diff --git a/ABM - discounted integral voting.ipynb b/ABM - discounted integral voting.ipynb
new file mode 100644
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--- /dev/null
+++ b/ABM - discounted integral voting.ipynb	
@@ -0,0 +1,650 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import random\n",
+    "\n",
+    "from scipy.stats import gamma\n",
+    "\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Discounted integral 'Voting'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Simple model proposed for implementing and testing a voting scheme\n",
+    "\n",
+    "- Assume a dynamic supply of governance tokens accessed by a bonding ETH (linear bonding curve)\n",
+    "- Assume this tokens also represent a stake in a revenue generating process\n",
+    "- The revenue generating process is has one parameter which is \"governed\" \n",
+    "- The revenue generated is random and there is a true \"best parameter\" unknown to the voters which may change\n",
+    "- The goal of the 'voting' system is for the selected parameter to trend toward the \"best parameter\" (even if it changes)\n",
+    "- In this set up, voting is completely passive, \"votes\" are automatically determined by each agents belief state and counted according to their balance of the 'Tokens' that representing their voting capacity\n",
+    "- An agent has the right to change their belief or preference at any time but the effect of their prior beliefs or prefences continues to influence the system, decaying in time according to the forgetfulness parameter\n",
+    "- These tokens also represent their stake in the pool of Ether being generated by the revenue process\n",
+    "\n",
+    "This is a sensor fusion problem -- coordination problem. The environment, the pool of agents, the process, the actions and the system updates have been made mind-numbingly noisy in order to show the effect of the di"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Define the Revenue generating process\n",
+    "def revenue(true_best_param,current_voted_param ):\n",
+    "    #use an concave function with unique maximum as true_best_param = current_voted_param\n",
+    "    base_scale = 1\n",
+    "    scale = base_scale*np.exp(-(true_best_param-current_voted_param)**2)\n",
+    "    shape = .5\n",
+    "    return gamma(scale, shape).rvs()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x112cb64a8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#test the revenue random variable as a function of how right the param is\n",
+    "N = 1000\n",
+    "bins = 10\n",
+    "d11=np.zeros(N)\n",
+    "d12=np.zeros(N)\n",
+    "d21=np.zeros(N)\n",
+    "d110=np.zeros(N)\n",
+    "d101=np.zeros(N)\n",
+    "for i in range(N):\n",
+    "    d11[i] = revenue(1,1)\n",
+    "    d12[i] = revenue(1,2)\n",
+    "    d21[i] = revenue(2,1)\n",
+    "    d110[i] = revenue(1,10)\n",
+    "    d101[i] = revenue(10,1)\n",
+    "\n",
+    "plt.hist(d11, bins, alpha=0.5, label='d11')\n",
+    "plt.hist(d12, bins, alpha=0.5, label='d12')\n",
+    "plt.hist(d21, bins, alpha=0.5, label='d21')\n",
+    "plt.hist(d110, bins, alpha=0.5, label='d110')\n",
+    "plt.hist(d101, bins, alpha=0.5, label='d101')\n",
+    "\n",
+    "plt.legend(loc='upper right')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Lets assuming that all tokens in existence is given by T\n",
+    "\n",
+    "def bond_mint(eth, Eth, Tokens):\n",
+    "    return eth*Tokens/Eth\n",
+    "\n",
+    "def burn_withdraw(tokens, Eth, Tokens):\n",
+    "    return tokens*Eth/Tokens\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#define a set of agents\n",
+    "agents = {}\n",
+    "\n",
+    "#in this passive voting model agents maintain a belief or a preference as part of their state with respect\n",
+    "#to the contract the 'token' came from\n",
+    "#their belief or preference may be updated by them at will\n",
+    "def add_agent(eth, tbparam, Eth, Tokens):\n",
+    "    agents[str(len(agents))] = {\"eth\":eth,\n",
+    "                           \"tokens\":0,\n",
+    "                           \"param_belief\": 2*tbparam*np.random.uniform(),\n",
+    "                           \"value_belief\":2*Tokens/Eth*np.random.uniform()}\n",
+    "    \n",
+    "\n",
+    "def agent_acts(a, Eth, Tokens, r, p):\n",
+    "    #update value belief\n",
+    "    agents[a][\"value_belief\"] = 2*Tokens/Eth*np.random.uniform()\n",
+    "    \n",
+    "    #update tokens held -- buy or sell -- pretty naive with random belief and random amount\n",
+    "    if agents[a][\"value_belief\"]<Tokens/Eth:\n",
+    "        urv =np.random.uniform() \n",
+    "        burn = agents[a][\"tokens\"]*urv\n",
+    "        eth_out = burn_withdraw(burn, Eth, Tokens)\n",
+    "        \n",
+    "        agents[a][\"tokens\"] = agents[a][\"tokens\"]-burn\n",
+    "        agents[a][\"eth\"] = agents[a][\"eth\"] + eth_out\n",
+    "        Tokens = Tokens - burn\n",
+    "        Eth = Eth - eth_out\n",
+    "        \n",
+    "    else:\n",
+    "        \n",
+    "        urv =np.random.uniform() \n",
+    "        bond =agents[a][\"eth\"]*urv\n",
+    "        mint = bond_mint(bond, Eth, Tokens)\n",
+    "        \n",
+    "        agents[a][\"tokens\"] = agents[a][\"tokens\"]+mint\n",
+    "        agents[a][\"eth\"] = agents[a][\"eth\"] -bond\n",
+    "        Tokens = Tokens + mint\n",
+    "        Eth = Eth + bond\n",
+    "        \n",
+    "    #update believe by taking a tiny step along slope of observations\n",
+    "    #only assumption is that the agent roughly estimates the direction of better returns from observed revenue events\n",
+    "    agents[a][\"param_belief\"] = np.max([0,agents[a][\"param_belief\"] + np.random.uniform()*np.polyfit(p, r, 1)[0]])\n",
+    "\n",
+    "    #print(agents[a][\"value_belief\"])\n",
+    "    return Eth, Tokens, agents[a]\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "K= 50000\n",
+    "alpha=.95 #forgetfulness\n",
+    "true_best_param = 5*np.ones(K) \n",
+    "#throw a step change in there to mix things up\n",
+    "#true_best_param[:int(K/2)] =.5*true_best_param[:int(K/2)]\n",
+    "\n",
+    "\n",
+    "E=np.zeros(K)\n",
+    "T=np.zeros(K)\n",
+    "\n",
+    "A=np.zeros(K)\n",
+    "\n",
+    "Eth =1\n",
+    "Tokens =100\n",
+    "E[0] = Eth\n",
+    "T[0] = Tokens\n",
+    "A[0] = 1\n",
+    "\n",
+    "agents = {}\n",
+    "add_agent(gamma(5,1).rvs(), true_best_param[0], Eth, Tokens)\n",
+    "\n",
+    "votes=np.zeros(K)\n",
+    "count=np.zeros(K)\n",
+    "param=np.zeros(K)\n",
+    "rev=np.zeros(K)\n",
+    "\n",
+    "n = np.zeros(K)\n",
+    "n[0]=1\n",
+    "\n",
+    "for k in range(1,K):\n",
+    "    Eth = E[k-1]\n",
+    "    Tokens = T[k-1]\n",
+    "    #resolve governance -- what is the belief of the param\n",
+    "    votes[k] = (np.sum([agents[a][\"tokens\"]*agents[a][\"param_belief\"] for a in list(agents.keys())])+ alpha*votes[k-1])\n",
+    "    count[k] = (np.sum([agents[a][\"tokens\"] for a in list(agents.keys())])+ alpha*count[k-1])\n",
+    "    if count[k]>0:\n",
+    "        param[k] = votes[k]/count[k]\n",
+    "    else:\n",
+    "        param[k] = 1\n",
+    "    rev[k] = revenue(true_best_param[k],param[k])\n",
+    "    Eth = Eth+rev[k]\n",
+    "    \n",
+    "    #new agents join\n",
+    "    if np.random.uniform()< np.log10(K/k):\n",
+    "        new = int(2*np.random.uniform())\n",
+    "        for i in range(new):\n",
+    "            add_agent(gamma(5,1).rvs(), true_best_param[k], Eth, Tokens)\n",
+    "    n[k]=len(agents)\n",
+    "        \n",
+    "    #pick some to update on the order of log of agents\n",
+    "    active = random.sample(list(agents.keys()), int(np.log2(len(agents))))\n",
+    "    for a in active:\n",
+    "        Eth, Tokens, val = agent_acts(a, Eth, Tokens, rev[:k+1], param[:k+1])\n",
+    "        agents[a] = val\n",
+    "    \n",
+    "    A[k] = len(active)\n",
+    "    E[k] = Eth\n",
+    "    T[k] = Tokens\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "49999"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "k"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1a1d075470>]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x112f094a8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(range(K),n)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([2.9647e+04, 1.4560e+04, 4.0430e+03, 1.1820e+03, 3.9800e+02,\n",
+       "        1.0900e+02, 3.8000e+01, 1.4000e+01, 6.0000e+00, 3.0000e+00]),\n",
+       " array([ 0.        ,  1.10725381,  2.21450762,  3.32176142,  4.42901523,\n",
+       "         5.53626904,  6.64352285,  7.75077666,  8.85803046,  9.96528427,\n",
+       "        11.07253808]),\n",
+       " <a list of 10 Patch objects>)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x112c9ae80>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(rev)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "        8.04513681e-05, 0.00000000e+00, 0.00000000e+00, 1.60902736e-04,\n",
+       "        5.09008724e-04, 1.28404393e-02, 9.21238778e-02, 5.70530723e-01,\n",
+       "        2.71035714e+00, 6.61315611e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.41354104e-04,\n",
+       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00]),\n",
+       " array([-2.00000000e+00, -1.90000000e+00, -1.80000000e+00, -1.70000000e+00,\n",
+       "        -1.60000000e+00, -1.50000000e+00, -1.40000000e+00, -1.30000000e+00,\n",
+       "        -1.20000000e+00, -1.10000000e+00, -1.00000000e+00, -9.00000000e-01,\n",
+       "        -8.00000000e-01, -7.00000000e-01, -6.00000000e-01, -5.00000000e-01,\n",
+       "        -4.00000000e-01, -3.00000000e-01, -2.00000000e-01, -1.00000000e-01,\n",
+       "         1.77635684e-15,  1.00000000e-01,  2.00000000e-01,  3.00000000e-01,\n",
+       "         4.00000000e-01,  5.00000000e-01,  6.00000000e-01,  7.00000000e-01,\n",
+       "         8.00000000e-01,  9.00000000e-01,  1.00000000e+00,  1.10000000e+00,\n",
+       "         1.20000000e+00,  1.30000000e+00,  1.40000000e+00,  1.50000000e+00,\n",
+       "         1.60000000e+00,  1.70000000e+00,  1.80000000e+00,  1.90000000e+00]),\n",
+       " <a list of 39 Patch objects>)"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAAD8CAYAAABXe05zAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAADeJJREFUeJzt3X2MpWV9xvHr6i5IRVqQPSi64Eii9C2pkBOqrjEWxFBqQFNM0NhCSzMhjQYaE8WQNLH/NLaJfUnfMiKKKV3trqylBJStsEETd+HMugsssyAgjeNu2YNGYDUBF3/94zxjx7PnzLln5jznmd/4/SSTec4895xz5Z7Za+95XmYcEQIA5PFLTQcAACwPxQ0AyVDcAJAMxQ0AyVDcAJAMxQ0AyVDcAJAMxQ0AyVDcAJDMxjqedNOmTTE1NVXHUwPAujQ7O/tMRLRKxtZS3FNTU+p0OnU8NQCsS7b/p3Qsh0oAIBmKGwCSobgBIJmRxW37XNv7Fr09Z/v6SYQDABxv5MnJiHhU0pskyfYGSd+TtKPmXACAIZZ7qOQiSU9ERPHZTwDAeC23uK+UtHXQDtvTtju2O91ud/XJAAADFRe37RMlXSZp26D9ETETEe2IaLdaRdeQAwBWYDkr7t+TtDcinq4rDABgtOXcOfl+DTlMAqxXf7vzsaH7/vziN04wCfD/ilbctl8u6WJJt9UbBwAwStGKOyJ+LOn0mrMAAApw5yQAJENxA0AyFDcAJENxA0AyFDcAJENxA0AytfzpMmC92Pzs7BJ7uQEHzWDFDQDJUNwAkAzFDQDJUNwAkAzFDQDJUNwAkAzFDQDJUNwAkAzFDQDJUNwAkAzFDQDJUNwAkAzFDQDJUNwAkExRcds+1fZ22wdtz9l+S93BAACDlf4+7r+X9JWIuML2iZJeXmMmAMASRha37V+R9HZJV0tSRLwo6cV6YwEAhik5VHKOpK6kz9r+lu2bbJ/cP8j2tO2O7U632x17UABAT0lxb5R0vqR/iYjzJP1I0g39gyJiJiLaEdFutVpjjgkAWFBS3POS5iNiT/V4u3pFDgBowMjijoj/lfRd2+dWH7pI0iO1pgIADFV6VcmHJd1aXVHypKQ/ri8SAGApRcUdEfsktWvOAgAowJ2TAJAMxQ0AyVDcAJAMxQ0AyVDcAJAMxQ0AyVDcAJAMxQ0AyVDcAJAMxQ0AyVDcAJAMxQ0AyVDcAJAMxQ0AyVDcAJAMxQ0AyVDcAJAMxQ0AyVDcAJAMxQ0AyRT9sWDbT0l6XtJLko5FBH84GAAaUlTcld+NiGdqSwIAKMKhEgBIprS4Q9LdtmdtT9cZCACwtNJDJVsi4pDtMyTttH0wIu5bPKAq9GlJOvvss8ccEwCwoGjFHRGHqvdHJO2QdMGAMTMR0Y6IdqvVGm9KAMDPjCxu2yfbPmVhW9K7JD1cdzAAwGAlh0peJWmH7YXx/x4RX6k1FQBgqJHFHRFPSvrtCWQBABTgckAASIbiBoBkKG4ASIbiBoBkKG4ASIbiBoBkKG4ASIbiBoBkKG4ASIbiBoBkKG4ASIbiBoBkKG4ASIbiBoBkKG4ASIbiBoBkKG4ASIbiBoBkKG4ASIbiBoBkKG4ASKa4uG1vsP0t23fUGQgAsLTlrLivkzRXVxAAQJmi4ra9WdLvS7qp3jgAgFFKV9x/J+mjkn5aYxYAQIGRxW373ZKORMTsiHHTtju2O91ud2wBAQA/r2TFvUXSZbafkvQFSRfa/rf+QRExExHtiGi3Wq0xxwQALBhZ3BHx8YjYHBFTkq6UdE9EfLD2ZACAgbiOGwCS2bicwRGxS9KuWpIAAIqw4gaAZChuAEiG4gaAZChuAEiG4gaAZChuAEiG4gaAZChuAEiG4gaAZChuAEiG4gaAZChuAEiG4gaAZChuAEiG4gaAZChuAEiG4gaAZChuAEiG4gaAZChuAEiG4gaAZEYWt+2TbN9ve7/tA7Y/MYlgAIDBNhaMeUHShRFx1PYJkr5h+66I2F1zNgDAACOLOyJC0tHq4QnVW9QZCgAwXNExbtsbbO+TdETSzojYU28sAMAwRcUdES9FxJskbZZ0ge3f6h9je9p2x3an2+2OOycAoLKsq0oi4oeSdkm6ZMC+mYhoR0S71WqNKR4AoF/JVSUt26dW278s6Z2SDtYdDAAwWMlVJWdKusX2BvWK/j8i4o56YwEAhim5quRBSedNIAsAoAB3TgJAMhQ3ACRDcQNAMhQ3ACRTclUJsH595+tNJwCWjRU3ACRDcQNAMhQ3ACRDcQNAMhQ3ACRDcQNAMlwOiF9o22bnm44ALBsrbgBIhuIGgGQobgBIhuIGgGQobgBIhuIGgGQobgBIhuIGgGQobgBIZmRx2z7L9r2252wfsH3dJIIBAAYrueX9mKSPRMRe26dImrW9MyIeqTkbAGCAkSvuiDgcEXur7eclzUl6bd3BAACDLesYt+0pSedJ2lNHGADAaMXFbfsVkr4k6fqIeG7A/mnbHdudbrc7zowAgEWKitv2CeqV9q0RcdugMRExExHtiGi3Wq1xZgQALFJyVYklfUbSXER8qv5IAICllKy4t0j6Q0kX2t5XvV1acy4AwBAjLweMiG9I8gSyAAAKcOckACRDcQNAMhQ3ACRDcQNAMhQ3ACRDcQNAMhQ3ACRDcQNAMhQ3ACRDcQNAMhQ3ACRDcQNAMhQ3ACRDcQNAMhQ3ACRDcQNAMhQ3ACRDcQNAMhQ3ACRDcQNAMhQ3ACQzsrht32z7iO2HJxEIALC0khX35yRdUnMOAEChkcUdEfdJ+sEEsgAACnCMGwCSGVtx25623bHd6Xa743paAECfsRV3RMxERDsi2q1Wa1xPCwDow6ESAEim5HLArZK+Kelc2/O2r6k/FgBgmI2jBkTE+ycRBABQhkMlAJAMxQ0AyVDcAJAMxQ0AyVDcAJAMxQ0AyVDcAJAMxQ0AyYy8AQdI7TtfbzoBMHasuAEgGVbcWNe2zc43HQEYO1bcAJAMxQ0AyVDcAJAMxQ0AyVDcAJAMxQ0AyVDcAJAMxQ0AyVDcAJAMxQ0AyRQVt+1LbD9q+3HbN9QdCgAw3MjfVWJ7g6R/knSxpHlJD9i+PSIeqTscMMq27VubjgBMXMmK+wJJj0fEkxHxoqQvSLq83lgAgGFKfjvgayV9d9HjeUm/U08c4OexogaOV1LcHvCxOG6QPS1punp41PajK8y0SdIzK/zcupFtZdZptg+MNcgA63Teapc12+tKn6SkuOclnbXo8WZJh/oHRcSMpJnSFx7Gdici2qt9njqQbWXItjJkW5lfhGwlx7gfkPQG26+3faKkKyXdvtoXBgCszMgVd0Qcs/0hSV+VtEHSzRFxoPZkAICBiv50WUTcKenOmrMsWPXhlhqRbWXItjJkW5l1n80Rx51nBACsYdzyDgDJNF7ctv/G9kHbD9reYfvUIeMmftu97ffZPmD7p7aHngm2/ZTth2zvs91ZY9mamLdX2t5p+9vV+9OGjHupmrN9tms94T1qHmy/zPYXq/17bE/VmWeZ2a623V00V386oVw32z5i++Eh+237H6rcD9o+fxK5CrO9w/azi+bsLyaY7Szb99qeq/6NXjdgzOrmLiIafZP0Lkkbq+1PSvrkgDEbJD0h6RxJJ0raL+k3JpDt1yWdK2mXpPYS456StGnC8zYyW4Pz9teSbqi2bxj0Na32HZ3QXI2cB0l/Julfq+0rJX1xDWW7WtI/TvL7q3rdt0s6X9LDQ/ZfKuku9e71eLOkPWso2zsk3THpOate+0xJ51fbp0h6bMDXdFVz1/iKOyLujohj1cPd6l0n3q+R2+4jYi4iVnojUa0KszX16woul3RLtX2LpPdM4DWXUjIPizNvl3SR7UE3nzWRrRERcZ+kHywx5HJJn4+e3ZJOtX3mGsnWmIg4HBF7q+3nJc2pdwf6Yquau8aLu8+fqPe/UL9Bt933T0STQtLdtmerO0jXiqbm7VURcVjqfRNLOmPIuJNsd2zvtl1nuZfMw8/GVAuJZyWdXmOm5WSTpD+ofqTebvusAfubsNb/Xb7F9n7bd9n+zSYCVIfczpO0p2/Xquau6HLA1bL935JePWDXjRHxn9WYGyUdk3TroKcY8LGxXA5Tkq3Alog4ZPsMSTttH6xWBE1na2TelvE0Z1fzdo6ke2w/FBFPjCNfn5J5qG2uRih53f+StDUiXrB9rXo/GVxYe7LRmpqzEnslvS4ijtq+VNKXJb1hkgFsv0LSlyRdHxHP9e8e8CnFczeR4o6Idy613/ZVkt4t6aKoDgD1Kbrtvo5shc9xqHp/xPYO9X78XXVxjyFbI/Nm+2nbZ0bE4erHvyNDnmNh3p60vUu9lUkdxV0yDwtj5m1vlPSrmsyP4iOzRcT3Fz38tHrngtaC2r6/VmtxUUbEnbb/2famiJjI7zCxfYJ6pX1rRNw2YMiq5q7xQyW2L5H0MUmXRcSPhwxbs7fd2z7Z9ikL2+qdbB14prsBTc3b7ZKuqravknTcTwe2T7P9smp7k6Qtkur6He8l87A48xWS7hmyiJh4tr5jn5epd8x0Lbhd0h9VV0i8WdKzC4fImmb71QvnKGxfoF7XfX/pzxrba1vSZyTNRcSnhgxb3dw1cda17+zq4+od69lXvS2c2X+NpDv7zsI+pt6K7MYJZXuvev8zviDpaUlf7c+m3tUA+6u3A2spW4Pzdrqkr0n6dvX+ldXH25JuqrbfKumhat4eknRNzZmOmwdJf6negkGSTpK0rfp+vF/SOZOYq8Jsf1V9b+2XdK+kX5tQrq2SDkv6SfW9do2kayVdW+23en9k5Ynqazj0yqsGsn1o0ZztlvTWCWZ7m3qHPR5c1GuXjnPuuHMSAJJp/FAJAGB5KG4ASIbiBoBkKG4ASIbiBoBkKG4ASIbiBoBkKG4ASOb/AP9vP+dMLgsnAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1a235e07b8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist((true_best_param-param)/true_best_param, bins=(np.arange(-1,1,.1)), density=True, alpha=.5)\n",
+    "plt.hist((true_best_param-param)/true_best_param, bins=(np.arange(-1,1,.1)), density=True, weights = rev, alpha=.25)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5,0,'Number of Agents')"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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d5u3TrZjvnn84Hz3sYArz319Brm9sYum6bQzvU0EIgVuemMuvJ0fn8+dPvUvfHiUM71PBC++tBeDYgZV85NA+DOhVxnPvrKZm/TZm1WxgR9PuNcLuJQVs2t7YZpluenROu+XNz9NutcvqylKKCvLYvqOJPt2KKSsqYOWm7TQ0NrNswzYOPaQbp48+mIriAt5ZuZmqiiKu/tAwCvJEZVnhbn9rS2q38vDMZcyq2cDTb6/mlBFVnDSsiouOraY5BNbVNVCUn8ecFZtYsXE7B5UXcVjf7vQqL2LtlnrO+9VLHR5vgPunLe0wz2tLNnSY545/zOeOfxlDZVkhzSFwwpCDdgb05ubAknVb+caf36C0KJ/hvSu46Nj+zFy6geF9KjiyukfOP1ekbL67ZezYsWH69On7tOzSdVs55Zbndk5fdEw1f5u9kke++EFGHNyNGYvX8/Ffv8zvrjiODx/a533Lf+3/ZvHsO6v536vG0a+yhPLigjYvDm1p+aZZXlxASWE+U+bXMuE3r3DKiCp+eslRnPLj53Yb1yj6RyxiSFUZ0xatB6KLyFdOH8kpI3rv1X7vaGomT6lfQPd2m8keG4juqpoyv5bN9Y2MH9WbTdsa6VZSwONvruCrk2btlvd/rjyeU0cmfwy2NTTx0Mxl/O8ri3feAnzy8CpOHlHF03NW8e6qzXzk0D4M613BlvpGJDFnxSZeX7KezdsbGdCrlI8eejDHDKzktcXraWgK1KzfytQF62hoauafjuzLs++sZtuOJi44uh/XnTaSb/7lTV6eX0tVRTFVFUX0LCvi3VWbqa3b1SRZVVFMz7JCzj2qH4d0L6FvZQnLN2xj0vQayosLGD+yNyWF+QzvU8HAXmXU1tUzZX4tk+euYVFtHccM7MlhfbtRWpjPcYN7cUR1D1Zv3k79jmZ+9uRcpi9eT1VFMTXrt7J2SwP5eeKI6h5UlRfR0NTM1IXraGiM/uaKCvJ2fgYoKczb+S28M114TDXFBXkMqSrn42P6U1kaBZv8PDF1QS13v7iQkQdXMKQq2ufZyzZSXpzPCUMPio9BAweVF/H4myt5c9lGDuvbjW0NTby2ZD2lhfmcOqo3U+bXct+rS9lSv6vsFcUFfOyIQ3h31WZm1XTc73feUf341EmDOaK6O/nSbrXC2i31dC8t5N1Vmxl5cLe9+jvPJEkzQghjk8qbriAgaShwI9AjhHBxMsukEgRWb97O8T94BoAPjezNrZcdTWVZ0c75C9fW8eGfTuZnlxzFx8f0f9/yZ/7iefp0L+b3V43bp+23Vrulnp5lReTliZfnrWVmzQYamwKfOmkw81Zv4eO/fhmAG88+DAnunbKYZRu2cc8Vx7V7EWxsaqa2roGDu5fw0ry1XPP7GWyub2RY73L+9zPjmDK/lj+/VsOR1ZV88SPDqSguYEdTM0/PWcXWhiZm1WygOQRKC/M57bCD6d2tmP49yygqaPsPfWtDI5PnrmFAzzIqSgp48b01fPvhtwC46dzRXH7iYHY0N7Nmcz3VlaVIYubSDTw1ZyV19U0cO6gnJw49iH9/YBaT57Y9zHdpYT5jBvWkT/diHnxtGQBDe5ezYesOhvUup6ggj0Vrt3JY3+68uWwDqzbVc+5R/bj5oiN5eOYybvzL7J3rGt23O9+74HDGDOrV4fnZtH0Hf31jBXf+Yz6LarfuNm/wQWWMH9WH5+auZnE87wsfHsZXThtJQXxR2NHUHF1A4otHCIE/v7aMvj1KGDek18586VDf2ERRft77apLvrdpMaVE+/XuW8erCdfx99koampqYs3wTqzbVs21HE+vqGjiyugc/veQoRh3SjR1NzRTkibeWb+KZt1ezdP1WBvUqo6pbMUOqyhl8UDnLNmxl8tw1lBTmU1ffyEcO7cPYwR0f886yvq6BZ95ZzR3/mE9Tc2Bhwo0ghfni9NEH86ERvelRWsjKTdt55u3VjDqkG/17lvKddmpb/XqUEIAVG7fvll5ckMcPLzySMw4/mG4lUf/ia0vWM3PJBv553MCsaZbq9CAg6R7gHGB1COGIhPSzgP8C8oG7Qwg/SmJdD6QjCDQ1B75032tc+cEhbf5Bbtq+gw/c9CTfPPtQrv7QsN3mbdy2g6O+8yRfP2tU2truf/DXOZQVFfCV00cCUFffyPm3vcS81VuYduNpO5usGhqjf0qA8297iTeXbaQoP6/dEVN7lBaycdsOLjymmq+ePpJPT5zGvD2MqTSgVynfOOtQ6uob+ehhB1NVEW23uTnwqd+9urO5oyMXHVPNkKpyfvbUu++blyf4tzNGMbBXGXNWbOLtFZuYsWg9ow7pxr1XHU9ZUcHOY/Dfz85j8tyoaW5x7VYO7l7MjqbAsg3bgKjGlNhkMGZQT67+0FDOGH3wPjWnhRB4a/kmNm3bwfA+FRQX5NOjLPpnb2oOzFm+iaG9yykv7rItqQe8Ocs3sWrzdvpXljKkqnyPAbiuvpHbJ89j1tKNDO1dzqZtO3gooT9xeJ8KVm7cvltNY0+e//cP07eyhB1NzXz7obdYu6Wemy86kn5pvntwfwSBDwFbgHtbgoCkfOBd4HSgBpgGTCAKCDe3WsWVIYTV8XJpCQIdCSEw+j+e4JPjBvKtc0bvNm/W0g2cf9tL3PWvYzjj8EP2y/aT0VKOfzlhIEOqKjiqfw8uvmMKhfnarW25urKU6p6l/Oqfj6FPtxKW1G7lwddr6NOthMuOG8Avn36XW5+dB0TfjG46L+rbOGFoL7qVFNLcHJi2aB3vrNzM7ZPnsX3HroAyZlBPPn/qMH774kKmLKjl0rH9GXVId1Zu3MYJQw9i/Kg+NIfA3S8s5IEZSzl99CH8ffaKnd+mqyqK+NPnTmTjth28s2Izry6s5fKTBnPswJ77fFxaLtQDepXRo7SQqQtq+eqkWVx23ACuGT/sgKmyW/ZaXFtHc4AhVeW7pb+2ZD23/P0dXkl4V8knxg6gqCCP37+yuN31/fjjR3Lp2AG8t3oL33tsDt+/4AgGHVTebv5U7ZfmIEmDgccSgsCJwE0hhDPj6RsAQgitA0Dr9ewxCEi6GrgaYODAgWMWL27/wKbq1J88x1H9K7l1wjG7pT8wo4av/d8s/vblUzisb/f9tv1kjPvh0zs7mVucNOwgKooLGD+qDxOOH9DhN94QAo++sYIHZtRw/lH92mz+arFswzaefWc1Q6vK+cPUxTz+5kog6sD86ukj+dRJgzvc3sqN23l45jKOGlDJkdU9/K3ZuqQQAo3NYeeXjvteXcIND765c/4lY/pTW9fQ5k0mLfbXNWZvgkAq/53VQGL3fg3QbgO6pIOAHwDHSLqhvWARQrhL0grg3KKiojEplK9DlWVFbIjvFkr07YeiduX+PTP/ANiZhx/CvVMW86GRvVmzuZ7vX3AEYwbt3bdoSZx3VD/OO6pfh3mrK0v51xMGAVGweXjmckoK8xg/qk/S7Z2H9Cjhc6cO6zij2QFMEoX5u74QTTh+IBOOH/i+fBNfWtjunV4f+68XmPS5Exnep4Je5UVt5tnfUgkCbX0dbLdaEUKoBa5JZsUhhEeBR8eOHfvZfSxbUvpXljJ98TpCCLt9uy3MF/l5BTs7fjLpO+cdzufHD6Nvj/QHJElccEx12rdr1pVc8cEhXPHBISzfsI2fPjmXH154JNMWreNff/sqAJfeGd3KPvf7Z1FckP6O5VQaT2uAAQnT/YG2n9DaS6mOIpqsYwZWsmpTPRu27l4bKMjP44JjOv7WnA6SMhIAzKxz9ass5eeXHk1JYT6njOjN698+fbf5Y773dEYGpEwlCEwDRkgaIqkIuAx4pDMK1VnvGO5Iy0NfC9bufrdMXX3jzjtUzMz2h57lRSz60T9xy8UfAGBLfSNDbng87eVIKghIug+YAoySVCPpqhBCI/BF4AngbWBSCOGtzihUumoCLW1w6+t21QQaGpupb2ymmzszzSwNLh07gF984qiMbT+pIBBCmBBC6BtCKAwh9A8h/DZOfzyEMDKEMCyE8IPOKlS6agLlxVH729aEAd82bY8CQstAc2Zm+9uFx+y6Y29LfSNTF9SyraFpD0t0npy+obqlyWdrwoMgLY/Ody91TcDM0qeqImqZ+PjtL/OJu17hF0+//0HL/SErg0C6moMq4tdPJj4NuCm+ZbSHawJmlkZfP/NQAOau2gzA2yvS8/rbrAwC6WoO6lZcQH6ednv3QMsoo9lwe6iZ5Y5TR+0+RlhemkYRzsogkK6agKRo+NvNu4LAzj4BBwEzS6M+rYa0v/zEQWnZblYGgXTVBCC6d3f5xm07p9dvbakJuE/AzNKn9XAsfbqVpGW7WRkE0ql/ZSkvvLeWWUujkSifnrOK6spSDumenhNgZtYi8T0g6XqnVM4HgZbx3y+8PXobUs36rRw1oMdev2rQzCxVN527a0Rj9wmkoU8Adg2A1PKmvrr6Jsr9tLCZZUBxwiCNOV0TSGefwDkf6Lvz89yVm6mrb/TQx2aWEUUJ78LI6ZpAOiW+NObWZ96jrqHRncJmlhGFuwWB9Gwz54NAouKCPJoDHjzOzDKipHDXJTmnm4PS2SeQaGs8VkfLmEJmZulUWbbrxTL78o7sfZGVQSCdfQIQvToRYPXm7fG0HxQzs/QrLnCfQEY8eO1JACzfEAWBg/2MgJllQGIQSNdN6g4CwPA+3RhaVU5tXfRCdw8eZ2aZUJZwZ6JrAmlWVJDHjqboYYHEzhkzs3Rpbt71esmc7hjOhMRnA0qL3DFsZul3SI9dTdE5HQQycXdQ4rMBfljMzDIh8TmB4oL0fBnNyiCQ7ruDACoSLvweNsLMMi0/TU+LZWUQyISWl8iUFeWn7eCbmbXHdwelWUtzkJuCzCwb5HSfQCa0DNxU4SBgZlkgp58YzoSC/OiAuynIzLKBawJp1tIr3xxCBznNzPa/LtknIOkCSb+R9LCkM9K57Y4UxjUBxwAzywZZ1xwk6R5JqyXNbpV+lqS5kuZJun5P6wghPBRC+CxwBfCJfSrxflKQFx2KpmZHATPLvHS1TO9NL+hE4FfAvS0JkvKB24DTgRpgmqRHgHzg5lbLXxlCWB1//la8XNYoLHBzkJllD6WpQSjpIBBCeF7S4FbJxwPzQggLACTdD5wfQrgZOKf1OhTVb34E/C2E8Nq+Fnp/KHCHsJllkQOlY7gaWJowXROntedLwGnAxZKuaSuDpKslTZc0fc2aNSkWL3n5cp+AmeWeVG+KbytWtXsZDSHcCty6pxWGEO6StAI4t6ioaEyK5Uuabw01s2xyoAwlXQMMSJjuDyxPcZ0ZGTvIQcDMssmB0hw0DRghaYikIuAy4JFUC5WJUURbgkBwe5CZZYGse05A0n3AFGCUpBpJV4UQGoEvAk8AbwOTQghvpVoo1wTMLNelqzlob+4OmtBO+uPA451WIqKaAHDu8OHDO3O1e9RywF0PMLNscKA0B+0XmawJuD5gZtkg654YTqfM9AmkbVNmZlkjKy99magJpKv9zcwsm2RlEMgEdwybWS7KyiCQkeYg1wTMLAdlZRDISHOQawJmloOyMghkgpuDzCwXZWUQyERzkDuGzSwXZWUQ8BPDZmbpkZVBIBPcMWxmuchBIJbnI2FmOciXvpibg8wsF2VlEMjkcwIeQM7McklWBoFMdAy3DNbku4TMLJdkZRDIJPcNmFku8SUv1lIBcE3AzHKJg0ArDgJmlkscBGItrxZ2DDCzXJKVQSATdwc1x1HANQEzyyVZGQQycXfQriCQtk2amWVcVgYBMzNLDweBmOJXzAc/LWZmOcRBIOauADPLRQ4CrbgiYGa5xEEg5oqAmeWitAUBSYdJukPSA5I+n67t7q3gTgEzyyFJBQFJ90haLWl2q/SzJM2VNE/S9XtaRwjh7RDCNcClwNh9L/L+0dIn4BBgZrkk2ZrAROCsxARJ+cBtwMeA0cAESaMlHSnpsVY/feJlzgNeBJ7ptD3oNG4QMrPcU5BMphDC85IGt0o+HpgXQlgAIOl+4PwQws3AOe2s5xHgEUl/Bf7YVh5JVwNXAwwcODCZ4pmZ2T5KKgi0oxpYmjBdA4xrL7Ok8cBFQDHweHv5Qgh3AXcBjB07Nv2tM24PMrMckkoQaKv9pN1LaAhhMjBhIdqzAAAPDUlEQVQ5qRVL5wLnDh8+fJ8Kti/cJ2BmuSiVu4NqgAEJ0/2B5akVJ5KRN4vt2nbatmlmlmmpBIFpwAhJQyQVAZcBj3RGoTIxiqiZWS5K9hbR+4ApwChJNZKuCiE0Al8EngDeBiaFEN7qjEJloiZgZpaLkr07aEI76Y+zh07efZWZPgHfImpmuScrh41wTcDMLD2yMghksk/A3cJmlkuyMgi4JmBmlh5ZGQQyyT0DZpZLsjIIuDnIzCw9sjIIZPJhMTOzXJKVQSATXAMws1yUlUHATwybmaVHVgYBNweZmaVHVgYBMzNLDwcBM7MclpVBwH0CZmbpkZVBwE8Mm5mlR1YGgUzyO2XMLJc4CMQ8krSZ5SIHgZhrAGaWixwEzMxyWFYGgUzcHeTmIDPLRVkZBHx3kJlZemRlEMiE8uLodctHD6jMcEnMzNInqRfN54KqimIe+9LJDO9TkemimJmljYNAgiOq3fxkZrnFzUFmZjnMQcDMLIc5CJiZ5bC0BgFJ5ZJmSDonnds1M7O2JRUEJN0jabWk2a3Sz5I0V9I8SdcnsapvAJP2paBmZtb5kr07aCLwK+DelgRJ+cBtwOlADTBN0iNAPnBzq+WvBD4AzAFKUiuymZl1lqSCQAjheUmDWyUfD8wLISwAkHQ/cH4I4Wbgfc09kj4MlAOjgW2SHg8hNLeR72rgaoCBAwcmvydmZrbXUnlOoBpYmjBdA4xrL3MI4UYASVcAa9sKAHG+uyStAM4tKioak0L5zMysA6l0DLc15FqHAzKHECaGEB7rII/HDjIzS4NUgkANMCBhuj+wPLXiRPyOYTOz9EglCEwDRkgaIqkIuAx4pDMK5ZqAmVl6JHuL6H3AFGCUpBpJV4UQGoEvAk8AbwOTQghvdUahXBMwM0uPZO8OmtBO+uPA451aomi9jwKPjh079rOdvW4zM9vFw0aYmeWwrAwCbg4yM0uPrAwC7hg2M0uPrAwCrgmYmaVHVgYB1wTMzNIjK4OAmZmlh4OAmVkOy8og4D4BM7P0yMog4D4BM7P0yMogYGZm6eEgYGaWw7IyCLhPwMwsPbIyCLhPwMwsPbIyCJiZWXo4CJiZ5TAHATOzHOYgYGaWw7IyCPjuIDOz9MjKIOC7g8zM0iMrg4CZmaWHg4CZWQ5zEDAzy2EOAmZmOcxBwMwsh6UtCEgaL+kFSXdIGp+u7ZqZWfuSCgKS7pG0WtLsVulnSZoraZ6k6ztYTQC2ACVAzb4V18zMOlNBkvkmAr8C7m1JkJQP3AacTnRRnybpESAfuLnV8lcCL4QQ/iHpYODnwCdTK7qZmaUqqSAQQnhe0uBWyccD80IICwAk3Q+cH0K4GThnD6tbDxS3N1PS1cDVAAMHDkymeGZmto9S6ROoBpYmTNfEaW2SdJGkO4HfE9Uq2hRCuCuEMDaEMLZ3794pFM/MzDqSbHNQW9RGWmgvcwjhQeDBpFYsnQucO3z48H0smpmZJSOVmkANMCBhuj+wPLXiRDx2kJlZeqQSBKYBIyQNkVQEXAY80hmF8iiiZmbpkewtovcBU4BRkmokXRVCaAS+CDwBvA1MCiG81RmFck3AzCw9kr07aEI76Y8Dj3dqiXCfgJlZumTlsBGuCZiZpUdWBgH3CZiZpUdWBgHXBMzM0iMrg4CZmaVHVgYBNweZmaVHVgYBNweZmaVHVgYBMzNLj6wMAm4OMjNLj6wMAm4OMjNLj6wMAmZmlh4OAmZmOcxBwMwsh2VlEHDHsJlZemRlEHDHsJlZemRlEDAzs/RwEDAzy2EOAmZmOcxBwMwshyX1esl08+slzSxXTfrciSyurUvb9hRCSNvG9tbYsWPD9OnTM10MM7MDiqQZIYSxyeR1c5CZWQ5zEDAzy2EOAmZmOcxBwMwshzkImJnlsLTdIiopD/ge0B2YHkL4n3Rt28zM2pZUTUDSPZJWS5rdKv0sSXMlzZN0fQerOR+oBnYANftWXDMz60zJ1gQmAr8C7m1JkJQP3AacTnRRnybpESAfuLnV8lcCo4ApIYQ7JT0APJNa0c3MLFVJBYEQwvOSBrdKPh6YF0JYACDpfuD8EMLNwDmt1yGpBmiIJ5va25akq4Gr48ktkuYmU8Y2VAFr93HZA5n3O3fk4j6D9zsZg5JdaSp9AtXA0oTpGmDcHvI/CPy3pFOA59vLFEK4C7grhXIBIGl6sk/MdSXe79yRi/sM3u/OXm8qQUBtpLU7BkUIYStwVQrbMzOzTpbKLaI1wICE6f7A8tSKY2Zm6ZRKEJgGjJA0RFIRcBnwSOcUq1Ok3KR0gPJ+545c3GfwfneqpEYRlXQfMJ6oY2IV8J8hhN9KOhv4JdEdQfeEEH6wPwppZmb7R1YPJW1mZvuXh40wM8thXTII7OWTzFlN0gBJz0l6W9Jbkr4cp/eS9JSk9+LfPeN0Sbo13vc3JB2bsK5Pxfnfk/SpTO3T3pCUL+l1SY/F00MkTY334U9xfxSSiuPpefH8wQnruCFOnyvpzMzsSfIkVUp6QNI78Xk/saufb0lfif++Z0u6T1JJVzzXbY2+0JnnVtIYSW/Gy9wqqa27OHcXQuhSP0T9E/OBoUARMAsYnelypbA/fYFj48/dgHeB0cAtwPVx+vXAj+PPZwN/I7qF9wRgapzeC1gQ/+4Zf+6Z6f1LYv+/CvwReCyengRcFn++A/h8/Pla4I7482XAn+LPo+O/gWJgSPy3kZ/p/epgn/8H+Ez8uQio7Mrnm+iZo4VAacI5vqIrnmvgQ8CxwOyEtE47t8CrwInxMn8DPtZhmTJ9UPbDQT4ReCJh+gbghkyXqxP372GioTrmAn3jtL7A3PjzncCEhPxz4/kTgDsT0nfLl40/RLcdPwN8BHgs/sNeCxS0PtfAE8CJ8eeCOJ9an//EfNn4QzTA4kLi/rrW57Ernm92PXjaKz53jwFndtVzDQxuFQQ65dzG895JSN8tX3s/XbE5qK0nmaszVJZOFVd7jwGmAgeHEFYAxL/7xNna2/8D8bj8Evg60BxPHwRsCCE0xtOJ+7Bz/+L5G+P8B9p+DwXWAL+Lm8HullROFz7fIYRlwE+BJcAKonM3g65/rlt01rmtZvfBOZPa/64YBPbqSeYDhaQK4M/AdSGETXvK2kZa2EN6VpJ0DrA6hDAjMbmNrKGDeQfUfhN9sz0W+HUI4RigjqiJoD0H/H7HbeDnEzXh9APKgY+1kbWrneuO7O1+7tP+d8Ug0OWeZJZUSBQA/hBCeDBOXiWpbzy/L7A6Tm9v/w+04/JB4DxJi4D7iZqEfglUSmoZ7iRxH3buXzy/B7COA2+/a4CaEMLUePoBoqDQlc/3acDCEMKaEMIOonHGTqLrn+sWnXVua+LPrdP3qCsGgWx/knmvxL37vwXeDiH8PGHWI0DLXQGfIuoraEm/PL6z4ARgY1zFfAI4Q1LP+JvXGXFaVgoh3BBC6B9CGEx0Dp8NIXwSeA64OM7Wer9bjsfFcf4Qp18W31EyBBhB1HmWlUIIK4GlkkbFSR8F5tC1z/cS4ARJZfHfe8s+d+lznaBTzm08b7OkE+LjeHnCutqX6U6S/dTxcjbRXTTzgRszXZ4U9+VkoirdG8DM+OdsojbQZ4D34t+94vwies/DfOBNYGzCuq4E5sU/n870vu3FMRjPrruDhhL9Y88D/g8ojtNL4ul58fyhCcvfGB+PuSRxt0Smf4CjgenxOX+I6A6QLn2+ge8A7wCzgd8T3eHT5c41cB9Rv0fLy7Wu6sxzC4yNj+F8onfAqKMy+YlhM7Mc1hWbg8zMLEkOAmZmOcxBwMwshzkImJnlMAcBM7Mc5iBgWUFSkPSzhOmvSbqpk9Y9UdLFHedMeTuXxKN+PtfO/K9I2i6px37a/nhJJ+2PdVvX5SBg2aIeuEhSVaYLkkhS/l5kvwq4NoTw4XbmTyB6mPHClAvWtvFET9qaJc1BwLJFI9E7VL/Sekbrb/KStsS/x0v6h6RJkt6V9CNJn5T0ajym+rCE1Zwm6YU43znx8vmSfiJpWjxe++cS1vucpD8SPaTTujwT4vXPlvTjOO0/iB7su0PST9pYZhhQAXyLKBi0pJfF5X9D0Rj5UyWNjeedIWmKpNck/V88fhSSFkn6Tpz+pqRD48EFrwG+ImmmpFPimslsSbMkPb8X58JySEHHWczS5jbgDUm37MUyRwGHEY0dswC4O4RwvKKX73wJuC7ONxg4FRgGPCdpONFj9RtDCMdJKgZekvRknP944IgQwsLEjUnqB/wYGAOsB56UdEEI4buSPgJ8LYQwvY1yTiB6WvQFYJSkPiGE1URj468PIXxA0hFET4QT14i+BZwWQqiT9A2idyt8N17f2hDCsZKujbf5GUl3AFtCCD+N1/EmcGYIYZmkyr04ppZDXBOwrBGi0VHvBf7fXiw2LYSwIoRQT/SofMtF/E2iC3+LSSGE5hDCe0TB4lCiMVculzSTaHjug4jGmwF4tXUAiB0HTA7RYGeNwB+IXhTSkcuA+0MIzUQDpF0Sp59MNEAeIYTZRENFQPQSkdFEgWkm0ZgygxLW1zKQ4IxW+5noJWCipM8SvWzJ7H1cE7Bs80vgNeB3CWmNxF9Y4oGxihLm1Sd8bk6Ybmb3v+/W46O0DL37pRDCbgOrSRpPNIRzWzp+XV/rBaQPEAWXp6LiU0QUiG7bw/oEPBVCmNDO/Jb9bKKd/+MQwjWSxgH/BMyUdHQIoXZvy29dm2sCllVCCOuIXit4VULyIqLmF4jGnS/ch1VfIikvbpsfSjTA2BPA5xUN1Y2kkYpe4LInU4FTJVXFncYTgH90sMwE4KYQwuD4px9QLWkQ8CJwabz90cCR8TKvAB+Mm61a+g5GdrCdzUSvICVeZlgIYWoI4T+I3r41oN0lLWc5CFg2+hmQeJfQb4guvK8C42j/W/qezCW6WP8NuCaEsB24m2jI4tcUvfj7TjqoHYdouN4biIY5ngW8FkLoaLjey4C/tEr7S5x+O9Bb0hvAN4iagzaGENYQvWf3vnjeK0RNWHvyKHBhS8cw8JOWDmzg+bi8ZrvxKKJmGRTXJgpDCNvjWsozwMgQQkOGi2Y5wn0CZplVRnS3UiFRP8DnHQAsnVwTMDPLYe4TMDPLYQ4CZmY5zEHAzCyHOQiYmeUwBwEzsxz2/wFhdRA10G9wswAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1a210c59e8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.semilogy(n, np.abs(true_best_param - param)/true_best_param, n, .1)\n",
+    "plt.title(\"Logscale absolute percent error\")\n",
+    "plt.labels([\"error\", \"10% reference\"])\n",
+    "plt.xlabel(\"Number of Agents\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1a1f219cc0>,\n",
+       " <matplotlib.lines.Line2D at 0x1a1f219e80>]"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1a1d6a7550>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(range(K), true_best_param, range(K), param )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x10cfd4b70>]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1a1cebe0b8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(range(K), E/T)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x10cff07b8>,\n",
+       " <matplotlib.lines.Line2D at 0x112eaac18>]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x112cd96d8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(range(K), E, range(K), T)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x10f2ccda0>]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x112d23e10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(E, T)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x1a1d00b320>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10f2b1358>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(param,rev)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[4, 6, 0, 4]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11013cef0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.kdeplot(param,rev)\n",
+    "plt.axis([4,6,0,4])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.JointGrid at 0x1a1e495278>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1a1e480320>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.jointplot(param, rev, kind=\"kde\", size=7, space=0, xlim=[4,6],ylim=[0,5])"
+   ]
+  },
+  {
+   "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": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/c_2014_zargham_etal.pdf b/c_2014_zargham_etal.pdf
new file mode 100644
index 0000000..3eeec1d
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diff --git a/passive_voting.isdb b/passive_voting.isdb
new file mode 100644
index 0000000..f058636
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diff --git a/passive_voting.stmx b/passive_voting.stmx
new file mode 100644
index 0000000..30b8dae
--- /dev/null
+++ b/passive_voting.stmx
@@ -0,0 +1,453 @@
+<?xml version="1.0" encoding="utf-8"?>
+<xmile version="1.0" xmlns="http://docs.oasis-open.org/xmile/ns/XMILE/v1.0" xmlns:isee="http://iseesystems.com/XMILE">
+	<header>
+		<smile version="1.0" namespace="std, isee" uses_arrays="2"/>
+		<name>passive_voting</name>
+		<uuid>28b846bc-f595-4851-9ecf-21816b3c6d4c</uuid>
+		<vendor>isee systems, inc.</vendor>
+		<product version="1.6.2" isee:build_number="1445" isee:saved_by_v1="true" lang="en">Stella Professional</product>
+	</header>
+	<sim_specs isee:simulation_delay="0.00015" method="Euler" time_units="Blocks" isee:instantaneous_flows="false">
+		<start>1</start>
+		<stop>10000</stop>
+		<dt reciprocal="true">1</dt>
+	</sim_specs>
+	<dimensions>
+		<dim name="choice" size="4"/>
+	</dimensions>
+	<isee:prefs show_module_prefix="true" live_update_on_drag="true" show_restore_buttons="false" layer="model" saved_runs="1" keep="false" rifp="true"/>
+	<isee:multiplayer_settings include_chat="true" allow_observers="false" advance_time_increment="1" observer_start_page="home_page" enabled="false"/>
+	<isee:time_formats default_format="Builtin">
+		<isee:time_format name="Builtin" type="adaptive"/>
+	</isee:time_formats>
+	<default_format/>
+	<model_units/>
+	<model>
+		<variables>
+			<stock name="Tokens">
+				<dimensions>
+					<dim name="choice"/>
+				</dimensions>
+				<eqn>GAMMA(1, 2)</eqn>
+				<inflow>mint</inflow>
+				<inflow>exchange</inflow>
+				<outflow>burn</outflow>
+				<outflow>drift</outflow>
+				<non_negative/>
+				<units>tokens</units>
+			</stock>
+			<flow name="mint">
+				<dimensions>
+					<dim name="choice"/>
+				</dimensions>
+				<eqn>Tokens*new_interest*preference_noise</eqn>
+				<non_negative/>
+				<units>tokens/Blocks</units>
+			</flow>
+			<flow name="burn">
+				<dimensions>
+					<dim name="choice"/>
+				</dimensions>
+				<eqn>Tokens*loss_of_interest</eqn>
+				<non_negative/>
+				<units>tokens/Blocks</units>
+			</flow>
+			<flow name="exchange">
+				<dimensions>
+					<dim name="choice"/>
+				</dimensions>
+				<eqn>limbo*vol[choice,choice]*Tokens</eqn>
+				<units>tokens/Blocks</units>
+			</flow>
+			<flow name="drift">
+				<dimensions>
+					<dim name="choice"/>
+				</dimensions>
+				<eqn>.1*sentiment*(selection*Tokens+((selection-1)*Tokens)/SIZE(selection))</eqn>
+				<units>tokens/Blocks</units>
+			</flow>
+			<stock name="Conviction">
+				<dimensions>
+					<dim name="choice"/>
+				</dimensions>
+				<eqn>0</eqn>
+				<inflow>accumation</inflow>
+				<outflow>forgetting</outflow>
+				<non_negative/>
+			</stock>
+			<flow name="accumation">
+				<dimensions>
+					<dim name="choice"/>
+				</dimensions>
+				<eqn>Tokens</eqn>
+				<non_negative/>
+			</flow>
+			<flow name="forgetting">
+				<dimensions>
+					<dim name="choice"/>
+				</dimensions>
+				<eqn>Conviction[choice]*(1-forgetfulness)</eqn>
+				<non_negative/>
+			</flow>
+			<aux name="forgetfulness">
+				<eqn>.995</eqn>
+			</aux>
+			<aux name="preference noise">
+				<dimensions>
+					<dim name="choice"/>
+				</dimensions>
+				<eqn>UNIFORM(0, 2)</eqn>
+			</aux>
+			<aux name="loss of interest">
+				<eqn>.05</eqn>
+			</aux>
+			<aux name="new interest">
+				<eqn>.05</eqn>
+			</aux>
+			<aux name="vol">
+				<dimensions>
+					<dim name="choice"/>
+					<dim name="choice"/>
+				</dimensions>
+				<eqn>NORMAL(0, .05)</eqn>
+			</aux>
+			<stock name="limbo">
+				<eqn>0</eqn>
+				<outflow>exchange</outflow>
+				<non_negative/>
+				<units>tokens</units>
+			</stock>
+			<aux name="selection">
+				<dimensions>
+					<dim name="choice"/>
+				</dimensions>
+				<eqn>IF(Conviction[choice]=MAX(Conviction)) THEN 1 ELSE 0</eqn>
+			</aux>
+			<aux name="sentiment">
+				<eqn>UNIFORM(0, 1)*SIN(10*PI*TIME/(STOPTIME))/100</eqn>
+			</aux>
+			<aux name="sum of drift">
+				<eqn>SUM(drift)</eqn>
+			</aux>
+			<isee:dependencies>
+				<var name="mint">
+					<in>new_interest</in>
+					<in>Tokens</in>
+					<in>preference_noise</in>
+				</var>
+				<var name="burn">
+					<in>loss_of_interest</in>
+					<in>Tokens</in>
+				</var>
+				<var name="exchange">
+					<in>Tokens</in>
+					<in>vol</in>
+					<in>limbo</in>
+				</var>
+				<var name="drift">
+					<in>Tokens</in>
+					<in>sentiment</in>
+					<in>selection</in>
+				</var>
+				<var name="accumation">
+					<in>Tokens</in>
+				</var>
+				<var name="forgetting">
+					<in>Conviction</in>
+					<in>forgetfulness</in>
+				</var>
+				<var name="selection">
+					<in>Conviction</in>
+				</var>
+				<var name="sum_of_drift">
+					<in>drift</in>
+				</var>
+			</isee:dependencies>
+		</variables>
+		<views>
+			<style color="black" background="white" font_style="normal" font_weight="normal" text_decoration="none" text_align="center" vertical_text_align="center" font_color="black" font_family="Arial" font_size="10pt" padding="2" border_color="black" border_width="thin" border_style="none">
+				<text_box color="black" background="white" text_align="left" vertical_text_align="top" font_size="12pt"/>
+				<isee:loop_indicator color="black" background="white" text_align="left" vertical_text_align="top" font_size="12pt"/>
+				<numeric_display color="blue" background="white" font_size="9pt" isee:transparent="false"/>
+				<graph color="black" background="white" font_size="12pt" axis_color="#666666" grid_color="#C8C8C8" isee:graph_area_color="white" legend_position="bottom" isee:transparent="false" isee:hide_border="false">
+					<isee:series_styles>
+						<isee:series_style color="blue" thickness="1"/>
+						<isee:series_style color="red" thickness="1" pen_style="dot_dashed"/>
+						<isee:series_style color="fuchsia" thickness="1" pen_style="dotted"/>
+						<isee:series_style color="#008F44" thickness="1" pen_style="dashed"/>
+						<isee:series_style color="#FF7F00" thickness="1"/>
+						<isee:series_style color="#7F00FF" thickness="1" pen_style="dot_dashed"/>
+						<isee:series_style color="#0CA0FF" thickness="1" pen_style="dotted"/>
+						<isee:series_style color="lime" thickness="1" pen_style="dashed"/>
+						<isee:series_style color="#FF007F" thickness="1"/>
+						<isee:series_style color="aqua" thickness="1" pen_style="dot_dashed"/>
+						<isee:series_style color="#F586FF" thickness="1" pen_style="dotted"/>
+						<isee:series_style color="black" thickness="1" pen_style="dashed"/>
+						<isee:series_style color="#C8C8C8" thickness="1"/>
+					</isee:series_styles>
+				</graph>
+				<table color="black" background="#E0E0E0" text_align="right" font_size="12pt" orientation="vertical" wrap_text="false" isee:auto_fit="true" isee:use_alternate_row_colors="false" isee:unlimited_table_length="false" blank_column_width="80" column_width="160" interval="1" report_balances="beginning" report_flows="instantaneous"/>
+				<button color="black" background="#E0E0E0" font_size="12pt" transparent="false" corner_radius="0" isee:flat="false" icon_side="top"/>
+				<isee:annotation color="black" background="#E0E0E0" font_size="12pt" transparent="false" corner_radius="0" isee:flat="false" icon_side="top" popup_background_color="#FFFECF"/>
+				<slider color="black" background="#E0E0E0" font_size="12pt" num_ticks="3" label_side="top" wrap_title="true" isee:always_show_hover_tip="false" input_expands="true" input_width="100"/>
+				<isee:sim_speed_slider color="black" background="white"/>
+				<isee:time_slider color="#E95F74" background="silver"/>
+				<isee:pie_input color="black" background="white" font_size="12pt" legend_position="bottom" isee:transparent="false"/>
+				<knob color="#66CC66" background="#E0E0E0" font_size="12pt" label_side="bottom" wrap_title="true"/>
+				<numeric_input color="black" background="#E0E0E0" font_size="12pt" label_side="left" wrap_title="true" input_expands="false" input_width="100"/>
+				<switch color="black" background="#E0E0E0" font_size="12pt" label_side="top" wrap_title="true" isee:appearance="switch"/>
+				<options color="black" background="white" appearance="radio-buttons" arrangement="vertical"/>
+				<graphical_input color="black" background="#E0E0E0" font_size="12pt"/>
+				<group_input color="black" background="#E0E0E0" font_size="12pt"/>
+				<lamp color="black" background="white" font_size="9pt"/>
+				<gauge color="black" background="white" font_size="9pt" needle_color="#F09F72" bar_color="#EEEEEE" bar_progress_color="black" appearance="radial" num_major_ticks="11" num_minor_ticks="11" show_numeric_value="true"/>
+				<isee:spatial_map color="black" background="white" font_size="12pt"/>
+				<isee:animation_object color="black" background="white" font_size="12pt"/>
+			</style>
+			<view isee:show_pages="false" page_width="822" page_height="575" isee:page_cols="3" isee:page_rows="2" isee:scroll_x="120" isee:scroll_y="55" isee:popup_graphs_are_comparative="true" type="stock_flow">
+				<style color="black" background="white" font_style="normal" font_weight="normal" text_decoration="none" text_align="center" vertical_text_align="center" font_color="black" font_family="Arial" font_size="10pt" padding="2" border_color="black" border_width="thin" border_style="none">
+					<stock color="blue" background="white" font_color="blue" font_size="9pt" label_side="top">
+						<shape type="rectangle" width="45" height="35"/>
+					</stock>
+					<flow color="blue" background="white" font_color="blue" font_size="9pt" label_side="bottom"/>
+					<module color="blue" background="white" font_color="blue" font_size="9pt" label_side="top">
+						<shape type="rectangle" width="55" height="45"/>
+					</module>
+					<aux color="blue" background="white" font_color="blue" font_size="9pt" label_side="bottom">
+						<shape type="circle" radius="18"/>
+					</aux>
+					<group color="red" background="white" font_color="red" font_size="9pt"/>
+					<connector color="#FF007F" background="white" font_color="#FF007F" font_size="9pt" isee:thickness="1"/>
+					<text_box color="black" background="white" text_align="left" vertical_text_align="top" font_size="12pt"/>
+					<isee:loop_indicator color="black" background="white" text_align="left" vertical_text_align="top" font_size="12pt"/>
+					<numeric_display color="blue" background="white" font_size="9pt" isee:transparent="false"/>
+					<graph color="black" background="white" font_size="12pt" axis_color="#666666" grid_color="#C8C8C8" isee:graph_area_color="white" legend_position="bottom" isee:transparent="false" isee:hide_border="false">
+						<isee:series_styles>
+							<isee:series_style color="blue" thickness="1"/>
+							<isee:series_style color="red" thickness="1" pen_style="dot_dashed"/>
+							<isee:series_style color="fuchsia" thickness="1" pen_style="dotted"/>
+							<isee:series_style color="#008F44" thickness="1" pen_style="dashed"/>
+							<isee:series_style color="#FF7F00" thickness="1"/>
+							<isee:series_style color="#7F00FF" thickness="1" pen_style="dot_dashed"/>
+							<isee:series_style color="#0CA0FF" thickness="1" pen_style="dotted"/>
+							<isee:series_style color="lime" thickness="1" pen_style="dashed"/>
+							<isee:series_style color="#FF007F" thickness="1"/>
+							<isee:series_style color="aqua" thickness="1" pen_style="dot_dashed"/>
+							<isee:series_style color="#F586FF" thickness="1" pen_style="dotted"/>
+							<isee:series_style color="black" thickness="1" pen_style="dashed"/>
+							<isee:series_style color="#C8C8C8" thickness="1"/>
+						</isee:series_styles>
+					</graph>
+					<table color="black" background="#E0E0E0" text_align="right" font_size="12pt" orientation="vertical" wrap_text="false" isee:auto_fit="true" isee:use_alternate_row_colors="false" isee:unlimited_table_length="false" blank_column_width="80" column_width="160" interval="1" report_balances="beginning" report_flows="instantaneous"/>
+				</style>
+				<stock label_side="center" x="347" y="224" name="Tokens"/>
+				<flow x="253.25" y="227" name="mint">
+					<pts>
+						<pt x="182" y="227"/>
+						<pt x="324.5" y="227"/>
+					</pts>
+				</flow>
+				<flow x="414.25" y="227" name="burn">
+					<pts>
+						<pt x="369.5" y="227"/>
+						<pt x="483" y="227"/>
+					</pts>
+				</flow>
+				<flow label_side="right" x="347" y="151.5" name="exchange">
+					<pts>
+						<pt x="347" y="96.5"/>
+						<pt x="347" y="206.5"/>
+					</pts>
+				</flow>
+				<flow label_side="left" label_angle="225" x="344" y="289.25" name="drift">
+					<pts>
+						<pt x="344" y="241.5"/>
+						<pt x="344" y="346"/>
+					</pts>
+				</flow>
+				<stock label_side="center" x="344" y="437" name="Conviction"/>
+				<flow x="252.875" y="437" name="accumation">
+					<pts>
+						<pt x="184.25" y="437"/>
+						<pt x="321.5" y="437"/>
+					</pts>
+				</flow>
+				<flow x="422.75" y="437" name="forgetting">
+					<pts>
+						<pt x="366.5" y="437"/>
+						<pt x="503" y="437"/>
+					</pts>
+				</flow>
+				<aux x="450.25" y="492" name="forgetfulness"/>
+				<connector uid="1" angle="32.4712">
+					<from>Conviction</from>
+					<to>forgetting</to>
+				</connector>
+				<connector uid="2" angle="65.0485">
+					<from>forgetfulness</from>
+					<to>forgetting</to>
+				</connector>
+				<connector uid="3" angle="225">
+					<from>Tokens</from>
+					<to>accumation</to>
+				</connector>
+				<aux x="134" y="106.5" name="preference noise"/>
+				<aux x="501" y="139.5" name="loss of interest"/>
+				<connector uid="4" angle="193.285">
+					<from>loss_of_interest</from>
+					<to>burn</to>
+				</connector>
+				<connector uid="5" angle="24.2277">
+					<from>Tokens</from>
+					<to>burn</to>
+				</connector>
+				<aux x="144" y="267" name="new interest"/>
+				<connector uid="6" angle="356.186">
+					<from>new_interest</from>
+					<to>mint</to>
+				</connector>
+				<connector uid="7" angle="151.991">
+					<from>Tokens</from>
+					<to>mint</to>
+				</connector>
+				<connector uid="8" angle="349.38">
+					<from>preference_noise</from>
+					<to>mint</to>
+				</connector>
+				<connector uid="9" angle="120.964">
+					<from>Tokens</from>
+					<to>exchange</to>
+				</connector>
+				<connector uid="10" angle="296.03">
+					<from>Tokens</from>
+					<to>drift</to>
+				</connector>
+				<aux x="446" y="79" name="vol"/>
+				<connector uid="11" angle="191.31">
+					<from>vol</from>
+					<to>exchange</to>
+				</connector>
+				<stock x="347" y="79" name="limbo"/>
+				<aux x="440.75" y="388" name="selection"/>
+				<aux x="459" y="322" name="sentiment"/>
+				<connector uid="12" angle="49.6355">
+					<from>Conviction</from>
+					<to>selection</to>
+				</connector>
+				<connector uid="13" angle="126.027">
+					<from>sentiment</from>
+					<to>drift</to>
+				</connector>
+				<connector uid="14" angle="100.62">
+					<from>selection</from>
+					<to>drift</to>
+				</connector>
+				<stacked_container uid="15" x="833" y="648.5">
+					<graph x="658" y="523.5" width="350" height="250" type="time_series" show_grid="false" include_units_in_legend="false" plot_numbers="false" isee:label_pie_slices="false" num_x_grid_lines="0" num_y_grid_lines="0" num_x_labels="5" num_y_labels="3" title="Graph" isee:fill_intensity="0.1" left_axis_multi_scale="false" left_axis_auto_scale="true" right_axis_multi_scale="false" right_axis_auto_scale="true">
+						<plot color="blue" pen_width="1" index="0" show_y_axis="true">
+							<entity name="Tokens[1]"/>
+						</plot>
+						<plot color="red" pen_style="dot_dashed" pen_width="1" index="1" show_y_axis="true">
+							<entity name="Tokens[2]"/>
+						</plot>
+						<plot color="fuchsia" pen_style="dotted" pen_width="1" index="2" show_y_axis="true">
+							<entity name="Tokens[3]"/>
+						</plot>
+						<plot color="#008F44" pen_style="dashed" pen_width="1" index="3" show_y_axis="true">
+							<entity name="Tokens[4]"/>
+						</plot>
+						<plot color="#FF7F00" pen_width="1" index="4" show_y_axis="true">
+							<entity name="limbo"/>
+						</plot>
+					</graph>
+				</stacked_container>
+				<connector uid="16" angle="247.932">
+					<from>limbo</from>
+					<to>exchange</to>
+				</connector>
+				<stacked_container uid="17" x="833" y="148.5">
+					<graph x="658" y="23.5" width="350" height="250" type="area" show_grid="false" include_units_in_legend="false" plot_numbers="false" isee:label_pie_slices="false" num_x_grid_lines="0" num_y_grid_lines="0" num_x_labels="5" num_y_labels="3" title="Graph" isee:fill_intensity="0.1" isee:stacked_area_y_max="1.2" left_axis_multi_scale="false" left_axis_auto_scale="true" right_axis_multi_scale="false" right_axis_auto_scale="true">
+						<plot color="red" pen_style="dot_dashed" pen_width="1" index="0" show_y_axis="true">
+							<entity name="selection[1]"/>
+						</plot>
+						<plot color="fuchsia" pen_style="dotted" pen_width="1" index="1" show_y_axis="true">
+							<entity name="selection[2]"/>
+						</plot>
+						<plot color="blue" pen_width="1" index="2" show_y_axis="true">
+							<entity name="selection[3]"/>
+						</plot>
+						<plot color="#008F44" pen_width="1" index="3" show_y_axis="true">
+							<entity name="selection[4]"/>
+						</plot>
+					</graph>
+				</stacked_container>
+				<aux x="192" y="343" name="sum of drift"/>
+				<connector uid="18" angle="165.292">
+					<from>drift</from>
+					<to>sum_of_drift</to>
+				</connector>
+				<stacked_container uid="19" x="833" y="398.5">
+					<graph x="658" y="273.5" width="350" height="250" type="time_series" show_grid="false" include_units_in_legend="false" plot_numbers="false" isee:label_pie_slices="false" num_x_grid_lines="0" num_y_grid_lines="0" num_x_labels="5" num_y_labels="3" title="Graph" isee:fill_intensity="0.1" left_axis_multi_scale="false" left_axis_auto_scale="true" right_axis_multi_scale="false" right_axis_auto_scale="true">
+						<plot color="blue" pen_width="1" index="0" show_y_axis="true">
+							<entity name="Conviction[1]"/>
+						</plot>
+						<plot color="red" pen_style="dot_dashed" pen_width="1" index="1" show_y_axis="true">
+							<entity name="Conviction[2]"/>
+						</plot>
+						<plot color="fuchsia" pen_style="dotted" pen_width="1" index="2" show_y_axis="true">
+							<entity name="Conviction[3]"/>
+						</plot>
+						<plot color="#008F44" pen_style="dashed" pen_width="1" index="3" show_y_axis="true">
+							<entity name="Conviction[4]"/>
+						</plot>
+					</graph>
+				</stacked_container>
+				<stacked_container uid="20" x="1183" y="148.5">
+					<graph x="1008" y="23.5" width="350" height="250" type="bar" show_grid="false" include_units_in_legend="false" plot_numbers="false" isee:label_pie_slices="false" num_x_grid_lines="0" num_y_grid_lines="0" num_x_labels="5" num_y_labels="3" title="Graph" isee:fill_intensity="0.1" left_axis_multi_scale="false" left_axis_auto_scale="true" right_axis_multi_scale="false" right_axis_auto_scale="true">
+						<plot color="blue" pen_width="1" index="0" show_y_axis="true">
+							<entity name="preference_noise[1]"/>
+						</plot>
+						<plot color="red" pen_width="1" index="1" show_y_axis="true">
+							<entity name="preference_noise[2]"/>
+						</plot>
+						<plot color="fuchsia" pen_width="1" index="2" show_y_axis="true">
+							<entity name="preference_noise[3]"/>
+						</plot>
+						<plot color="#008F44" pen_width="1" index="3" show_y_axis="true">
+							<entity name="preference_noise[4]"/>
+						</plot>
+					</graph>
+				</stacked_container>
+				<stacked_container uid="21" x="1183" y="648.5">
+					<graph x="1008" y="523.5" width="350" height="250" type="time_series" show_grid="false" include_units_in_legend="false" plot_numbers="false" isee:label_pie_slices="false" num_x_grid_lines="0" num_y_grid_lines="0" num_x_labels="5" num_y_labels="3" title="Graph" isee:fill_intensity="0.1" left_axis_multi_scale="false" left_axis_auto_scale="true" right_axis_multi_scale="false" right_axis_auto_scale="true">
+						<plot color="blue" pen_width="1" index="0" show_y_axis="true"/>
+					</graph>
+				</stacked_container>
+				<stacked_container uid="22" x="1183" y="648.5">
+					<graph x="1008" y="523.5" width="350" height="250" type="time_series" show_grid="false" include_units_in_legend="false" plot_numbers="false" isee:label_pie_slices="false" num_x_grid_lines="0" num_y_grid_lines="0" num_x_labels="5" num_y_labels="3" title="Graph" isee:fill_intensity="0.1" left_axis_multi_scale="false" left_axis_auto_scale="true" right_axis_multi_scale="false" right_axis_auto_scale="true">
+						<plot color="blue" pen_width="1" index="0" show_y_axis="true">
+							<entity name="sentiment"/>
+						</plot>
+					</graph>
+				</stacked_container>
+				<stacked_container uid="23" x="1183" y="398.5">
+					<graph x="1008" y="273.5" width="350" height="250" type="time_series" show_grid="false" include_units_in_legend="false" plot_numbers="false" isee:label_pie_slices="false" num_x_grid_lines="0" num_y_grid_lines="0" num_x_labels="5" num_y_labels="3" title="Graph" isee:fill_intensity="0.1" left_axis_multi_scale="false" left_axis_auto_scale="true" right_axis_multi_scale="false" right_axis_auto_scale="true">
+						<plot color="blue" pen_width="1" index="0" show_y_axis="true">
+							<entity name="drift[1]"/>
+						</plot>
+						<plot color="red" pen_style="dot_dashed" pen_width="1" index="1" show_y_axis="true">
+							<entity name="drift[2]"/>
+						</plot>
+						<plot color="fuchsia" pen_style="dotted" pen_width="1" index="2" show_y_axis="true">
+							<entity name="drift[3]"/>
+						</plot>
+						<plot color="#008F44" pen_style="dashed" pen_width="1" index="3" show_y_axis="true">
+							<entity name="drift[4]"/>
+						</plot>
+					</graph>
+				</stacked_container>
+			</view>
+		</views>
+	</model>
+</xmile>
+