKCLS3lp6ZvAw8Dvg0fBCMxsKTAY+CSs+HxgZvCYAc4AJZtYXuA3IAxxYaWaL3X1XMM93gOWEwuA84K+R7VLTsnp0YeSA7q1aJiOtE7lDezNxRD+Ozu6hB8NEJOm0KAzcfZmZDW9g0t3ADcCisLIpwKPBX/ZvmllvMzscyAdedPcyADN7ETjPzJYCPd39zaD8UeDLxCgMpo4fxtTxw2KxahGRhBXxBWQzmwIUuvvqepMGA1vDPhcEZU2VFzRQ3tA2Z5rZCjNbUVJSEmnVRUSknojCwMwygZuBW6Nbnaa5+4PunufueVlZWe25aRGRpBbpmcEIIAdYbWZbgCHAO2Y2ECgEhobNOyQoa6p8SAPlIiLSTiIKA3df6+7Z7j7c3YcTato50d13AIuBKy3kFGCPu28HlgCTzayPmfUhdOF5STBtr5mdEtyJdCWfvQYhIiIx1qIwMLMngH8Ax5hZgZnNaGL254HNwEbgIeBqgODC8c+Bt4PXz+ouJgfz/E+wzCZidPFYREQaZjG6nT/m8vLyfMWKFfGuhohIQjGzle6eV79c3VGIiIjCQEREEriZyMxKgH+2YpH+wM4YVacjS8X9TsV9htTc71TcZ2jbfh/h7p+7Nz9hw6C1zGxFQ+1kyS4V9zsV9xlSc79TcZ8hNvutZiIREVEYiIhIaoXBg/GuQJyk4n6n4j5Dau53Ku4zxGC/U+aagYiINC6VzgxERKQRKREGZnaemW0ws41mdlO86xMLZjbUzF41s/fMbL2ZXR+U9zWzF83so+Bnn3jXNdrMLM3M3jWz54LPOWa2PDje882sc7zrGG3BOCFPm9kHZva+mU1M9mNtZj8M/m+vM7MnzKxrMh5rM5tnZsVmti6srMFjG/QBd0+w/2vM7MRIt5v0YWBmacB9hEZgGwVMM7NR8a1VTBwEfuzuo4BTgGuC/bwJeNndRwIvB5+TzfXA+2GffwXc7e5HAbuApvrSSlS/Bf7m7scCYwntf9IeazMbDFwH5AVD76YBU0nOY/0wodEewzV2bMNHlpxJaNTIiCR9GADjgY3uvtndq4AnCY3GllTcfXvdWNPuvo/Ql8NgQvv6SDDbI4RGkUsaZjYE+CKhjg4Jer49E3g6mCUZ97kXcDowF8Ddq9x9N0l+rAmNzNjNzNKBTGA7SXis3X0ZUFavuLFje2hkyWC0yLqRJVstFcKgsRHWklYwROk4QmNKDwi6CQfYAQyIU7Vi5TeEhl6tDT73A3a7+8HgczIe7xygBPh90Dz2P2Z2GEl8rN29EJhNaLz17cAeYCXJf6zrNHZso/b9lgphkFLMrDuwEPiBu+8NnxaMS500t4+Z2YVAsbuvjHdd2lk6cCIwx93HAeXUaxJKwmPdh9BfwTnAIOAwPt+UkhJidWxTIQwaG2Et6ZhZBqEg+KO7PxMUF9WdNgY/i+NVvxj4AvClYLS9Jwk1GfyW0KlyejBPMh7vAqDA3ZcHn58mFA7JfKzPBj529xJ3rwaeIXT8k/1Y12ns2Ebt+y0VwuBtYGRw10FnQhedFse5TlEXtJXPBd539/8XNmkxMD14P50kGkXO3X/q7kOC0famAq+4+xXAq8AlwWxJtc8AwYiCW83smKDoLOA9kvhYE2oeOsXMMoP/63X7nNTHOkxjx7axkSVbz92T/gVcAHxIaBS1/4h3fWK0j6cSOnVcA6wKXhcQakN/GfgIeAnoG++6xmj/84HngvdHAm8RGjnvKaBLvOsXg/3NBVYEx/tPQJ9kP9bA7cAHwDrgMaBLMh5r4AlC10WqCZ0Fzmjs2AJG6G7JTcBaQndbRbRdPYEsIiIp0UwkIiLNUBiIiIjCQEREFAYiIoLCQEREUBhIigt6/7w6eD/IzJ5ubpk2bCvXzC6I1fpF2kJhIKmuN3A1gLtvc/dLmpm/LXIJPfsh0uHoOQNJaWZW14vtBkIP9Bzn7qPN7BuEeoY8jFD3wLOBzsDXgUrgAncvM7MRhB76yQIOAN9x9w/M7FLgNqCGUKdqZxN6MKoboe4Cfgk8B9wLjAYygFnuvijY9sVAL0Kdjv3B3W+P8T+FpLj05mcRSWo3AaPdPTfo7fW5sGmjCfX+2pXQF/mN7j7OzO4GriTUY+qDwFXu/pGZTQDuJ9RH0q3Aue5eaGa93b3KzG4l9ITotQBm9p+EutD4lpn1Bt4ys5eCbY8Ptn8AeNvM/uLuK2L5DyGpTWEg0rhXPTQ2xD4z2wP8OShfC4wJeoj9P8BToe5ygFAXCQCvAw+b2QJCnao1ZDKhjvZ+EnzuCgwL3r/o7qUAZvYMoe5GFAYSMwoDkcZVhr2vDftcS+h3pxOh/vRz6y/o7lcFZwpfBFaa2UkNrN+Ar7r7hs8Uhpar336r9lyJKV1AllS3D+gRyYIeGi/i4+D6QN14tGOD9yPcfbm730poIJqhDWxrCfD9oBdOzGxc2LRzgnFvuxG6dvF6JHUUaSmFgaS0oCnm9WDw8bsiWMUVwAwzWw2s519Dqt5lZmuD9b4BrCbU3fIoM1tlZpcDPyd04XiNma0PPtd5i9DYFGuAhbpeILGmu4lEOpjgbqJDF5pF2oPODERERGcGIiKiMwMREUFhICIiKAxERASFgYiIoDAQEREUBiIiAvx/gkiX5/X4ZikAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"df.plot('timestep',['effective_supply'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As expected *effective_supply* is increasing with the arrival of new participants."
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7ff2d141ba50>"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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y3sh+SvIZ7LSJ3t3vcvdCdy8CbgRecPdPm9kwAIv9z3Yd8Hac6n8EZptZfzPrD8wO1olIBti9/whrdx5g1iT1qslkHRnUbJGZFQAGrAZuATCzEuAWd7/Z3WvM7DvAyqDOfU03ZkUk/f1pXRSA2ZPVqyaTtSnRu/tyYHnwfmYrZcqBm5stPwo82u4IRSRlyiJRziroydgC9arJZHoyVkTiOnDkGK9u2ctsNdtkPCV6EYlr+YYqGhpdzTYhoEQvInGVRqIU9M6luLBfqkORDlKiF5FTHG04zp83VHPVxMFkZelhqEynRC8ip3h1y15qjzaofT4klOhF5BSlkSg9u3fj4rEDUx2KJIESvYicpLHRKYtEmTF+MHk53VIdjiSBEr2InGTNzv1UHzqqoYdDRIleRE5SGomSnWVcMX5wqkORJFGiF5GTlEWiTDtrIH175KQ6FEkSJXoROWFLdS2bq2rVbBMySvQickJZJDaImRJ9uCjRi8gJZZEoU0f0ZXi//FSHIkmkRC8iAFQdquON7ft0NR9CSvQiAsDSdVW4a+z5MEo40ZtZNzN708yWBMuLzGyDmb1tZo+aWdxb9GZ23MxWB6/FyQpcRJKrLBJl5IB8xg/pnepQJMnackV/O7Cu2fIiYAIwFcin2WQjLRxx9+LgNa99YYpIZ6o92sDLm/cwe9JQYrODSpgklOjNrBC4BvhZ0zp3/+9gcnAHVhCb+FtEMtCLG6upb2hkttrnQynRK/oHgTuBxpYbgiabfwD+0ErdPDMrN7PXzOy6eAXMbEFQpry6ujrBkEQkWUorKunfI4cLRvdPdSjSCU6b6M1sLlDl7qtaKfIw8KK7v9TK9tHuXgJ8CnjQzMa2LODuC929xN1LCgoKEo1dRJLg2PFGXlhfxZUTh5DdTf0zwiiRszodmGdm24CngJlm9gsAM7sHKAC+2lpld98VfH2H2MTi53UsZBFJphVbazhY16BmmxA7baJ397vcvdDdi4AbgRfc/dNmdjMwB/iku5/SpANgZv3NLDd4P4jYH41I0qIXkQ4rragkLyeLy8bpv+mw6sj/af8BDAFeDbpO3g1gZiVm1nTTdiJQbmZrgGXA991diV4kTbjHxp6/bFwB+d019nxYZbelsLsvJ9b8grvHrevu5QRdLd39FWLdL0UkDVXsPsjuA3V8ZdY5qQ5FOpHuvIh0YaUVlWQZXDVR7fNhpkQv0oWVRqKUFA1gQM/uqQ5FOpESvUgXtX3v+6yvPKTeNl2AEr1IF1UaqQQ09nxXoEQv0kWVRqJMGNqb0QN7pjoU6WRK9CJdUM3hesq31ehqvotQohfpgpaui9LoMHvS0FSHImeAEr1IF1QWiTKsbx5TRvRJdShyBrTpgSmRTPL86l2s3Xkg1WGkpRc3VfP3JSM19nwXoUQvoVR7tIGv/2YtGHTXiIyn6NE9m789X1NIdBVK9BJKL26spv54I08tmMa0swamOhyRlNKljoRS00QaJZpIQ0SJXsJHE2mInEy/BRI6TRNpqI+4SIwSvYRO00Qal2siDRFAiV5CpmkijUvP1kQaIk0STvRm1s3M3jSzJcHyGDN73cw2m9mvzCzuOKdmdldQZoOZzUlW4CLxNE2kMXuymm1EmrTliv52YF2z5R8AP3L3s4F9wOdaVjCzScTmmZ0MXA08bGa6zJJO0zSRxpUTBqc6FJG0kVCiN7NC4BrgZ8GyATOBp4MijwPXxal6LfCUux91963AZuCijgYt0prSSJSS0QMY2Cs31aGIpI1Er+gfBO4EGoPlgcB+d28IlncCI+LUGwHsaLYct5yZLTCzcjMrr66uTjAkkZM1TaSh3jYiJzttojezuUCVu6/qrCDcfaG7l7h7SUGBekpI+zRNpKH2eZGTJTIEwnRgnpl9FMgD+gA/BvqZWXZwVV8I7IpTdxcwstlya+VEOqwsEmX8EE2kIdLSaa/o3f0udy909yJiN1ZfcPebgGXA9UGx+cDzcaovBm40s1wzGwOMA1YkJXKRZmoO17NyW42u5kXi6Eg/+m8AXzWzzcTa7H8OYGbzzOw+AHevAH4NRIA/ALe5+/GOhSxyqhfWV9Homv9UJJ42jV7p7suB5cH7d4jTg8bdFxO7km9a/h7wvY4EKXI6pRWVDO2Tx9QRfVMdikja0ZOxkvGO1B/nxU3VzJ48RBNpiMShRC8Z7+XNe6g71qhmG5FWKNFLxiutqKR3XjYfHqMJRkTi0QxTkrYqD9TxlV+9yZFjjR9YbkPlQeZMHkr3bF23iMSjRC9p67nVu3jtnRouP6eAD2p5v2TsID47fcwZi0sk0yjRS9oqrahkyog+PPFZDY8k0hH6X1fSUtWhOt7csZ9ZE4emOhSRjKdEL2lp6boq3DVujUgyKNFLWiqtqGTkgHwmDO2d6lBEMp4SvaSd2qMN/GXLXmZNHKoHoESSQIle0s6LG6upb2hUs41IkijRS9opi0Tp3yOHktH9Ux2KSCgo0UtaOXa8kaXrosycMITsbvrxFEkG/SZJWlmxtYaDdQ1qthFJIiV6SStlkSi52VlcNm5QqkMRCQ0lekkb7k5pRSWXjSugR3c9tC2SLIlMDp5nZivMbI2ZVZjZt4P1L5nZ6uC128yea6X+8WblFscrIwJQsfsguw/UqdlGJMkSuWw6Csx091ozywFeNrP/cffLmgqY2TPEnzMW4Ii7FychVgm50kiULIMrJwxOdSgioZLI5ODu7rXBYk7w8qbtZtYHmAnEvaIXSVRpRSUlowcwsFduqkMRCZWE2ujNrJuZrQaqgDJ3f73Z5uuApe5+sJXqeWZWbmavmdl1rex/QVCmvLq6uk0HIOGwo+Z91lceUrONSCdIKNG7+/Gg+aUQuMjMpjTb/EngyQ+oPtrdS4BPAQ+a2dg4+1/o7iXuXlJQUNCG8CUsSiNRAE0HKNIJ2tTrxt33A8uAqwHMbBBwEfD7D6izK/j6DrAcOK+dsUqIlVZUMn5Ib0YP7JnqUERCJ5FeNwVm1i94nw/MAtYHm68Hlrh7XSt1+5tZbvB+EDAdiCQjcAmPfYfrWbmtRs02Ip0kkSv6YcAyM1sLrCTWRr8k2HYjLZptzKzEzH4WLE4Eys1sDbH/BL7v7kr0cpKl66todDXbiHSW03avdPe1tNLc4u4z4qwrB24O3r8CTO1YiBJ2pRWVDO2Tx9QRfVMdikgo6clYSakj9cd5cVM1syYN0djzIp1EiV5S6uXNe6g7prHnRTqTEr2kVGlFJb3zsvnwmIGpDkUktJToJWWONzpL11dxxfjBdM/Wj6JIZ9Fvl6TMqnf3UXO4Xs02Ip1MiV5SpixSSfduWfzNOXoaWqQzKdFLSrg7pZEoF48dSO+8nFSHIxJqmt1Bkq6+oZH3Dhz5wDI7ao7w7t73WXD5WWcoKpGuS4leku72p97kf96uPG25LIOrJqp9XqSzKdFLUtUebWDp+ipmTxrC1VOGfmDZ4f3yGdIn7wxFJtJ1KdFLUr24sZr6hkY+e+kYpp2lvvEi6UA3YyWpyiJR+vXIoWR0/1SHIiIBJXpJmmPHG1m6LsqVE4aQ3U0/WiLpQr+NkjQrt9ZwsK5BD0CJpBklekma0kiU3OwsLhs3KNWhiEgzicwwlWdmK8xsjZlVmNm3g/WPmdlWM1sdvIpbqT/fzDYFr/nJPgBJD+5OaUUll40roEd33eMXSSeJ/EYeBWa6e62Z5QAvm9n/BNu+7u5Pt1bRzAYA9wAlgAOrzGyxu+/raOCSXip2H2T3gTq+ctU5qQ5FRFpIZIYpB2qDxZzg5Qnufw6xqQdrAMysjNjE4k9+YC05Y9bu3M/ho8c7vJ/Fa3aTZXDlxMFJiEpEkimh/7HNrBuwCjgbeMjdXzezLwDfM7O7gaXAN939aIuqI4AdzZZ3Buta7n8BsABg1KhRbT4IaZ/X3tnLjQtfS9r+pp01gIG9cpO2PxFJjoQSvbsfB4rNrB/wrJlNAe4CKoHuwELgG8B97QnC3RcG+6CkpCTR/xakg/7wdiV5OVn8fP6FZCVhGr/xQ3snISoRSbY23TVz9/1mtgy42t0fCFYfNbP/Au6IU2UXMKPZciGwvB1xSpI1v3k6/Wz1khEJs0R63RQEV/KYWT4wC1hvZsOCdQZcB7wdp/ofgdlm1t/M+gOzg3WSYk03T2dNUp93kbBL5Ip+GPB40E6fBfza3ZeY2QtmVgAYsBq4BcDMSoBb3P1md68xs+8AK4N93dd0Y1ZSqzQSjd08naCbpyJhl0ivm7XAeXHWz2ylfDlwc7PlR4FHOxCjdILSikpKinTzVKQr0JOxXdCOmvdZX3mI2Wq2EekSlOi7oNJIFEDt8yJdhBJ9F1QWqWTC0N6MHtgz1aGIyBmgQUkyxDvVtby5fX+H93O80VmxtYbbrjg7CVGJSCZQos8Qt/3yTda9dzAp+zKDj04dlpR9iUj6U6LPANv3vs+69w5y+5Xj+MT5hR3eX373bhT0Vm8bka5CiT4DlEYqAfjE+YWMGtgjxdGISKbRzdgMUBqJMmFobyV5EWkXJfo0V3O4nvJtNeoKKSLtpkSf5paui9LoMHvS0FSHIiIZSok+zZVGogzrm8eUEX1SHYqIZCgl+jR2pP44L22qZtakIVgSxosXka5JiT6NvbSpmrpjjWq2EZEOUffKM8Td+eWK7dTU1idc588bq+mdl82HzxrQiZGJSNgp0Z8hb2zfx7eejTc3ywebf/FocrrpHy8RaT8l+jOktCJKdpax8ltX0Tsv8W97tpK8iHTQaTOOmeUBLwK5Qfmn3f0eM1sElADHgBXA/3L3Y3HqHwfeCha3u/u8ZAWfKdyd0kiUi8cOpH/P7qkOR0S6mEQuF48CM939XKAYuNrMpgGLgAnAVCCfZrNKtXDE3YuDV5dL8gBbqmvZuuewJvoQkZRIZCpBB2qDxZzg5e7+301lzGwF0PHRtkLqjxWxiT6uUqIXkRRIqAHYzLqZ2WqgCihz99ebbcsB/gH4QyvV88ys3MxeM7PrWtn/gqBMeXV1dRsPIf2VRaJ8qLAvw/rmpzoUEemCEkr07n7c3YuJXbVfZGZTmm1+GHjR3V9qpfpody8BPgU8aGZj4+x/obuXuHtJQUFBGw8hvUUP1rF6x34124hIyrSpS4e77weWAVcDmNk9QAHw1Q+osyv4+g6wHDivnbFmpLJgftbZk/XQk4ikxmkTvZkVmFm/4H0+MAtYb2Y3A3OAT7p7Yyt1+5tZbvB+EDAdiCQr+ExQFokyemAPxg3ulepQRKSLSqRD9zDgcTPrRuwPw6/dfYmZNQDvAq8G47D81t3vM7MS4BZ3vxmYCDxiZo1B3e+7e8Ym+s1Vh3j0L9uI3Z9OzCtb9vCZS4o0Vo2IpEwivW7WEqe5xd3j1nX3coKulu7+CrHul6Hw8LItLF6zmwFt6As/tG8ef5uE6f9ERNpLT8Ym6NjxRpaur2Je8XB++PfFqQ5HRCRher4+QSu31nDgyDGNJCkiGUeJPkGlkSi52Vlcfs6gVIciItImSvQJcHfKIlEuGzeIHt3V2iUimUWJPgEVuw+ya/8RNduISEZSok9AWSSKGcycODjVoYiItJkSfQJKI1FKRvdnUK/cVIciItJmSvSnsaPmfda9d1DNNiKSsXRnMfD0qp0sXrP7lPXVh44CMEuDkolIhlKiJ9ar5oE/bqCh0Snsf/JQwrnZWXzyolEUDeqZouhERDpGiR54a9cBKg/W8W9/dy6fuEDDFYhIuKiNntjE3d2yjJkT1KtGRMJHiR4ojVRyYVF/TdwtIqHU5RP9tj2H2RitZZZ61YhISHX5RH9iBij1qhGRkFKij0SZOKwPIwf0SHUoIiKdIpGpBPPMbIWZrTGzCjP7drB+jJm9bmabzexXZha3gdvM7grKbDCzOck+gI7YU3uU8ndr1EdeREItkSv6o8BMdz8XKAauNrNpwA+AH7n72cA+4HMtK5rZJOBGYDKxCcUfDqYkTAsvrKui0dVsIyLhlshUgg7UBos5wcuBmcCngvWPA/cC/96i+rXAU+5+FNhqZpuBi4BXOxx5C/vfr+fv/qNtu606dJQR/fKZPLxPssMREUkbCT0wFVyFrwLOBh4CtgD73b0hKLITGBGn6gjgtWbLccuZ2QJgAcYEOAwAAAcDSURBVMCoUaMSjf0kWVnGuCG92lRn3JBefHTqME3cLSKhllCid/fjQLGZ9QOeBSYkMwh3XwgsBCgpKfH27KNPXg4P33RBMsMSEQmFNvW6cff9wDLgYqCfmTX9oSgEdsWpsgsY2Wy5tXIiItJJEul1UxBcyWNm+cAsYB2xhH99UGw+8Hyc6ouBG80s18zGAOOAFckIXEREEpNI080w4PGgnT4L+LW7LzGzCPCUmX0XeBP4OYCZzQNK3P1ud68ws18DEaABuC1oBhIRkTPEYp1q0kdJSYmXl5enOgwRkYxiZqvcvSTeti7/ZKyISNgp0YuIhJwSvYhIyCnRi4iEXNrdjDWzauDdNlYbBOzphHDSWVc8Zuiax90Vjxm65nF35JhHu3tBvA1pl+jbw8zKW7vbHFZd8Zihax53Vzxm6JrH3VnHrKYbEZGQU6IXEQm5sCT6hakOIAW64jFD1zzurnjM0DWPu1OOORRt9CIi0rqwXNGLiEgrlOhFREIuoxO9mV0dTDq+2cy+mep4OouZjTSzZWYWCSZovz1YP8DMysxsU/C1f6pjTTYz62Zmb5rZkmA5oUnpM5WZ9TOzp81svZmtM7OLu8h5/t/Bz/bbZvakmeWF8Vyb2aNmVmVmbzdbF/f8WsxPguNfa2bnt/dzMzbRB8MmPwR8BJgEfDKYjDyMGoCvufskYBpwW3Cs3wSWuvs4YGmwHDa3E5v/oMlpJ6XPcD8G/uDuE4BziR17qM+zmY0AvkxsePMpQDfgRsJ5rh8Drm6xrrXz+xFic3iMIzbVass5uROWsYme2CTjm939HXevB54iNhl56Lj7e+7+RvD+ELFf/hHEjvfxoNjjwHWpibBzmFkhcA3ws2DZiE1K/3RQJFTHbGZ9gcsJ5nZw9/pgVrdQn+dANpAfzFrXA3iPEJ5rd38RqGmxurXzey3whMe8RmxWv2Ht+dxMTvQjgB3NlluboDxUzKwIOA94HRji7u8FmyqBISkKq7M8CNwJNAbLA0lsUvpMNQaoBv4raK76mZn1JOTn2d13AQ8A24kl+APAKsJ9rptr7fwmLcdlcqLvcsysF/AM8BV3P9h8m8f6yYamr6yZzQWq3H1VqmM5g7KB84F/d/fzgMO0aKYJ23kGCNqkryX2h2440JNTmze6hM46v5mc6LvUxONmlkMsyS9y998Gq6NN/8oFX6tSFV8nmA7MM7NtxJrlZhJrv05kUvpMtRPY6e6vB8tPE0v8YT7PAFcBW9292t2PAb8ldv7DfK6ba+38Ji3HZXKiXwmMC+7Mdyd282ZximPqFEHb9M+Bde7+w2abFhObmB1an6A9I7n7Xe5e6O5FxM7tC+5+E4lNSp+R3L0S2GFm44NVVxKbbzm05zmwHZhmZj2Cn/Wm4w7tuW6htfO7GPjHoPfNNOBAsyaetnH3jH0BHwU2AluAb6U6nk48zkuJ/Tu3FlgdvD5KrM16KbAJ+BMwINWxdtLxzwCWBO/PAlYAm4HfALmpji/Jx1oMlAfn+jmgf1c4z8C3gfXA28D/A3LDeK6BJ4ndhzhG7D+4z7V2fgEj1rNwC/AWsV5J7fpcDYEgIhJymdx0IyIiCVCiFxEJOSV6EZGQU6IXEQk5JXoRkZBTopdQCkaBvDV4P9zMnj5dnQ58VrGZfbSz9i/SUUr0Elb9gFsB3H23u19/mvIdUUzsuQaRtKR+9BJKZtY0mukGYg+iTHT3KWb2GWKjA/YkNvzrA0B34B+Ao8BH3b3GzMYSe1ilAHgf+Ly7rzezvwPuAY4TG3zrKmIP9OQTezz9/wBLgP8LTAFygHvd/fngsz8O9CU2ONUv3P3bnfytECH79EVEMtI3gSnuXhyM+Lmk2bYpxEYAzSOWpL/h7ueZ2Y+AfyQ2auZC4BZ332RmHwYeJjbezt3AHHffZWb93L3ezO4m9tTiFwHM7F+IDdnwWTPrB6wwsz8Fn31R8PnvAyvN7PfuXt6Z3wgRJXrpipZ5bFz/Q2Z2APhdsP4t4EPBKKGXAL+JDb0CxB7JB/gL8JiZ/ZrY4FvxzCY2INsdwXIeMCp4X+buewHM7LfEhrdQopdOpUQvXdHRZu8bmy03EvudyCI2Fnpxy4rufktwhX8NsMrMLoizfwM+4e4bTloZq9eyrVRtp9LpdDNWwuoQ0Ls9FT021v/WoD2+ae7Oc4P3Y939dXe/m9gkISPjfNYfgS8FIzFiZuc12zYrmCM0n9i9gr+0J0aRtlCil1AKmkf+EkzCfH87dnET8DkzWwNU8NdpKu83s7eC/b4CrCE2nO4kM1ttZjcA3yF2E3atmVUEy01WEJtXYC3wjNrn5UxQrxuRMyTodXPipq3ImaIrehGRkNMVvYhIyOmKXkQk5JToRURCToleRCTklOhFREJOiV5EJOT+P8GNsWH7+E2EAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"df.plot('timestep',['participant_count'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Review system health metrics\n",
"\n",
"Below we will analysis system health metrics, such as fraction of supply used for voting, which is effective supply over total supply and percentage of proposals and requested funds in different stages. "
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7ff2d14dd4d0>"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"df.plot('timestep',['fractionOfSupplyForVoting'])"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7ff2d2da5150>"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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XYRMdHe1iY/23EX/ybAIvjFvBj2v20KZqJG8/GEVIlkBflyUiGZyZxTnnkuzS1p2iaSQsaxAD21Xl+XtK8c3SeP40dCF7j2WKEZ0i4iMK9DQUEGD0vqckQzpUY9Pe4zzwSQxLdxz2dVki4qcU6LfA/eUL8m3POoRkCeTRIQv5eolmbBSR1KdAv0VKF8zBlGfqUKN4Xl76ZhWvTl7Ned2EJCKpSIF+C+UODWZE5+p0q1ecLxZsp92wRRw4cdbXZYmIn1Cg32JBgQG80rwc/f5UmRU7j/DAJzGsjD/i67JExA8o0H2kdZXb+eap2gSY8fDgBUyIi/d1SSKSwSnQfSjq9lx816su0UXz8JfxK3h18mrOJahfXURujALdx/KGBfNFlxp0r1+CLxZs5/FPF7LvuMari0jKKdDTgaDAAP6vWVk+eawKa3Yfo8XHMcRt10MzRCRlFOjpyAOVbmPi07XJFhzIo0MXMmrBNs0DIyLJpkBPZ8oUzMmUp+tS985w/jF5DX8Zv5Iz5y/4uiwRyQAU6OlQrtAsfPZEdXo3Lsm3y+J5aKAeRi0i16dAT6cCAozn7y3F509UJ/7wKVp8MpeZ6/f5uiwRSccU6Onc3WXyM7VXPW7PE0rnEUv4cNoGLiSqX11E/kiBngEUyRfKxJ61aVM1ko9/2UjnEUs4fPKcr8sSkXRGgZ5BhGQJ5P22FfnXgxVYuPkgLT6JYcVOTRkgIv+jQM9AzIzHaxZhfI+7AGg7eAGjF27X0EYRARToGVKlwrmZ2qsute/Mx98nrabPuBWcOpfg67JExMcU6BlUnrBgPn+iOn3uLcWk5btoPWAem/ad8HVZIuJDCvQMLCDAeLZxSUZ1qcnBE+do1T+GKSt2+7osEfERBbofqFsynO+frUfZQjl5dswy/jFpNWcTdHepSGajQPcTBXOFMKZ7LbrXL8Gohdt5eNACdh7S3aUimYkC3Y9k8c7aOLRDNbYfPEmzj+fy05o9vi5LRG4RBbofuq98Qb5/th7Fw8P486g43pq6Vg/OEMkEkh3oZhZoZsvMbGoSy/qY2VozW2lmv5hZ0dQtU1KqcN5Qxve4i061i/FZzFYeGbKA+MPqghHxZylpofcG1l1l2TIg2jlXEZgAvHuzhcnNyxoUyOstyzOwXVU27ztBs4/m8rO6YET8VrIC3cwigebAsKSWO+dmOucuNv8WApGpU56khmYVCjH12boUyRdK91FxvPmdumBE/FFyW+j9gBeB5KRAV+CHpBaYWXczizWz2P379ydz15IaiuYL45unavPEXUX5fN5W2g6er1EwIn7muoFuZi2Afc65uGSs2x6IBt5LarlzbqhzLto5Fx0REZHiYuXmZA0K5I1WUQxuX5UtBzyjYP676jdflyUiqSQ5LfQ6QEsz2waMBRqZ2egrVzKze4BXgJbOubOpWqWkqiZRhfjvs/UoEZGdnl8u5e+TVukxdyJ+4LqB7px72TkX6ZwrBjwKzHDOtb98HTOrAgzBE+Z6rE4GUDhvKOP/fBfd65dg9MIdmgtGxA/c8Dh0M3vTzFp6X74HZAfGm9lyM5uSKtVJmgoO8tyINLxzdfYdP8sDn8QwLnanpuMVyaDMV//zRkdHu9jYWJ/sW/5o77EzPP/1cuZvPkiryrfxz9ZR5AjJ4uuyROQKZhbnnItOapnuFBUACuQMYVTXmvzlvlJMXfmbnogkkgEp0OWSwADjmUYl+bp7LRIuONoMms+Q2ZtJ1EOpRTIEBbr8QXSxvPz32XrcW64A//5hPU8MX8y+Y2d8XZaIXIcCXZKUKzQLA9tV5d8PVWDJtkM0/WguM9bv9XVZInINCnS5KjPjsRpF+O6ZukTkyEqXEbG8PmWNxqyLpFMKdLmukgVyMOnpOnSqXYwR87fResA8Nu497uuyROQKCnRJlpAsnpkbP+8Uzf7jZ2nxSQyjFmzTmHWRdESBLinSqEwBfniuHrVK5OMfk9fw5MhYDpzQTA8i6YECXVIsf44QhneqzqstyjF30wGa9JvLrF8144OIrynQ5YYEBBhd6hZnyjN1yBcWTKfhS3TBVMTHFOhyU8oUzMnkZ/53wbRl/xjW7j7m67JEMiUFuty0ixdMR3apweFT52k9YB6fztmiO0xFbjEFuqSaBqUi+Om5+jQsHcHb/11Hu2GL2H3ktK/LEsk0FOiSqvKGBTOkQzXebVORlfFHaNJvDpOX7/J1WSKZggJdUp2Z8Uj1wvy3dz3uzJ+d3mOX02vMMo6eOu/r0kT8mgJd0kzRfGGM+/Nd/OW+Uvyw6jfu7zeHuRv1cHCRtKJAlzQVFBjAM41KMrFnHcKyBtLhs8W8PmUNp89peKNIalOgyy1RITIX3z9bj851PMMbm38yVw/QEEllCnS5ZUKyBPLaA+X58smanD53gYcGzefDaRs4fyHR16WJ+AUFutxyde4M58fn6tOq8m18/MtGHhw4jw2avVHkpinQxSdyZcvCh49UZnD7quw+coYWn8QwdM5mLuhmJJEbpkAXn2oSVYifnqtPg1IR/Ou/63l06AK2Hzzp67JEMiQFuvhcRI6sDO1QjQ/aVmL9b8dp+tFcRi3YpqkDRFJIgS7pgpnRplokPz1fn2pF8/CPyWvo+PlidmnqAJFkU6BLunJb7mx80aUGbz8YxdIdh7m/7xy+XrJDT0YSSYZkB7qZBZrZMjObmsSyrGb2tZltMrNFZlYsNYuUzMXMaFezKD89V5+o23Py0jer6DR8Cb8dVWtd5FpS0kLvDay7yrKuwGHn3J1AX+Cdmy1MpHDeUL56shZvtCzP4q2HuK/vHMbF7lRrXeQqkhXoZhYJNAeGXWWVVsBI79cTgMZmZjdfnmR2AQHGE7WL8UPvepQtmJMXJ6yky4gl7Dl6xteliaQ7yW2h9wNeBK52S9/twE4A51wCcBTId+VKZtbdzGLNLHb/fk3SJMlXLDyMsd1r8doD5Viw5SD39p3NeLXWRX7nuoFuZi2Afc65uJvdmXNuqHMu2jkXHRERcbObk0wmIMDoXKc4P/auT9mCOfnrhJV0HrFED9EQ8UpOC70O0NLMtgFjgUZmNvqKdXYBhQHMLAjIBRxMxTpFLrnYWn+jZXkWbTnE/X3nMGaxRsKIXDfQnXMvO+cinXPFgEeBGc659lesNgV4wvv1w9519H+XpJmLfes/PVef8rfn5OVvV9H+s0XsPHTK16WJ+MwNj0M3szfNrKX35WdAPjPbBPQB/pYaxYlcT5F8npEwbz8YxYqdR7m/3xxGztddppI5ma8a0tHR0S42NtYn+xb/tOvIaV7+dhVzNuynerE8vNOmIiUisvu6LJFUZWZxzrnopJbpTlHxG7fnzsbIztV5v20lft1znCYfzWXQrM0kaL51ySQU6OJXzIyHq0UyvU8D7i4dwTs/rqf1wHms3X3M16WJpDkFuvil/DlDGNy+GgPbVWXP0TO07B/Dez+t58x5PctU/JcCXfyWmdGsQiGm92lAq8q3M2DmZpp9PJfFWw/5ujSRNKFAF7+XOzSYDx6pxBddanAuIZFHhizglYmrOHbmvK9LE0lVCnTJNOqXiuDn5+vTtW5xxizewX0fzuHnNXt8XZZIqlGgS6YSGhzEP1qU49uedcgdmoXuo+J4anQc+45psi/J+BTokilVLpyb73rV5a/3l+aX9fto/OFsvlq0QzckSYamQJdMK0tgAE/ffafnQRq35eL/Jq7iT0MXsGnfcV+XJnJDFOiS6RUPD+OrbjV5t01FNuw9QdOP5tJ32gbOJmiIo2QsCnQRPEMcH6lemF9eaEDzCoX46JeNNP1oLgs2a9JQyTgU6CKXCc+elX6PVmFklxqcv5DIY58u5K/jV3D45DlflyZyXQp0kSQ0KBXBz8814KmGdzBx2S4afzibb+LiNee6pGsKdJGryBYcyEtNyjD12boUyxfKC+NX8Pini9i8/4SvSxNJkgJd5DrKFMzJhB61efvBKNbsPkrTfnP5cNoGzQsj6Y4CXSQZAgKMdjWL8ssLDWlWoSAf/7KRJv3mMHejHnYu6YcCXSQFInJ4Lpp++WRNzIwOny2m15hlutNU0gUFusgNqHNnOD/0rsfz95TipzV7aPzBbEbM28oF3WkqPqRAF7lBIVkC6X1PSX56rj6Vi+Tm9e/W0mpADMt3HvF1aZJJKdBFblLx8DC+6FKD/o9XYd+xszw4cB6vTFzFkVMauy63lgJdJBWYGS0q3sYvLzSgc+3ijF2yk0YfzGZc7E5N+CW3jAJdJBXlCMnCqw+U47tn6lI8PIwXJ6yk7ZAFrNl91NelSSagQBdJA+Vuy8n4P9/Fuw9XZOuBkzzwSQyvT1mjpyRJmlKgi6SRgADjkejCzHyhIe1qFmXkgm00el9TCEjaUaCLpLFcoVl4q3UUU56uS2SebLwwfgVtBy9g7e5jvi5N/Mx1A93MQsxssZmtMLM1ZvZGEusUMbOZZrbMzFaaWbO0KVck46oQmYtvn6rNu20qsuXASVp8MpfXJq/m6Cl1w0jqSE4L/SzQyDlXCagMNDGzWles83dgnHOuCvAoMDB1yxTxDwEBnnnXZ7zQgHY1izJq4XYafTCLcUs0GkZu3nUD3XlcnF4ui/fflT95Dsjp/ToXsDvVKhTxQ7lDg3mrdRTf9fKOhvlmJQ8Omq+bkuSmWHIuzphZIBAH3AkMcM69dMXyQsDPQB4gDLjHOReXxHa6A90BihQpUm379u03fQAiGZ1zjknLd/Gv/65n//GzPBIdyYtNyhCePauvS5N0yMzinHPRSS5LydV2M8sNTAR6OedWX/Z+H++2PjCzu4DPgCjnXOLVthUdHe1iY2OTvW8Rf3f8zHk+mbGJz2O2ki04kOfuKUXHu4qSJVBjF+R/rhXoKfpJcc4dAWYCTa5Y1BUY511nARAChKe8VJHMK0dIFv6vWVl+fK4+VYrk4a2pa2n20VxiNh7wdWmSQSRnlEuEt2WOmWUD7gXWX7HaDqCxd52yeAJdE0WL3IA782dnZOfqfNoxmrMJibT/bBHdv4hlx8FTvi5N0rnktNALATPNbCWwBJjmnJtqZm+aWUvvOi8A3cxsBTAG6OR054TIDTMz7i1XgJ+fr89f7y9NzKYD3NN3Nu/9tJ6TZxN8XZ6kUynqQ09N6kMXSb49R8/wzo/rmbhsF/lzZOVvTcvQuvLtBASYr0uTWyzV+tBFxDcK5gqh758q881TtSmUK4Q+41bw0KD5LNtx2NelSTqiQBfJQKoVzcPEnnV47+GK7DpymgcHzqfP18vZc1SPwBMFukiGExBgtI0uzMy/NKRnwzuYuvI37n5/Fp/8spEz5y/4ujzxIQW6SAaVPWsQLzYpw/Q+DWhYOoIPpm2g0fuzmLx8l2ZzzKQU6CIZXJF8oQxqX42x3WuROzSY3mOX89Cg+SxV/3qmo0AX8RO1SuTju151ebdNReIPn+ahgfPpPXYZu46c9nVpcoso0EX8SKB3NseZf2nI03ffwQ+r99Do/Vl88POvGr+eCSjQRfxQ9qxB/PX+Msx4oQFNogryyYxNNHx/Fl8v2cEFTdPrtxToIn4sMk8oHz1ahW971qZI3lBe+mYVzT+ey9yNmpnDHynQRTKBqkXyMKHHXQx4vConzyXQ4bPFdBq+mA17j/u6NElFCnSRTMLMaF6xENP7NOCVZmVZuv0wTfrN4eVvV7HvuG5M8geay0Ukkzp88hwfz9jIqAXbCQ4KoEeDO3iyXnFCg4N8XZpcg+ZyEZE/yBMWzGsPlGdanwY0KBXBh9M2cLcunGZoCnSRTK54eBiD2ldjQo+7uC13Nl76ZhVNP5rDzPX7dMdpBqNAFxEAoovl5dunajOwXVXOJiTSecQS2g1bxKr4o74uTZJJgS4il5gZzSoUYtrzDXj9gXKs33OcB/rH8OyYZew8pCcmpXe6KCoiV3X8zHmGzN7CsJgtXEh0tK9VlF6NSpI3LNjXpWVa17ooqkAXkevac/QM/aZvYFzsTsKCg+jR8A461ymmETE+oEAXkVSxce9x3vnxV6av20v+HFl57p5SPBIdSVCgem9vFQ1bFJFUUbJADoY9Ec34HndROG8o/zdxFff1m8MPq37TiJh0QIEuIilWvVheJvS4i6EdqhFoxlNfLtvPecwAAA2BSURBVKX1gHnM33zA16Vlagp0EbkhZsZ95Qvy43P1effhiuw7fpbHP11Eh88WsXqXhjr6gvrQRSRVnDl/gVELtjNg1iaOnDpPi4qFeOG+0hQPD/N1aX5FF0VF5JY5duY8Q2dv4bOYrZy7kMgj0ZH0blyKgrlCfF2aX1Cgi8gtt//4WQbM3MSXi7YTYMYTtYvxVIM7yKMx7Dflpka5mFmImS02sxVmtsbM3rjKeo+Y2VrvOl/dbNEikrFF5MjK6y3LM+OFhjSvWIhP526h3rsz+Wj6Rk7ocXhp4rotdDMzIMw5d8LMsgAxQG/n3MLL1ikJjAMaOecOm1l+59y+a21XLXSRzGXD3uN88POv/LRmL3nDgunZ8A7a1ypKSJZAX5eWodxUC915nPC+zOL9d+VvgW7AAOfcYe9nrhnmIpL5lCqQgyEdopn8dB3K35aTf36/jobvzWL0wu2cS0j0dXl+IVnDFs0s0MyWA/uAac65RVesUgooZWbzzGyhmTW5yna6m1msmcXu369nGopkRpUK52ZU15qM6VaL2/Nk4++TVtP4w1l8ExevedhvUoouippZbmAi0Ms5t/qy96cC54FHgEhgDlDBOXfkattSl4uIOOeY9et+3v/5V9bsPsad+bPz/D2laBpVkIAA83V56VKq3frvDeiZwJUt8HhginPuvHNuK7ABKHkjxYpI5mFm3F0mP989U5dB7apiwNNfLaX5JzFMW7tX0wmkUHJGuUR4W+aYWTbgXmD9FatNAhp61wnH0wWzJVUrFRG/FRBgNK1QiB+fq0/fP1Xi9LkEun0RS+sB85j1q56clFzJaaEXAmaa2UpgCZ4+9Klm9qaZtfSu8xNw0MzW4mnB/9U5dzBtShYRfxUYYDxYJZLpfRrwbpuKHDhxjk7Dl9B28ALmb9I8MdejG4tEJN06l5DI17E7GTBjE3uOnaFWibz0ubc0NYrn9XVpPqM7RUUkQztz/gJjFu9gwMzNHDhxlrp3hvP8vSWpVjTzBbsCXUT8wulzF/hy0XYGzdrMwZPnqF8qgufuKUnVInl8Xdoto0AXEb9y6lwCXyzYztA5Wzh08hwNvMFeJRMEuwJdRPzSybMJjFywjU/nbOHwqfM0LB3Bc/eUonLh3L4uLc0o0EXEr504m8AXC34f7L0b+2eLXYEuIpnCibMJjJy/jWFzPcHeoFQEvf2sj12BLiKZyomzCYxasJ2hczZz+NR56pUMp3fjkkQXy/ijYhToIpIpnTybwOiFnounB0+eo86d+ejVqCS1SuTzdWk3TIEuIpnaqXMJfLVoB4Nnb+HAibPUKJ6X3o1LUvuOfHge+ZBxKNBFRPjfDUqDZ29m77GzVC2Sm16NS9KwVESGCXYFuojIZc6cv8D4uHgGz9rMriOnqRiZi2fuvpN7yhZI99P2KtBFRJJwLiGRicviGThrM9sPnqJMwRw8ffedNKtQiMB0GuwKdBGRa0i4kMjUlb/Rf+YmNu07QYnwMHrefSetKt9GlsAUPTYizSnQRUSSITHR8eOaPfSfsYm1vx0jMk82/tzgDtpWi0w3D7NWoIuIpIBzjhnr99F/5iaW7ThCRI6sdKtXnMdrFiV71iCf1qZAFxG5Ac45Fmw+SP+Zm5i/+SC5smWhc51idKpdjNyhwT6pSYEuInKTlu44zMCZm5m+bi9hwYG0q1WUJ+sWJ3/OkFtahwJdRCSVrN9zjMGzNjNlxW6CAgJoUy2SHg1KUDRf2C3ZvwJdRCSV7Th4iiFzNjM+Np6ExESaV7yNpxrcQbnbcqbpfhXoIiJpZN+xM3wWs5XRC7dz8twFGpaO4KkGd1CjeN40uftUgS4iksaOnjrPqIXbGD5vGwdPnqNqkdz0aHBHqt99qkAXEblFzpy/wPjYnQyZs4X4w6e5M392/ly/BK0q305w0M3fpKRAFxG5xRIuJPL9qt8YNGsz6/ccp2DOELrWLc5jNYvc1Fh2BbqIiI8455i9YT9DZm9hwZaD5AgJ4p+to2hV+fYb2t61At23tzyJiPg5M6Nh6fw0LJ2f5TuPMGT2ZorkDU2TfV23Q8fMQsxssZmtMLM1ZvbGNdZtY2bOzJL87SEikplVLpybQe2rpdnDq5PTQj8LNHLOnTCzLECMmf3gnFt4+UpmlgPoDSxKgzpFROQ6rttCdx4nvC+zeP8l1fH+FvAOcCb1yhMRkeRK1hgaMws0s+XAPmCac27RFcurAoWdc9+nQY0iIpIMyQp059wF51xlIBKoYWZRF5eZWQDwIfDC9bZjZt3NLNbMYvfv33+jNYuISBJSNMrdOXcEmAk0ueztHEAUMMvMtgG1gClJXRh1zg11zkU756IjIiJuvGoREfmD5IxyiTCz3N6vswH3AusvLnfOHXXOhTvnijnnigELgZbOOQ0yFxG5hZLTQi8EzDSzlcASPH3oU83sTTNrmbbliYhIcl132KJzbiVQJYn3X73K+g1vviwREUkpn936b2b7ge0p+Eg4cCCNyknPMuNxZ8Zjhsx53JnxmOHmjruocy7Ji5A+C/SUMrPYq81f4M8y43FnxmOGzHncmfGYIe2O++bnchQRkXRBgS4i4icyUqAP9XUBPpIZjzszHjNkzuPOjMcMaXTcGaYPXUREri0jtdBFROQaFOgiIn4iQwS6mTUxs1/NbJOZ/c3X9aQFMytsZjPNbK33QSK9ve/nNbNpZrbR+9+0mRnfh7yzeS4zs6ne18XNbJH3fH9tZsG+rjG1mVluM5tgZuvNbJ2Z3ZVJzvXz3p/v1WY2xvsAHb8632b2uZntM7PVl72X5Lk1j4+9x77SO3PtDUv3gW5mgcAAoClQDnjMzMr5tqo0kQC84Jwrh2eCs6e9x/k34BfnXEngF+9rf9MbWHfZ63eAvs65O4HDQFefVJW2PgJ+dM6VASrhOX6/PtdmdjvwLBDtnIsCAoFH8b/zPYLfT2AIVz+3TYGS3n/dgUE3s+N0H+hADWCTc26Lc+4cMBZo5eOaUp1z7jfn3FLv18fx/A9+O55jHeldbSTQ2jcVpg0ziwSaA8O8rw1oBEzwruKPx5wLqA98BuCcO+edydSvz7VXEJDNzIKAUOA3/Ox8O+fmAIeuePtq57YV8IX3QUILgdxmVuhG950RAv12YOdlr+O97/ktMyuGZ/6cRUAB59xv3kV7gAI+Kiut9ANeBBK9r/MBR5xzCd7X/ni+iwP7geHerqZhZhaGn59r59wu4H1gB54gPwrE4f/nG65+blM13zJCoGcqZpYd+AZ4zjl37PJlzjPG1G/GmZpZC2Cfcy7O17XcYkFAVWCQc64KcJIrulf87VwDePuNW+H5hXYbEMYfuyb8Xlqe24wQ6LuAwpe9jvS+53e8D+H+BvjSOfet9+29F/8E8/53n6/qSwN1gJbeB6OMxfOn90d4/uy8OBOoP57veCD+skc5TsAT8P58rgHuAbY65/Y7584D3+L5GfD38w1XP7epmm8ZIdCXACW9V8KD8VxEmeLjmlKdt+/4M2Cdc+7DyxZNAZ7wfv0EMPlW15ZWnHMvO+civQ9GeRSY4Zxrh+epWA97V/OrYwZwzu0BdppZae9bjYG1+PG59toB1DKzUO/P+8Xj9uvz7XW1czsF6Ogd7VILOHpZ10zKOefS/T+gGbAB2Ay84ut60ugY6+L5M2wlsNz7rxmePuVfgI3AdCCvr2tNo+NvCEz1fl0CWAxsAsYDWX1dXxocb2Ug1nu+JwF5MsO5Bt7A88Sz1cAoIKu/nW9gDJ5rBOfx/DXW9WrnFjA8o/g2A6vwjAC64X3r1n8RET+REbpcREQkGRToIiJ+QoEuIuInFOgiIn5CgS4i4icU6JKheWct7On9+jYzm3C9z9zEviqbWbO02r7IzVKgS0aXG+gJ4Jzb7Zx7+Drr34zKeO4NEEmXNA5dMjQzuzj75q94btoo65yLMrNOeGa0C8MzNen7QDDQATgLNHPOHTKzO/Dc2BEBnAK6OefWm1lb4DXgAp5JpO7Bc+NLNjy3Zv8bmAp8AkQBWYDXnXOTvft+EMiFZ6Kl0c65N9L4WyFC0PVXEUnX/gZEOecqe2epnHrZsig8s1aG4Anjl5xzVcysL9ARz0yPQ4EezrmNZlYTGIhnTplXgfudc7vMLLdz7pyZvYrnTr5nAMzsX3imK+hiZrmBxWY23bvvGt79nwKWmNn3zrnYtPxGiCjQxZ/NdJ655Y+b2VHgO+/7q4CK3pktawPjPVOLAJ5b0QHmASPMbByeSaSSch+eycX+4n0dAhTxfj3NOXcQwMy+xTO1gwJd0pQCXfzZ2cu+TrzsdSKen/0APHNxV77yg865Ht4We3MgzsyqJbF9A9o453793Zuez13Zl6m+TUlzuigqGd1xIMeNfNB55pvf6u0vv/h8x0rer+9wzi1yzr2K52EUhZPY109AL+/MgZhZlcuW3et9jmQ2PH35826kRpGUUKBLhubt1pjnfSDvezewiXZAVzNbAazhf483fM/MVnm3Ox9YgWea13JmttzM/gS8hedi6EozW+N9fdFiPHPbrwS+Uf+53Aoa5SKSyryjXC5dPBW5VdRCFxHxE2qhi4j4CbXQRUT8hAJdRMRPKNBFRPyEAl1ExE8o0EVE/MT/A+nG2iEc76ojAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"df.plot('timestep',['fractionOfSupplyInPool'])"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7ff2d3048cd0>"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"df.plot('timestep',['percentageOfActiveProposals','percentageOfCompletedProposals','percentageOfKilledProposals'])"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7ff2d2c6e650>"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"df.plot('timestep',['percentageOfActiveFundsRequested','percentageOfCompletedFundsRequested','percentageOfKilledFundsRequested'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Conclusion\n",
"\n",
"We have created a conviction voting model that closely adheres to the 1Hive implementation. This notebook describes the use case, how the model works, and provides descriptions of how it fits together."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}