{ "cells": [ { "cell_type": "markdown", "metadata": {}, "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" ] }, { "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.\n", "\n", "* https://www.desmos.com/calculator/x9uc6w72lm\n", "* https://www.desmos.com/calculator/0lmtia9jql\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.\n", "\n", "### The Trigger Function\n", "\n", "https://www.desmos.com/calculator/yxklrjs5m3\n", "\n", "Below we show a sweep of the trigger function threshold, considering the share of funds requested (on the x-axis) with the alpha parameter and effective supply (y-axis):" ] }, { "cell_type": "code", "execution_count": 121, "metadata": {}, "outputs": [ { "output_type": "display_data", "data": { "text/plain": "", "application/javascript": "IPython.OutputArea.prototype._should_scroll = function(lines) {\n return false;\n}\n" }, "metadata": {} } ], "source": [ "%%javascript\n", "IPython.OutputArea.prototype._should_scroll = function(lines) {\n", " return false;\n", "}" ] }, { "cell_type": "code", "execution_count": 122, "metadata": {}, "outputs": [], "source": [ "import warnings\n", "warnings.filterwarnings(\"ignore\")\n", "\n", "from cadCAD.configuration.utils import config_sim\n", "from model.parts.utils import *\n", "from model.parts.sys_params import * \n", "\n", "sim_config = config_sim({\n", " 'N': 1,\n", " 'T': range(100), #day \n", " 'M': params,\n", "})" ] }, { "cell_type": "code", "execution_count": 123, "metadata": { "tags": [] }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": "for reference: max conviction = 5.25318713934522in log10 units\n" } ], "source": [ "supply = initial_values['supply']\n", "alpha = sim_config[0]['M']['alpha']\n", "\n", "mcv = supply/(1-alpha)\n", "print('for reference: max conviction = '+str(np.log10(mcv))+'in log10 units')" ] }, { "cell_type": "code", "execution_count": 124, "metadata": {}, "outputs": [], "source": [ "supply_sweep = trigger_sweep('effective_supply',trigger_threshold, sim_config[0]['M'], supply)\n", "alpha_sweep = trigger_sweep('alpha',trigger_threshold, sim_config[0]['M'], supply)" ] }, { "cell_type": "code", "execution_count": 125, "metadata": { "scrolled": false, "tags": [] }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": "
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\n" }, "metadata": { "needs_background": "light" } } ], "source": [ "trigger_grid(supply_sweep, alpha_sweep)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These plots demonstrate the increasing conviction required to pass a proposal when either % of funds requested, effective supply, or alpha parameter is high. Blue areas represent lower required conviction, and red areas represent impossibly high conviction. This ensures that the reserve pool of funds are not depleted by a small number of large proposals.\n", "\n", "\n", "### 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 = $E$\n", "#### Funding Pool = $F$\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", "\n", "Total supply $S$ can be made a param and the state supply should be only $E$, effective supply." ] }, { "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. Metrics can often be thought of as KPIs, or Key Performance Indicators. \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", "\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", "## 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), *conviction* Tthe 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*, and *total_supply*.\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." ] }, { "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 additiona 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 day. 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": [ "## 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." ] }, { "cell_type": "code", "execution_count": 126, "metadata": {}, "outputs": [ { "output_type": "error", "ename": "ImportError", "evalue": "cannot import name 'Experiment' from 'cadCAD.configuration' (C:\\Users\\jeffe\\AppData\\Local\\Programs\\Python\\Python38-32\\lib\\site-packages\\cadCAD\\configuration\\__init__.py)", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mImportError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mmodel\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mconfig\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32mc:\\Users\\jeffe\\Aragon_Conviction_Voting\\models\\v3\\model\\config.py\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mtyping\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mDict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mList\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mcadCAD\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconfiguration\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mExperiment\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 8\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mcadCAD\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconfiguration\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mutils\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mbound_norm_random\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mep_time_step\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconfig_sim\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maccess_block\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 9\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mcopy\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mdeepcopy\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mImportError\u001b[0m: cannot import name 'Experiment' from 'cadCAD.configuration' (C:\\Users\\jeffe\\AppData\\Local\\Programs\\Python\\Python38-32\\lib\\site-packages\\cadCAD\\configuration\\__init__.py)" ] } ], "source": [ "from model import config" ] }, { "cell_type": "code", "execution_count": 127, "metadata": {}, "outputs": [ { "output_type": "error", "ename": "NameError", "evalue": "name 'config' is not defined", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m# pull out configurations to illustrate\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0msim_config\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mstate_variables\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mseeds\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mpartial_state_update_blocks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mconfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_configs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mNameError\u001b[0m: name 'config' is not defined" ] } ], "source": [ "# pull out configurations to illustrate\n", "sim_config,state_variables,seeds,partial_state_update_blocks = config.get_configs()" ] }, { "cell_type": "code", "execution_count": 128, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "[{'N': 1,\n 'T': range(0, 100),\n 'M': {'beta': 0.2,\n 'rho': 0.0025,\n 'alpha': 0.875,\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}}]" }, "metadata": {}, "execution_count": 128 } ], "source": [ "sim_config" ] }, { "cell_type": "code", "execution_count": 129, "metadata": {}, "outputs": [ { "output_type": "error", "ename": "NameError", "evalue": "name 'partial_state_update_blocks' is not defined", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mpartial_state_update_blocks\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mNameError\u001b[0m: name 'partial_state_update_blocks' is not defined" ] } ], "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": 130, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "{'initial_sentiment': 0.6,\n 'n': 30,\n 'm': 7,\n 'initial_funds': 4867.21,\n 'supply': 22392.22}" }, "metadata": {}, "execution_count": 130 } ], "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": 131, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "{'beta': 0.2,\n 'rho': 0.0025,\n 'alpha': 0.875,\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}" }, "metadata": {}, "execution_count": 131 } ], "source": [ "sim_config[0]['M']" ] }, { "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", "* $\\alpha$ : 0.875 The decay rate for previously accumulated conviction\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, \n", "* base_failure_rate': [180],\n", "* base_engagement_rate' :[0.3],\n", "* lowest_affinity_to_support': [0.3]," ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Initialization\n", "Now we will initialize our model's state variables. " ] }, { "cell_type": "code", "execution_count": 132, "metadata": {}, "outputs": [], "source": [ "# initialize \n", "network = initialize_network(initial_values['n'],initial_values['m'],\n", " initial_values['initial_funds'],\n", " initial_values['supply'],sim_config[0]['M'])\n", "initial_funds = initial_values['initial_funds']\n", "\n", "genesis_states = { \n", " 'network': network,\n", " 'funds':initial_values['initial_funds'],\n", " 'sentiment': initial_values['initial_sentiment'],\n", " 'effective_supply': initial_values['supply']-initial_values['initial_funds'],\n", " 'total_supply': initial_values['supply']\n", "\n", "}\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": 133, "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(network, 'proposal')\n", "participants = get_nodes_by_type(network, 'participant')\n", "supporters = get_edges_by_type(network, 'support')\n", "influencers = get_edges_by_type(network, 'influence')\n", "competitors = get_edges_by_type(network, 'conflict')" ] }, { "cell_type": "code", "execution_count": 134, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "{'type': 'participant',\n 'holdings': 590.5639035629654,\n 'sentiment': 0.7844670796305386}" }, "metadata": {}, "execution_count": 134 } ], "source": [ "#sample a participant\n", "network.nodes[participants[0]]" ] }, { "cell_type": "code", "execution_count": 135, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "Text(0, 0.5, 'Count of Participants')" }, "metadata": {}, "execution_count": 135 }, { "output_type": "display_data", "data": { "text/plain": "
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\n" }, "metadata": { "needs_background": "light" } } ], "source": [ "# Let's look at the distribution of participant holdings at the start of the sim\n", "plt.hist([ 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": 136, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "Text(0.5, 1.0, 'Participants Social Network')" }, "metadata": {}, "execution_count": 136 }, { "output_type": "display_data", "data": { "text/plain": "
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\n" }, "metadata": {} } ], "source": [ "nx.draw_spring(network, nodelist = participants, edgelist=influencers)\n", "plt.title('Participants Social Network')" ] }, { "cell_type": "code", "execution_count": 137, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "{'type': 'proposal',\n 'conviction': 0,\n 'status': 'candidate',\n 'age': 0,\n 'funds_requested': 2169.827526317872,\n 'trigger': inf}" }, "metadata": {}, "execution_count": 137 } ], "source": [ "#lets look at proposals\n", "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": 138, "metadata": {}, "outputs": [], "source": [ "funds_array = np.array([ network.nodes[i]['funds_requested'] for i in proposals])\n", "conviction_required = np.array([trigger_threshold(r, initial_funds, supply, alpha,sim_config[0]['M']) for r in funds_array])" ] }, { "cell_type": "code", "execution_count": 139, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "Text(0, 0.5, 'Amount of Honey requested(as a Fraction of Funds available)')" }, "metadata": {}, "execution_count": 139 }, { "output_type": "display_data", "data": { "text/plain": "
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\n" }, "metadata": { "needs_background": "light" } } ], "source": [ "plt.bar( proposals, funds_array/initial_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": 140, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "Text(0, 0.5, 'Amount of Conviction')" }, "metadata": {}, "execution_count": 140 }, { "output_type": "display_data", "data": { "text/plain": "
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\n" }, "metadata": { "needs_background": "light" } } ], "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": 141, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "Text(0.5, 55.73999999999998, 'Participant_id')" }, "metadata": {}, "execution_count": 141 }, { "output_type": "display_data", "data": { "text/plain": "
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\n" }, "metadata": { "needs_background": "light" } } ], "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] = 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": 142, "metadata": {}, "outputs": [ { "output_type": "error", "ename": "ImportError", "evalue": "cannot import name 'Experiment' from 'cadCAD.configuration' (C:\\Users\\jeffe\\AppData\\Local\\Programs\\Python\\Python38-32\\lib\\site-packages\\cadCAD\\configuration\\__init__.py)", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mImportError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mcadCAD\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconfiguration\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mExperiment\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;31m# Create configuration\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mexp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mExperiment\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mImportError\u001b[0m: cannot import name 'Experiment' from 'cadCAD.configuration' (C:\\Users\\jeffe\\AppData\\Local\\Programs\\Python\\Python38-32\\lib\\site-packages\\cadCAD\\configuration\\__init__.py)" ] } ], "source": [ "from cadCAD.configuration import Experiment\n", "\n", "# Create configuration\n", "exp = Experiment()\n", "\n", "exp.append_configs(\n", " sim_configs=sim_config,\n", " initial_state=genesis_states,\n", " seeds=seeds,\n", " partial_state_update_blocks=partial_state_update_blocks\n", ")" ] }, { "cell_type": "code", "execution_count": 143, "metadata": { "tags": [] }, "outputs": [ { "output_type": "error", "ename": "ImportError", "evalue": "cannot import name 'Experiment' from 'cadCAD.configuration' (C:\\Users\\jeffe\\AppData\\Local\\Programs\\Python\\Python38-32\\lib\\site-packages\\cadCAD\\configuration\\__init__.py)", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mImportError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mutils\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[1;33m*\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mmodel\u001b[0m \u001b[1;32mimport\u001b[0m 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\u001b[1;33m.\u001b[0m\u001b[0mparts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mutils\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[1;33m*\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mmodel\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mconfig\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mcadCAD\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mExecutionMode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mExecutionContext\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mexec_mode\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mExecutionMode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mc:\\Users\\jeffe\\Aragon_Conviction_Voting\\models\\v3\\model\\config.py\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mtyping\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mDict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mList\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mcadCAD\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconfiguration\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mExperiment\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 8\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mcadCAD\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconfiguration\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mutils\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mbound_norm_random\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mep_time_step\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconfig_sim\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maccess_block\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 9\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mcopy\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mdeepcopy\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mImportError\u001b[0m: cannot import name 'Experiment' from 'cadCAD.configuration' (C:\\Users\\jeffe\\AppData\\Local\\Programs\\Python\\Python38-32\\lib\\site-packages\\cadCAD\\configuration\\__init__.py)" ] } ], "source": [ "import numpy as np\n", "import pandas as pd\n", "from model.parts.utils import *\n", "from model import run\n", "from cadCAD import configs\n", "pd.options.display.float_format = '{:.2f}'.format\n", "\n", "%matplotlib inline\n", "\n", "rdf = run.run(configs)\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": 144, "metadata": {}, "outputs": [ { "output_type": "error", "ename": "NameError", "evalue": "name 'run' is not defined", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m=\u001b[0m \u001b[0mrun\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpostprocessing\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrdf\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mNameError\u001b[0m: name 'run' is not defined" ] } ], "source": [ "df= run.postprocessing(rdf,0)" ] }, { "cell_type": "code", "execution_count": 145, "metadata": {}, "outputs": [ { "output_type": "error", "ename": "NameError", "evalue": "name 'df' is not defined", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mNameError\u001b[0m: name 'df' is not defined" ] } ], "source": [ "df.head(5)" ] }, { "cell_type": "code", "execution_count": 146, "metadata": {}, "outputs": [ { "output_type": "error", "ename": "NameError", "evalue": "name 'df' is not defined", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'timestep'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'sentiment'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mNameError\u001b[0m: name 'df' is not defined" ] } ], "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": 147, "metadata": {}, "outputs": [ { "output_type": "error", "ename": "NameError", "evalue": "name 'df' is not defined", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'timestep'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'funds'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'candidate_funds'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mNameError\u001b[0m: name 'df' is not defined" ] } ], "source": [ "df.plot('timestep',['funds', 'candidate_funds'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Funds represent the total available funds, whereas candidate funds represent total funds requested by candidate proposals." ] }, { "cell_type": "code", "execution_count": 148, "metadata": {}, "outputs": [ { "output_type": "error", "ename": "NameError", "evalue": "name 'df' is not defined", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0maffinities_plot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mNameError\u001b[0m: name 'df' is not defined" ] } ], "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": 149, "metadata": {}, "outputs": [ { "output_type": "error", "ename": "NameError", "evalue": "name 'df' is not defined", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m df.plot(x='timestep',y=['candidate_count','active_count','completed_count', 'killed_count', 'failed_count'],\n\u001b[0m\u001b[0;32m 2\u001b[0m kind='area')\n\u001b[0;32m 3\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Proposal Status'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'count of proposals'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlegend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mncol\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'upper center'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbbox_to_anchor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0.5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m-\u001b[0m\u001b[1;36m0.15\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mNameError\u001b[0m: name 'df' is not defined" ] } ], "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": 150, "metadata": {}, "outputs": [ { "output_type": "error", "ename": "NameError", "evalue": "name 'df' is not defined", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'timestep'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'candidate_funds'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'active_funds'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'completed_funds'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'killed_funds'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'failed_funds'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'area'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Proposal Status weighted by funds requested'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Funds worth of proposals'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlegend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mncol\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'upper center'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbbox_to_anchor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0.5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m-\u001b[0m\u001b[1;36m0.15\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mNameError\u001b[0m: name 'df' is not defined" ] } ], "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": 151, "metadata": {}, "outputs": [ { "output_type": "error", "ename": "NameError", "evalue": "name 'df' is not defined", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnets\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnetwork\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mNameError\u001b[0m: name 'df' is not defined" ] } ], "source": [ "nets = df.network.values" ] }, { "cell_type": "code", "execution_count": 152, "metadata": {}, "outputs": [ { "output_type": "error", "ename": "NameError", "evalue": "name 'nets' is not defined", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[0mN\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0msnap_plot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnets\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mK\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mN\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msize_scale\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;36m300\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0msavefigs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mNameError\u001b[0m: name 'nets' is not defined" ] } ], "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": 153, "metadata": {}, "outputs": [ { "output_type": "error", "ename": "NameError", "evalue": "name 'nets' is not defined", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0msnap_plot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnets\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m80\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m81\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msize_scale\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;36m300\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0msavefigs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mNameError\u001b[0m: name 'nets' is not defined" ] } ], "source": [ "snap_plot(nets[80:81], size_scale = 1/300,savefigs=True)" ] }, { "cell_type": "code", "execution_count": 154, "metadata": {}, "outputs": [ { "output_type": "error", "ename": "NameError", "evalue": "name 'df' is not defined", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'timestep'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'effective_supply'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mNameError\u001b[0m: name 'df' is not defined" ] } ], "source": [ "df.plot('timestep',['effective_supply'])" ] }, { "cell_type": "code", "execution_count": 155, "metadata": {}, "outputs": [ { "output_type": "error", "ename": "NameError", "evalue": "name 'df' is not defined", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'timestep'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'participant_count'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mNameError\u001b[0m: name 'df' is not defined" ] } ], "source": [ "df.plot('timestep',['participant_count'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As expected *effective_supply* is increasing with the arrival of new participants." ] }, { "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 }