conviction/conviction_cadCAD.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "from cadCAD.engine import ExecutionMode, ExecutionContext, Executor\n",
    "from cadCAD.configuration import Configuration\n",
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import scipy.stats as sts\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "from scipy.stats import expon, gamma\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook uses the differential games framework developed by BlockScience. It is currently in private beta, and building towards a full open source release.\n",
    "\n",
    "**Description:**\n",
    "\n",
    "cadCAD is a Python library that assists in the processes of designing, testing and validating complex systems through simulation. At its core, cadCAD is a differential games engine that supports parameter sweeping and Monte Carlo analyses and can be easily integrated with other scientific computing Python modules and data science workflows.\n",
    "\n",
    "To learn more about cadCAD, follow our [tutorial series](https://github.com/BlockScience/cadCAD-Tutorials/tree/master/01%20Tutorials)\n",
    "\n",
    "**Installing cadCAD:**\n",
    "\n",
    "cadCAD is in private beta. Tokens are issued to participants. Replace `<TOKEN>` in the installation URL below\n",
    "```bash\n",
    "pip3 install cadCAD --extra-index-url https://<TOKEN>@repo.fury.io/blockscience/\n",
    "```\n",
    "\n",
    "If you'd like to participate in the beta program, contact cadcad [at] block [dot] science.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#helper functions\n",
    "def get_nodes_by_type(g, node_type_selection):\n",
    "    return [node for node in g.nodes if g.nodes[node]['type']== node_type_selection ]\n",
    "\n",
    "def get_edges_by_type(g, edge_type_selection):\n",
    "    return [edge for edge in g.edges if g.edges[edge]['type']== edge_type_selection ]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "125020.46534074425\n"
     ]
    }
   ],
   "source": [
    "#generate an initial set of 'n' participants\n",
    "network = nx.DiGraph()\n",
    "n = 100\n",
    "for i in range(n):\n",
    "    network.add_node(i)\n",
    "    network.nodes[i]['type']=\"participant\"\n",
    "    \n",
    "    h_rv = expon.rvs(loc=0.0, scale=1000)\n",
    "    network.nodes[i]['holdings'] = h_rv\n",
    "    \n",
    "    s_rv = np.random.rand() \n",
    "    network.nodes[i]['sentiment'] = s_rv\n",
    "\n",
    "participants = get_nodes_by_type(network, 'participant')\n",
    "initial_supply = np.sum([ network.nodes[i]['holdings'] for i in participants])\n",
    "print(initial_supply)\n",
    "\n",
    "initial_funds = initial_supply\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.5493976373731984\n"
     ]
    }
   ],
   "source": [
    "#generate initial proposals\n",
    "m = 7\n",
    "for j in range(m):\n",
    "    ind = n+j\n",
    "    network.add_node(ind)\n",
    "    network.nodes[ind]['type']=\"proposal\"\n",
    "    network.nodes[ind]['conviction']=0\n",
    "    network.nodes[ind]['status']='candidate'\n",
    "    network.nodes[ind]['age']=0\n",
    "    \n",
    "    r_rv = gamma.rvs(3,loc=0.001, scale=10000)\n",
    "    network.node[ind]['funds_requested'] = r_rv\n",
    "    \n",
    "    for i in range(n):\n",
    "        network.add_edge(i, ind)\n",
    "        \n",
    "        rv = np.random.rand()\n",
    "        a_rv = 1-4*(1-rv)*rv #polarized distribution\n",
    "        network.edges[(i, ind)]['affinity'] = a_rv\n",
    "        network.edges[(i,ind)]['tokens'] = 0\n",
    "        network.edges[(i, ind)]['conviction'] = 0\n",
    "\n",
    "proposals = get_nodes_by_type(network, 'proposal')\n",
    "total_requested = np.sum([ network.nodes[i]['funds_requested'] for i in proposals])\n",
    "print(total_requested/initial_funds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'type': 'proposal',\n",
       " 'conviction': 0,\n",
       " 'status': 'candidate',\n",
       " 'age': 0,\n",
       " 'funds_requested': 27581.372211726168}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "network.nodes[get_nodes_by_type(network, 'proposal')[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'type': 'participant',\n",
       " 'holdings': 1368.7265371328936,\n",
       " 'sentiment': 0.8242453852153833}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "network.nodes[get_nodes_by_type(network, 'participant')[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Histogram of Participants Token Holdings')"
      ]
     },
     "execution_count": 7,
     "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.hist([ network.nodes[i]['holdings'] for i in participants])\n",
    "plt.title('Histogram of Participants Token Holdings')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Histogram of Proposals Funds Requested')"
      ]
     },
     "execution_count": 8,
     "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.hist([ network.nodes[i]['funds_requested'] for i in proposals])\n",
    "plt.title('Histogram of Proposals Funds Requested')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Histogram of Affinities between Participants and Proposals')"
      ]
     },
     "execution_count": 9,
     "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.hist([ network.edges[e]['affinity'] for e in network.edges])\n",
    "plt.title('Histogram of Affinities between Participants and Proposals')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Histogram of Affinities between Participants and Proposals weighted by holdings')"
      ]
     },
     "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": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist([ network.edges[e]['affinity'] for e in network.edges], weights = [network.nodes[e[0]]['holdings']for e in network.edges],alpha = 1)\n",
    "plt.title('Histogram of Affinities between Participants and Proposals weighted by holdings')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "T = 200 #iterations of graph update in our simulation\n",
    "#param for conviction accumilation\n",
    "alpha = .5 #later we should set this to be param so we can sweep it\n",
    "\n",
    "#sentiment of the outside world which drives grants\n",
    "initial_sentiment = .8\n",
    "\n",
    "#sentiment decay rate\n",
    "mu =.001 #later we should set this to be param so we can sweep it\n",
    "\n",
    "#maximum share of funds a proposal can take\n",
    "beta = .2 #later we should set this to be param so we can sweep it\n",
    "# tuning param for the trigger function\n",
    "rho = .001\n",
    "\n",
    "#minimum periods passed before a proposal can pass\n",
    "tmin = 7\n",
    "\n",
    "#how close to a participant's highest affinity proposal that\n",
    "#another proposal has to be for them to be happy about its advancement\n",
    "sensitivity = .75"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n",
    "# Settings of general simulation parameters, unrelated to the system itself\n",
    "# `T` is a range with the number of discrete units of time the simulation will run for;\n",
    "# `N` is the number of times the simulation will be run (Monte Carlo runs)\n",
    "# We'll cover the `M` key in a future article. For now, let's leave it empty\n",
    "simulation_parameters = {\n",
    "    'T': range(T),\n",
    "    'N': 1,\n",
    "    'M': {}\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "initial_conditions = {'network':network,\n",
    "                      'supply': initial_supply,\n",
    "                      'funds':initial_funds,\n",
    "                      'sentiment': initial_sentiment}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#functions for partial state update block 1\n",
    "\n",
    "def gen_new_participant(network, new_participant_holdings):\n",
    "    \n",
    "    i = len([node for node in network.nodes])\n",
    "    \n",
    "    network.add_node(i)\n",
    "    network.nodes[i]['type']=\"participant\"\n",
    "    \n",
    "    s_rv = np.random.rand() \n",
    "    network.nodes[i]['sentiment'] = s_rv\n",
    "    network.nodes[i]['holdings']=new_participant_holdings\n",
    "    \n",
    "    for j in get_nodes_by_type(network, 'proposal'):\n",
    "        network.add_edge(i, j)\n",
    "        \n",
    "        rv = np.random.rand()\n",
    "        a_rv = 1-4*(1-rv)*rv #polarized distribution\n",
    "        network.edges[(i, j)]['affinity'] = a_rv\n",
    "        network.edges[(i,j)]['tokens'] = a_rv*network.nodes[i]['holdings']\n",
    "        network.edges[(i, j)]['conviction'] = 0\n",
    "    \n",
    "    return network\n",
    "    \n",
    "\n",
    "def gen_new_proposal(network):\n",
    "    j = len([node for node in network.nodes])\n",
    "    network.add_node(j)\n",
    "    network.nodes[j]['type']=\"proposal\"\n",
    "    \n",
    "    network.nodes[j]['conviction']=0\n",
    "    network.nodes[j]['status']='candidate'\n",
    "    network.nodes[j]['age']=0\n",
    "    \n",
    "    r_rv = gamma.rvs(3,loc=0.001, scale=10000)\n",
    "    network.node[j]['funds_requested'] = r_rv\n",
    "    \n",
    "    participants = get_nodes_by_type(network, 'participant')\n",
    "    proposing_participant = np.random.choice(participants)\n",
    "    \n",
    "    for i in participants:\n",
    "        network.add_edge(i, j)\n",
    "        if i==proposing_participant:\n",
    "            network.edges[(i, j)]['affinity']=1\n",
    "        else:\n",
    "            rv = np.random.rand()\n",
    "            a_rv = 1-4*(1-rv)*rv #polarized distribution\n",
    "            network.edges[(i, j)]['affinity'] = a_rv\n",
    "            \n",
    "        network.edges[(i, j)]['conviction'] = 0\n",
    "        network.edges[(i,j)]['tokens'] = 0\n",
    "    return network\n",
    "        \n",
    "        \n",
    "\n",
    "def driving_process(params, step, sL, s):\n",
    "    \n",
    "    #placeholder plumbing for random processes\n",
    "    arrival_rate = 10/s['sentiment']\n",
    "    rv1 = np.random.rand()\n",
    "    new_participant = bool(rv1<1/arrival_rate)\n",
    "    if new_participant:\n",
    "        h_rv = expon.rvs(loc=0.0, scale=1000)\n",
    "        new_participant_holdings = h_rv\n",
    "    else:\n",
    "        new_participant_holdings = 0\n",
    "    \n",
    "    network = s['network']\n",
    "    affinities = [network.edges[e]['affinity'] for e in network.edges ]\n",
    "    median_affinity = np.median(affinities)\n",
    "    \n",
    "    proposals = get_nodes_by_type(network, 'proposal')\n",
    "    fund_requests = [network.nodes[j]['funds_requested'] for j in proposals if network.nodes[j]['status']=='candidate' ]\n",
    "    \n",
    "    funds = s['funds']\n",
    "    total_funds_requested = np.sum(fund_requests)\n",
    "    \n",
    "    proposal_rate = 10/median_affinity * total_funds_requested/funds\n",
    "    rv2 = np.random.rand()\n",
    "    new_proposal = bool(rv2<1/proposal_rate)\n",
    "    \n",
    "    sentiment = s['sentiment']\n",
    "    funds = s['funds']\n",
    "    scale_factor = 1+4000*sentiment**2\n",
    "    \n",
    "    #this shouldn't happen but expon is throwing domain errors\n",
    "    if scale_factor > 1: \n",
    "        funds_arrival = expon.rvs(loc = 0, scale = scale_factor )\n",
    "    else:\n",
    "        funds_arrival = 0\n",
    "    \n",
    "    return({'new_participant':new_participant,\n",
    "            'new_participant_holdings':new_participant_holdings,\n",
    "            'new_proposal':new_proposal, \n",
    "            'funds_arrival':funds_arrival})\n",
    "\n",
    "def update_network(params, step, sL, s, _input):\n",
    "    \n",
    "    network = s['network']\n",
    "    #placeholder plumbing for new proposals and new participants\n",
    "    new_participant = _input['new_participant'] #T/F\n",
    "    new_proposal = _input['new_proposal'] #T/F\n",
    "    # IF THEN logic to create new nodes // left out for now since always FALSE\n",
    "    if new_participant:\n",
    "        new_participant_holdings = _input['new_participant_holdings']\n",
    "        network = gen_new_participant(network, new_participant_holdings)\n",
    "    \n",
    "    if new_proposal:\n",
    "        network= gen_new_proposal(network)\n",
    "    \n",
    "    #update age of the existing proposals\n",
    "    proposals = get_nodes_by_type(network, 'proposal')\n",
    "    for j in proposals:\n",
    "         network.nodes[j]['age']=  network.nodes[j]['age']+1\n",
    "    \n",
    "    key = 'network'\n",
    "    value = network\n",
    "    \n",
    "    return (key, value)\n",
    "\n",
    "def increment_funds(params, step, sL, s, _input):\n",
    "    \n",
    "    funds = s['funds']\n",
    "    funds_arrival = _input['funds_arrival']\n",
    "\n",
    "    #increment funds\n",
    "    funds = funds + funds_arrival\n",
    "    \n",
    "    key = 'funds'\n",
    "    value = funds\n",
    "    \n",
    "    return (key, value)\n",
    "\n",
    "def increment_supply(params, step, sL, s, _input):\n",
    "    \n",
    "    supply = s['supply']\n",
    "    supply_arrival = _input['new_participant_holdings']\n",
    "\n",
    "    #increment funds\n",
    "    supply = supply + supply_arrival\n",
    "    \n",
    "    key = 'supply'\n",
    "    value = supply\n",
    "    \n",
    "    return (key, value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#partial state update block 2\n",
    "def check_progress(params, step, sL, s):\n",
    "    \n",
    "    network = s['network']\n",
    "    proposals = get_nodes_by_type(network, 'proposal')\n",
    "    \n",
    "    completed = []\n",
    "    for j in proposals:\n",
    "        if network.nodes[j]['status'] == 'active':\n",
    "            grant_size = network.nodes[j]['funds_requested']\n",
    "            likelihood = 1.0/(5+np.log(grant_size))\n",
    "            if np.random.rand() < likelihood:\n",
    "                completed.append(j)\n",
    "    \n",
    "    return({'completed':completed})\n",
    "\n",
    "def complete_proposal(params, step, sL, s, _input):\n",
    "    \n",
    "    network = s['network']\n",
    "    participants = get_nodes_by_type(network, 'participant')\n",
    "    \n",
    "    completed = _input['completed']\n",
    "    for j in completed:\n",
    "        network.nodes[j]['status']='completed'\n",
    "        for i in participants:\n",
    "            force = network.edges[(i,j)]['affinity']\n",
    "            sentiment = network.node[i]['sentiment']\n",
    "            network.node[i]['sentiment'] = get_sentimental(sentiment, force, decay=False)\n",
    "    \n",
    "    key = 'network'\n",
    "    value = network\n",
    "    \n",
    "    return (key, value)\n",
    "\n",
    "def update_sentiment_on_completion(params, step, sL, s, _input):\n",
    "    \n",
    "    network = s['network']\n",
    "    proposals = get_nodes_by_type(network, 'proposal')\n",
    "    completed = _input['completed']\n",
    "    \n",
    "    grants_outstanding = np.sum([network.nodes[j]['funds_requested'] for j in proposals if network.nodes[j]['status']=='active'])\n",
    "    \n",
    "    grants_completed = np.sum([network.nodes[j]['funds_requested'] for j in completed])\n",
    "    \n",
    "    sentiment = s['sentiment']\n",
    "    \n",
    "    force = grants_completed/grants_outstanding\n",
    "    if (force >=0) and (force <=1):\n",
    "        sentiment = get_sentimental(sentiment, force, True)\n",
    "    else:\n",
    "        sentiment = get_sentimental(sentiment, 0, True)\n",
    "    \n",
    "    \n",
    "    key = 'sentiment'\n",
    "    value = sentiment\n",
    "    \n",
    "    return (key, value)\n",
    "\n",
    "def get_sentimental(sentiment, force, decay=True):\n",
    "    sentiment = sentiment*(1-int(decay)*mu) + force\n",
    "    \n",
    "    if sentiment > 1:\n",
    "        sentiment = 1\n",
    "        \n",
    "    return sentiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def trigger_threshold(requested, funds, supply):\n",
    "    \n",
    "    share = requested/funds\n",
    "    if share < beta:\n",
    "        return rho*supply/(beta-share)**2\n",
    "    else: \n",
    "        return np.inf\n",
    "        \n",
    "    \n",
    "\n",
    "#partial state update block 3\n",
    "def trigger_function(params, step, sL, s):\n",
    "    \n",
    "    network = s['network']\n",
    "    funds = s['funds']\n",
    "    supply = s['supply']\n",
    "    proposals = get_nodes_by_type(network, 'proposal')\n",
    "    \n",
    "    accepted = []\n",
    "    for j in proposals:\n",
    "        if network.nodes[j]['status'] == 'candidate':\n",
    "            requested = network.nodes[j]['funds_requested']\n",
    "            age = network.nodes[j]['age']\n",
    "            if age > tmin:\n",
    "                threshold = trigger_threshold(requested, funds, supply)\n",
    "                conviction = network.nodes[j]['conviction']\n",
    "                if conviction >threshold:\n",
    "                    accepted.append(j)\n",
    "                    \n",
    "    return({'accepted':accepted})\n",
    "\n",
    "def decrement_funds(params, step, sL, s, _input):\n",
    "    \n",
    "    funds = s['funds']\n",
    "    network = s['network']\n",
    "    accepted = _input['accepted']\n",
    "\n",
    "    #decrement funds\n",
    "    for j in accepted:\n",
    "        funds = funds - network.nodes[j]['funds_requested']\n",
    "    \n",
    "    key = 'funds'\n",
    "    value = funds\n",
    "    \n",
    "    return (key, value)\n",
    "\n",
    "def update_proposals(params, step, sL, s, _input):\n",
    "    \n",
    "    network = s['network']\n",
    "    accepted = _input['accepted']\n",
    "    participants = get_nodes_by_type(network, 'participant')\n",
    "    \n",
    "    #bookkeeping conviction and participant sentiment\n",
    "    for j in accepted:\n",
    "        network.nodes[j]['status']='active'\n",
    "        network.nodes[j]['conviction']=np.nan\n",
    "        #change status to active\n",
    "        for i in participants:\n",
    "        \n",
    "            edge = (i,j)\n",
    "            #reset tokens assigned to other candidates\n",
    "            network.edges[(i,j)]['tokens']=0\n",
    "            network.edges[(i,j)]['conviction'] = np.nan\n",
    "            \n",
    "            #update participants sentiments (positive or negative) \n",
    "            affinities = [network.edges[(i,p)]['affinity'] for p in proposals if not(p in accepted)]\n",
    "            if len(affinities)>1:\n",
    "                max_affinity = np.max(affinities)\n",
    "                force = network.edges[(i,j)]['affinity']-sensitivity*max_affinity\n",
    "            else:\n",
    "                force = 0\n",
    "            \n",
    "            #based on what their affinities to the accepted proposals\n",
    "            network.nodes[i]['sentiment'] = get_sentimental(network.nodes[i]['sentiment'], force, False)\n",
    "            \n",
    "    \n",
    "    key = 'network'\n",
    "    value = network\n",
    "    \n",
    "    return (key, value)\n",
    "\n",
    "def update_sentiment_on_release(params, step, sL, s, _input):\n",
    "    \n",
    "    network = s['network']\n",
    "    proposals = get_nodes_by_type(network, 'proposal')\n",
    "    accepted = _input['accepted']\n",
    "    \n",
    "    proposals_outstanding = np.sum([network.nodes[j]['funds_requested'] for j in proposals if network.nodes[j]['status']=='candidate'])\n",
    "    \n",
    "    proposals_accepted = np.sum([network.nodes[j]['funds_requested'] for j in accepted])\n",
    "    \n",
    "    sentiment = s['sentiment']\n",
    "    force = proposals_accepted/proposals_outstanding\n",
    "    if (force >=0) and (force <=1):\n",
    "        sentiment = get_sentimental(sentiment, force, False)\n",
    "    else:\n",
    "        sentiment = get_sentimental(sentiment, 0, False)\n",
    "    \n",
    "    key = 'sentiment'\n",
    "    value = sentiment\n",
    "    \n",
    "    return (key, value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def participants_decisions(params, step, sL, s):\n",
    "    \n",
    "    network = s['network']\n",
    "    participants = get_nodes_by_type(network, 'participant')\n",
    "    proposals = get_nodes_by_type(network, 'proposal')\n",
    "    candidates = [j for j in proposals if network.nodes[j]['status']=='candidate']\n",
    "    \n",
    "    gain = .01\n",
    "    delta_holdings={}\n",
    "    proposals_supported ={}\n",
    "    for i in participants:\n",
    "        force = network.nodes[i]['sentiment']-sensitivity\n",
    "        delta_holdings[i] = network.nodes[i]['holdings']*gain*force\n",
    "        \n",
    "        support = []\n",
    "        for j in candidates:\n",
    "            affinity = network.edges[(i, j)]['affinity']\n",
    "            cutoff = sensitivity*np.max([network.edges[(i,p)]['affinity'] for p in candidates])\n",
    "            if affinity > cutoff:\n",
    "                support.append(j)\n",
    "        \n",
    "        proposals_supported[i] = support\n",
    "    \n",
    "    return({'delta_holdings':delta_holdings, 'proposals_supported':proposals_supported})\n",
    "\n",
    "def update_tokens(params, step, sL, s, _input):\n",
    "    \n",
    "    network = s['network']\n",
    "    delta_holdings = _input['delta_holdings']\n",
    "    proposals = get_nodes_by_type(network, 'proposal')\n",
    "    proposals_supported = _input['proposals_supported']\n",
    "    participants = get_nodes_by_type(network, 'participant')\n",
    "    \n",
    "    for i in participants:\n",
    "        network.nodes[i]['holdings'] = network.nodes[i]['holdings']+delta_holdings[i]\n",
    "        supported = proposals_supported[i]\n",
    "        total_affinity = np.sum([ network.edges[(i, j)]['affinity'] for j in supported])\n",
    "        for j in proposals:\n",
    "            if j in supported:\n",
    "                normalized_affinity = network.edges[(i, j)]['affinity']/total_affinity\n",
    "                network.edges[(i, j)]['tokens'] = normalized_affinity*network.nodes[i]['holdings']\n",
    "            else:\n",
    "                network.edges[(i, j)]['tokens'] = 0\n",
    "            \n",
    "            prior_conviction = network.edges[(i, j)]['conviction']\n",
    "            current_tokens = network.edges[(i, j)]['tokens']\n",
    "            network.edges[(i, j)]['conviction'] =current_tokens+alpha*prior_conviction\n",
    "    \n",
    "    for j in proposals:\n",
    "        network.nodes[j]['conviction'] = np.sum([ network.edges[(i, j)]['conviction'] for i in participants])\n",
    "    \n",
    "    key = 'network'\n",
    "    value = network\n",
    "    \n",
    "    return (key, value)\n",
    "\n",
    "def update_supply(params, step, sL, s, _input):\n",
    "    \n",
    "    supply = s['supply']\n",
    "    delta_holdings = _input['delta_holdings']\n",
    "    delta_supply = np.sum([v for v in delta_holdings.values()])\n",
    "    \n",
    "    supply = supply + delta_supply\n",
    "    \n",
    "    key = 'supply'\n",
    "    value = supply\n",
    "    \n",
    "    return (key, value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n",
    "# The Partial State Update Blocks\n",
    "partial_state_update_blocks = [\n",
    "    { \n",
    "        'policies': { \n",
    "            #new proposals or new participants\n",
    "            'random': driving_process\n",
    "        },\n",
    "        'variables': {\n",
    "            'network': update_network,\n",
    "            'funds':increment_funds,\n",
    "            'supply':increment_supply\n",
    "        }\n",
    "    },\n",
    "    {\n",
    "      'policies': {\n",
    "          'completion': check_progress #see if any of the funded proposals completes\n",
    "        },\n",
    "        'variables': { # The following state variables will be updated simultaneously\n",
    "            'sentiment': update_sentiment_on_completion, #note completing decays sentiment, completing bumps it\n",
    "            'network': complete_proposal #book-keeping\n",
    "        }\n",
    "    },\n",
    "        {\n",
    "      'policies': {\n",
    "          'release': trigger_function #check each proposal to see if it passes\n",
    "        },\n",
    "        'variables': { # The following state variables will be updated simultaneously\n",
    "            'funds': decrement_funds, #funds expended\n",
    "            'sentiment': update_sentiment_on_release, #releasing funds can bump sentiment\n",
    "            'network': update_proposals #reset convictions, and participants sentiments\n",
    "                                        #update based on affinities\n",
    "        }\n",
    "    },\n",
    "    { \n",
    "        'policies': { \n",
    "            'participants_act': participants_decisions, #high sentiment, high affinity =>buy\n",
    "                                                        #low sentiment, low affinities => burn\n",
    "                                                        #assign tokens to top affinities\n",
    "        },\n",
    "        'variables': {\n",
    "            'supply': update_supply,\n",
    "            'network': update_tokens #update everyones holdings \n",
    "                                    #and their conviction for each proposal\n",
    "        }\n",
    "    }\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n",
    "# The configurations above are then packaged into a `Configuration` object\n",
    "config = Configuration(initial_state=initial_conditions, #dict containing variable names and initial values\n",
    "                       partial_state_update_blocks=partial_state_update_blocks, #dict containing state update functions\n",
    "                       sim_config=simulation_parameters #dict containing simulation parameters\n",
    "                      )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "single_proc: [<cadCAD.configuration.Configuration object at 0x1a1f48e1d0>]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:47: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:47: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:93: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:80: RuntimeWarning: divide by zero encountered in double_scalars\n"
     ]
    }
   ],
   "source": [
    "exec_mode = ExecutionMode()\n",
    "exec_context = ExecutionContext(exec_mode.single_proc)\n",
    "executor = Executor(exec_context, [config]) # Pass the configuration object inside an array\n",
    "raw_result, tensor = executor.main() # The `main()` method returns a tuple; its first elements contains the raw results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(raw_result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>funds</th>\n",
       "      <th>network</th>\n",
       "      <th>run</th>\n",
       "      <th>sentiment</th>\n",
       "      <th>substep</th>\n",
       "      <th>supply</th>\n",
       "      <th>timestep</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>796</th>\n",
       "      <td>275205.632845</td>\n",
       "      <td>(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.999000</td>\n",
       "      <td>4</td>\n",
       "      <td>77794.439364</td>\n",
       "      <td>199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>797</th>\n",
       "      <td>279051.211671</td>\n",
       "      <td>(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.999000</td>\n",
       "      <td>1</td>\n",
       "      <td>77794.439364</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>798</th>\n",
       "      <td>279051.211671</td>\n",
       "      <td>(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.998001</td>\n",
       "      <td>2</td>\n",
       "      <td>77794.439364</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>799</th>\n",
       "      <td>279051.211671</td>\n",
       "      <td>(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.998001</td>\n",
       "      <td>3</td>\n",
       "      <td>77794.439364</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>800</th>\n",
       "      <td>279051.211671</td>\n",
       "      <td>(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.998001</td>\n",
       "      <td>4</td>\n",
       "      <td>77173.221287</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             funds                                            network  run  \\\n",
       "796  275205.632845  (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...    1   \n",
       "797  279051.211671  (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...    1   \n",
       "798  279051.211671  (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...    1   \n",
       "799  279051.211671  (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...    1   \n",
       "800  279051.211671  (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...    1   \n",
       "\n",
       "     sentiment  substep        supply  timestep  \n",
       "796   0.999000        4  77794.439364       199  \n",
       "797   0.999000        1  77794.439364       200  \n",
       "798   0.998001        2  77794.439364       200  \n",
       "799   0.998001        3  77794.439364       200  \n",
       "800   0.998001        4  77173.221287       200  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a227be2e8>"
      ]
     },
     "execution_count": 23,
     "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": [
    "df.supply.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a356e1c18>"
      ]
     },
     "execution_count": 24,
     "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.sentiment.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a35398198>"
      ]
     },
     "execution_count": 25,
     "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='funds')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pad(vec, length,fill=True):\n",
    "    \n",
    "    if fill:\n",
    "        padded = np.zeros(length,)\n",
    "    else:\n",
    "        padded = np.empty(length,)\n",
    "        padded[:] = np.nan\n",
    "        \n",
    "    for i in range(len(vec)):\n",
    "        padded[i]= vec[i]\n",
    "        \n",
    "    return padded\n",
    "\n",
    "def make2D(key, data, fill=False):\n",
    "    maxL = data[key].apply(len).max()\n",
    "    newkey = 'padded_'+key\n",
    "    data[newkey] = data[key].apply(lambda x: pad(x,maxL,fill))\n",
    "    reshaped = np.array([a for a in data[newkey].values])\n",
    "    \n",
    "    return reshaped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['conviction'] = df.network.apply(lambda g: [g.nodes[j]['conviction'] for j in get_nodes_by_type(g, 'proposal') ])\n",
    "df['candidate_count'] = df.network.apply(lambda g: len([j for j in get_nodes_by_type(g, 'proposal') if g.nodes[j]['status']=='candidate']))\n",
    "df['candidate_funds'] = df.network.apply(lambda g: np.sum([g.nodes[j]['funds_requested'] for j in get_nodes_by_type(g, 'proposal') if g.nodes[j]['status']=='candidate']))\n",
    "df['active_count'] = df.network.apply(lambda g: len([j for j in get_nodes_by_type(g, 'proposal') if g.nodes[j]['status']=='active']))\n",
    "df['active_funds'] = df.network.apply(lambda g: np.sum([g.nodes[j]['funds_requested'] for j in get_nodes_by_type(g, 'proposal') if g.nodes[j]['status']=='active']))\n",
    "df['completed_count'] = df.network.apply(lambda g: len([j for j in get_nodes_by_type(g, 'proposal') if g.nodes[j]['status']=='completed']))\n",
    "df['completed_funds'] = df.network.apply(lambda g: np.sum([g.nodes[j]['funds_requested'] for j in get_nodes_by_type(g, 'proposal') if g.nodes[j]['status']=='completed']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['funds_requested'] = df.network.apply(lambda g: np.array([g.nodes[j]['funds_requested'] for j in get_nodes_by_type(g, 'proposal')]))\n",
    "df['share_of_funds_requested'] = df['funds_requested']/df.funds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a35a82438>"
      ]
     },
     "execution_count": 44,
     "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'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a36188da0>"
      ]
     },
     "execution_count": 45,
     "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'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'conviction')"
      ]
     },
     "execution_count": 48,
     "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.plot(df.timestep,make2D('conviction', df))\n",
    "plt.title('conviction by proposal')\n",
    "plt.xlabel('time $t$')\n",
    "plt.ylabel('conviction')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'share_of_funds_requested')"
      ]
     },
     "execution_count": 53,
     "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.plot(df.timestep,make2D('share_of_funds_requested', df))\n",
    "plt.title('share_of_funds_requested by proposal')\n",
    "plt.xlabel('time $t$')\n",
    "plt.ylabel('share_of_funds_requested')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'share_of_funds_requested')"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.loglog(make2D('share_of_funds_requested', df), make2D('conviction', df),alpha=.5)\n",
    "plt.ylabel('conviction')\n",
    "plt.xlabel('share_of_funds_requested')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "last_net= rdf.network.values[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "completed\n",
      "completed\n",
      "completed\n",
      "completed\n",
      "completed\n",
      "completed\n",
      "completed\n",
      "completed\n",
      "completed\n",
      "completed\n",
      "completed\n",
      "completed\n",
      "completed\n",
      "completed\n",
      "completed\n",
      "completed\n",
      "completed\n",
      "completed\n",
      "completed\n",
      "completed\n",
      "active\n",
      "active\n",
      "active\n",
      "active\n",
      "candidate\n"
     ]
    }
   ],
   "source": [
    "last_props=get_nodes_by_type(last_net, 'proposal')\n",
    "\n",
    "for j in last_props:\n",
    "    print(last_net.nodes[j]['status'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "last_participants = get_nodes_by_type(last_net, 'participant')\n",
    "\n",
    "pos = {}\n",
    "last_n = len(last_participants)\n",
    "for ind in range(last_n):\n",
    "    i = last_participants[ind] \n",
    "    pos[i] = np.array([0, ind-last_n/2])\n",
    "\n",
    "last_m = len(last_props) \n",
    "for ind in range(last_m):\n",
    "    j = last_props[ind] \n",
    "    pos[j] = np.array([1, last_n/last_m *ind-last_n/2])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nx.draw(last_net, pos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['node_count']=df.network.apply(lambda g:len(g.nodes))\n",
    "df['net_growth']= df.node_count.diff()\n",
    "nets = df[df.net_growth>0].network.values"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "scrolled": false
   },
   "source": [
    "for i in range(len(nets)-1):\n",
    "    nx.draw(nets[i],pos=pos, alpha=.5, node_color='b')\n",
    "    nx.draw(nets[i+1],pos=pos,  alpha=.5, node_color='g')\n",
    "    plt.title(\"Update: $G_{\"+str(i)+\"}$ to $G_{\"+str(i+1)+\"}$\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}