cadCAD/demos/verifiers_dilemma.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import timedelta\n",
    "\n",
    "from SimCAD import configs\n",
    "from SimCAD.configuration import Configuration\n",
    "from SimCAD.configuration.utils import exo_update_per_ts, proc_trigger, bound_norm_random, \\\n",
    "    ep_time_step\n",
    "\n",
    "seed = {\n",
    "}\n",
    "\n",
    "# Genesis States\n",
    "genesis_states = {\n",
    "    'Verifiers_On': True,\n",
    "    'Cheaters_On': False,\n",
    "    'Total_Volume': 100,\n",
    "    'Honest_Volume': 100,\n",
    "    'Cheats_Volume': 0,\n",
    "    'Cheats_Caught_Volume': 0,\n",
    "    'Verifiers_Cost': 0,\n",
    "    'Verifiers_Reward': 0,\n",
    "    'Cheaters_Cost': 0,\n",
    "    'Cheater_Reward': 0,\n",
    "    'timestamp': '2018-01-01 00:00:00'\n",
    "}\n",
    "\n",
    "# Verifier's cost per transaction verified\n",
    "alfa = 0.001\n",
    "def verifier_cost(s):\n",
    "    return alfa * (s['Total_Volume'])\n",
    "\n",
    "# Verifier's reward per cheat caught\n",
    "beta = 4 \n",
    "def verifier_reward(s):\n",
    "    return beta * s['Cheats_Volume']\n",
    "\n",
    "def verifier_expected_reward(s):\n",
    "    '''\n",
    "    We assume the existence of an off-chain signaling mechanism \n",
    "    by which potential verifiers become aware of some of cheating volume.\n",
    "    '''\n",
    "    off_chain_cheating_signal = 0.01\n",
    "    off_chain_expected_cheating = off_chain_cheating_signal * s['Cheats_Volume']\n",
    "    on_chain_expected_cheating = s['Cheats_Caught_Volume']\n",
    "    return beta * max([off_chain_expected_cheating, on_chain_expected_cheating])\n",
    "\n",
    "# Cheater's reward per transaction sent successfully\n",
    "gamma = 1\n",
    "def cheater_reward(s):\n",
    "    return gamma * (s['Cheats_Volume'])\n",
    "\n",
    "# Cheater's cost per cheat caught\n",
    "delta = 5\n",
    "def cheater_cost(s):\n",
    "    return delta * s['Cheats_Caught_Volume']\n",
    "\n",
    "# verifiers required expected profit threshold before verifying\n",
    "theta = .1\n",
    "\n",
    "\n",
    "# Behaviors\n",
    "def verifier(step, sL, s):\n",
    "    act = False\n",
    "    if (verifier_expected_reward(s) > (1+theta)*verifier_cost(s)):\n",
    "        act = True\n",
    "    return {'verifier': act}\n",
    "\n",
    "def cheater(step, sL, s):\n",
    "    act = not(s['Verifiers_On'])\n",
    "    return {'cheater': act}\n",
    "\n",
    "# Mechanisms\n",
    "def commit_resources_to_verifying(step, sL, s, _input):\n",
    "    y = 'Verifiers_On'\n",
    "    x = _input['verifier']\n",
    "    return (y, x)\n",
    "\n",
    "def commit_resources_to_cheating(step, sL, s, _input):\n",
    "    y = 'Cheaters_On'\n",
    "    x = _input['cheater']\n",
    "    return (y, x)\n",
    "\n",
    "mechanisms = {\n",
    "    'commit': {\n",
    "        'behaviors': {\n",
    "            'verifier': verifier,\n",
    "            'cheater': cheater\n",
    "        },\n",
    "        'states': { \n",
    "            'Verifiers_On': commit_resources_to_verifying,\n",
    "            'Cheaters_On': commit_resources_to_cheating            \n",
    "        }\n",
    "    }\n",
    "}\n",
    "\n",
    "# Environmental Processes\n",
    "epsilon = 1\n",
    "def volume_ep(step, sL, s, _input):\n",
    "    y = 'Total_Volume'\n",
    "    x = epsilon*s['Total_Volume']\n",
    "    return (y, x)\n",
    "\n",
    "zeta=0.2\n",
    "def cheat_volume_ep(step, sL, s, _input):\n",
    "    y = 'Cheats_Volume'\n",
    "    if (s['Cheaters_On']):\n",
    "        x = zeta*(s['Total_Volume'])\n",
    "    else:\n",
    "        x = 0\n",
    "    return (y, x)\n",
    "\n",
    "def honest_volume_ep(step, sL, s, _input):\n",
    "    y = 'Honest_Volume'\n",
    "    if (s['Cheaters_On']):\n",
    "        x = (1-zeta)*s['Total_Volume']\n",
    "    else:\n",
    "        x = s['Total_Volume']\n",
    "    return (y, x)\n",
    "\n",
    "def cheats_caught_ep(step, sL, s, _input):\n",
    "    y = 'Cheats_Caught_Volume'\n",
    "    if (s['Verifiers_On']):\n",
    "        x = s['Cheats_Volume']\n",
    "    else:\n",
    "        x = 0\n",
    "    return (y, x)\n",
    "\n",
    "def verifier_cost_ep(step, sL, s, _input):\n",
    "    y = 'Verifiers_Cost'\n",
    "    if (s['Verifiers_On']):\n",
    "        x = verifier_cost(s)\n",
    "    else:\n",
    "        x = 0\n",
    "    return (y, x)\n",
    "\n",
    "def verifier_reward_ep(step, sL, s, _input):\n",
    "    y = 'Verifiers_Reward'\n",
    "    if (s['Verifiers_On']):\n",
    "        x = verifier_reward(s)\n",
    "    else:\n",
    "        x = 0\n",
    "    return (y, x)\n",
    "\n",
    "def cheater_cost_ep(step, sL, s, _input):\n",
    "    y = 'Cheaters_Cost'\n",
    "    if (s['Verifiers_On']):\n",
    "        x = cheater_cost(s)\n",
    "    else:\n",
    "        x = 0\n",
    "    return (y, x)\n",
    "\n",
    "def cheater_reward_ep(step, sL, s, _input):\n",
    "    y = 'Cheater_Reward'\n",
    "    if (s['Cheaters_On']):\n",
    "        x = cheater_reward(s)\n",
    "    else:\n",
    "        x = 0\n",
    "    return (y, x)\n",
    "\n",
    "ts_format = '%Y-%m-%d %H:%M:%S'\n",
    "t_delta = timedelta(days=0, minutes=0, seconds=1)\n",
    "def time_model(step, sL, s, _input):\n",
    "    y = 'timestamp'\n",
    "    x = ep_time_step(s, dt_str=s['timestamp'], fromat_str=ts_format, _timedelta=t_delta)\n",
    "    return (y, x)\n",
    "\n",
    "\n",
    "\n",
    "# remove `exo_update_per_ts` to update every ts\n",
    "exogenous_states = exo_update_per_ts(\n",
    "    {\n",
    "    'Total_Volume': volume_ep,\n",
    "    'Honest_Volume': honest_volume_ep,\n",
    "    'Cheats_Volume': cheat_volume_ep,\n",
    "    'Cheats_Caught_Volume': cheats_caught_ep,\n",
    "    'Verifiers_Cost': verifier_cost_ep,\n",
    "    'Verifiers_Reward': verifier_reward_ep,\n",
    "    'Cheaters_Cost': cheater_cost_ep,\n",
    "    'Cheater_Reward': cheater_reward_ep,\n",
    "    'timestamp': time_model\n",
    "    }\n",
    ")\n",
    "\n",
    "env_processes = {\n",
    "}\n",
    "\n",
    "\n",
    "\n",
    "sim_config = {\n",
    "    'N': 1,\n",
    "    'T': range(100)\n",
    "}\n",
    "\n",
    "configs.append(\n",
    "    Configuration(\n",
    "        sim_config=sim_config,\n",
    "        state_dict=genesis_states,\n",
    "        seed=seed,\n",
    "        exogenous_states=exogenous_states,\n",
    "        env_processes=env_processes,\n",
    "        mechanisms=mechanisms\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Run the engine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "single_proc: [<SimCAD.configuration.Configuration object at 0x120dd51d0>]\n"
     ]
    }
   ],
   "source": [
    "from SimCAD.engine import ExecutionMode, ExecutionContext, Executor\n",
    "# from demos import simple_tracker_config\n",
    "from SimCAD import configs\n",
    "exec_mode = ExecutionMode()\n",
    "\n",
    "single_config = [configs[0]]\n",
    "single_proc_ctx = ExecutionContext(exec_mode.single_proc)\n",
    "run = Executor(single_proc_ctx, single_config)\n",
    "run_raw_result = run.main()[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyze the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x121dcc470>"
      ]
     },
     "execution_count": 3,
     "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": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from tabulate import tabulate\n",
    "result = pd.DataFrame(run_raw_result)\n",
    "result.plot('timestamp', \n",
    "            ['Total_Volume',\n",
    "             'Honest_Volume',\n",
    "             'Cheats_Volume',\n",
    "             'Cheats_Caught_Volume'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1220e62e8>"
      ]
     },
     "execution_count": 4,
     "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": [
    "result.plot('timestamp', ['Verifiers_Cost',\n",
    "             'Verifiers_Reward',\n",
    "             'Cheaters_Cost',\n",
    "             'Cheater_Reward'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Cheater_Reward</th>\n",
       "      <th>Cheaters_Cost</th>\n",
       "      <th>Cheaters_On</th>\n",
       "      <th>Cheats_Caught_Volume</th>\n",
       "      <th>Cheats_Volume</th>\n",
       "      <th>Honest_Volume</th>\n",
       "      <th>Total_Volume</th>\n",
       "      <th>Verifiers_Cost</th>\n",
       "      <th>Verifiers_On</th>\n",
       "      <th>Verifiers_Reward</th>\n",
       "      <th>mech_step</th>\n",
       "      <th>run</th>\n",
       "      <th>time_step</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2018-01-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-01-01 00:00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2018-01-01 00:00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>100</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2018-01-01 00:00:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>100</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2018-01-01 00:00:04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>100</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2018-01-01 00:00:05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>2018-01-01 00:00:06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2018-01-01 00:00:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>2018-01-01 00:00:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>2018-01-01 00:00:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>100</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2018-01-01 00:00:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>100</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>2018-01-01 00:00:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>100</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>2018-01-01 00:00:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>2018-01-01 00:00:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>2018-01-01 00:00:14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>2018-01-01 00:00:15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>2018-01-01 00:00:16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>100</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>2018-01-01 00:00:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>100</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>2018-01-01 00:00:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>100</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>2018-01-01 00:00:19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Cheater_Reward  Cheaters_Cost Cheaters_On  Cheats_Caught_Volume  \\\n",
       "0              0.0            0.0       False                   0.0   \n",
       "1              0.0            0.0           0                   0.0   \n",
       "2              0.0            0.0           1                   0.0   \n",
       "3              0.0            0.0           1                   0.0   \n",
       "4             20.0            0.0           1                   0.0   \n",
       "5             20.0            0.0           0                  20.0   \n",
       "6              0.0           60.0           0                  20.0   \n",
       "7              0.0           60.0           0                   0.0   \n",
       "8              0.0            0.0           0                   0.0   \n",
       "9              0.0            0.0           1                   0.0   \n",
       "10             0.0            0.0           1                   0.0   \n",
       "11            20.0            0.0           1                   0.0   \n",
       "12            20.0            0.0           0                  20.0   \n",
       "13             0.0           60.0           0                  20.0   \n",
       "14             0.0           60.0           0                   0.0   \n",
       "15             0.0            0.0           0                   0.0   \n",
       "16             0.0            0.0           1                   0.0   \n",
       "17             0.0            0.0           1                   0.0   \n",
       "18            20.0            0.0           1                   0.0   \n",
       "19            20.0            0.0           0                  20.0   \n",
       "\n",
       "    Cheats_Volume  Honest_Volume  Total_Volume  Verifiers_Cost Verifiers_On  \\\n",
       "0             0.0          100.0           100             0.0         True   \n",
       "1             0.0          100.0           100             0.1            0   \n",
       "2             0.0          100.0           100             0.0            0   \n",
       "3            20.0           80.0           100             0.0            0   \n",
       "4            20.0           80.0           100             0.0            1   \n",
       "5            20.0           80.0           100             0.1            1   \n",
       "6             0.0          100.0           100             0.1            1   \n",
       "7             0.0          100.0           100             0.1            1   \n",
       "8             0.0          100.0           100             0.1            0   \n",
       "9             0.0          100.0           100             0.0            0   \n",
       "10           20.0           80.0           100             0.0            0   \n",
       "11           20.0           80.0           100             0.0            1   \n",
       "12           20.0           80.0           100             0.1            1   \n",
       "13            0.0          100.0           100             0.1            1   \n",
       "14            0.0          100.0           100             0.1            1   \n",
       "15            0.0          100.0           100             0.1            0   \n",
       "16            0.0          100.0           100             0.0            0   \n",
       "17           20.0           80.0           100             0.0            0   \n",
       "18           20.0           80.0           100             0.0            1   \n",
       "19           20.0           80.0           100             0.1            1   \n",
       "\n",
       "    Verifiers_Reward  mech_step  run  time_step            timestamp  \n",
       "0                0.0          0    1          0  2018-01-01 00:00:00  \n",
       "1                0.0          1    1          1  2018-01-01 00:00:01  \n",
       "2                0.0          1    1          2  2018-01-01 00:00:02  \n",
       "3                0.0          1    1          3  2018-01-01 00:00:03  \n",
       "4                0.0          1    1          4  2018-01-01 00:00:04  \n",
       "5               80.0          1    1          5  2018-01-01 00:00:05  \n",
       "6               80.0          1    1          6  2018-01-01 00:00:06  \n",
       "7                0.0          1    1          7  2018-01-01 00:00:07  \n",
       "8                0.0          1    1          8  2018-01-01 00:00:08  \n",
       "9                0.0          1    1          9  2018-01-01 00:00:09  \n",
       "10               0.0          1    1         10  2018-01-01 00:00:10  \n",
       "11               0.0          1    1         11  2018-01-01 00:00:11  \n",
       "12              80.0          1    1         12  2018-01-01 00:00:12  \n",
       "13              80.0          1    1         13  2018-01-01 00:00:13  \n",
       "14               0.0          1    1         14  2018-01-01 00:00:14  \n",
       "15               0.0          1    1         15  2018-01-01 00:00:15  \n",
       "16               0.0          1    1         16  2018-01-01 00:00:16  \n",
       "17               0.0          1    1         17  2018-01-01 00:00:17  \n",
       "18               0.0          1    1         18  2018-01-01 00:00:18  \n",
       "19              80.0          1    1         19  2018-01-01 00:00:19  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "result[\"Cumulative_Cheating_Volume\"]= result['Cheats_Volume'].cumsum()\n",
    "result[\"Cumulative_Cheating_Rewards\"]= result['Cheater_Reward'].cumsum()\n",
    "\n",
    "result[\"Cumulative_Verifiers_Cost\"]= result['Verifiers_Cost'].cumsum()\n",
    "result[\"Cumulative_Verifier_Rewards\"]= result['Verifiers_Reward'].cumsum()\n",
    "\n",
    "result[\"Cumulative_Cheating_Penalties\"]= result['Cheaters_Cost'].cumsum()\n",
    "result[\"Cumulative_Cheating_Profit\"] = result[\"Cumulative_Cheating_Rewards\"]-result[\"Cumulative_Cheating_Penalties\"]\n",
    "\n",
    "result[\"Cumulative_Verifier_Profit\"] = result[\"Cumulative_Verifier_Rewards\"]-result[\"Cumulative_Verifiers_Cost\"]\n",
    "result[\"Cumulative_Verifier_ROI\"] = result[\"Cumulative_Verifier_Profit\"]/result[\"Cumulative_Verifiers_Cost\"]\n",
    "\n",
    "result[\"Running_Cheating_Volume_Fraction\"]=result[\"Cumulative_Cheating_Volume\"]/result[\"Total_Volume\"].cumsum()\n",
    "\n",
    "result[\"Cumulative_Net_Profit\"] = result[\"Cumulative_Verifier_Profit\"] + result[\"Cumulative_Cheating_Profit\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1221ebc18>"
      ]
     },
     "execution_count": 21,
     "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": [
    "result[['Cumulative_Cheating_Profit', \"Cumulative_Verifier_Profit\", \"Cumulative_Net_Profit\"]].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.084\n"
     ]
    },
    {
     "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": [
    "result[\"Running_Cheating_Volume_Fraction\"].plot()\n",
    "h = result[\"Running_Cheating_Volume_Fraction\"].median()\n",
    "ax = plt.axis()\n",
    "plt.hlines(h, ax[0], ax[1], 'r')\n",
    "print(h)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "390.83673469387764\n"
     ]
    },
    {
     "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": [
    "result[\"Cumulative_Verifier_ROI\"].plot()\n",
    "h = result[\"Cumulative_Verifier_ROI\"].median()\n",
    "ax = plt.axis()\n",
    "plt.hlines(h, ax[0], ax[1], 'r')\n",
    "print(h)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Cheaters_On</th>\n",
       "      <th colspan=\"2\" halign=\"left\">False</th>\n",
       "      <th colspan=\"2\" halign=\"left\">1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Verifiers_On</th>\n",
       "      <th>0</th>\n",
       "      <th>True</th>\n",
       "      <th>0</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">Cheater_Reward</th>\n",
       "      <th>count</th>\n",
       "      <td>15.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.511628</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.482746</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">Cheaters_Cost</th>\n",
       "      <th>count</th>\n",
       "      <td>15.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>39.069767</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>28.934569</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">Cheats_Caught_Volume</th>\n",
       "      <th>count</th>\n",
       "      <td>15.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>13.023256</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.644856</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">Cheats_Volume</th>\n",
       "      <th>count</th>\n",
       "      <td>15.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.511628</td>\n",
       "      <td>9.655172</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.482746</td>\n",
       "      <td>10.170953</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">Verifiers_Reward</th>\n",
       "      <th>std</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>38.579426</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">mech_step</th>\n",
       "      <th>count</th>\n",
       "      <td>15.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.976744</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.152499</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">run</th>\n",
       "      <th>count</th>\n",
       "      <td>15.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">time_step</th>\n",
       "      <th>count</th>\n",
       "      <td>15.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.302326</td>\n",
       "      <td>49.793103</td>\n",
       "      <td>49.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>31.304952</td>\n",
       "      <td>29.301824</td>\n",
       "      <td>29.828533</td>\n",
       "      <td>29.283101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>25.500000</td>\n",
       "      <td>26.500000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>26.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>49.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>74.500000</td>\n",
       "      <td>75.500000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>72.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>99.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>95.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>160 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Cheaters_On                     False                      1           \n",
       "Verifiers_On                        0       True           0       True\n",
       "Cheater_Reward       count  15.000000  43.000000   29.000000  14.000000\n",
       "                     mean    0.000000   6.511628    0.000000  20.000000\n",
       "                     std     0.000000   9.482746    0.000000   0.000000\n",
       "                     min     0.000000   0.000000    0.000000  20.000000\n",
       "                     25%     0.000000   0.000000    0.000000  20.000000\n",
       "                     50%     0.000000   0.000000    0.000000  20.000000\n",
       "                     75%     0.000000  20.000000    0.000000  20.000000\n",
       "                     max     0.000000  20.000000    0.000000  20.000000\n",
       "Cheaters_Cost        count  15.000000  43.000000   29.000000  14.000000\n",
       "                     mean    0.000000  39.069767    0.000000   0.000000\n",
       "                     std     0.000000  28.934569    0.000000   0.000000\n",
       "                     min     0.000000   0.000000    0.000000   0.000000\n",
       "                     25%     0.000000   0.000000    0.000000   0.000000\n",
       "                     50%     0.000000  60.000000    0.000000   0.000000\n",
       "                     75%     0.000000  60.000000    0.000000   0.000000\n",
       "                     max     0.000000  60.000000    0.000000   0.000000\n",
       "Cheats_Caught_Volume count  15.000000  43.000000   29.000000  14.000000\n",
       "                     mean    0.000000  13.023256    0.000000   0.000000\n",
       "                     std     0.000000   9.644856    0.000000   0.000000\n",
       "                     min     0.000000   0.000000    0.000000   0.000000\n",
       "                     25%     0.000000   0.000000    0.000000   0.000000\n",
       "                     50%     0.000000  20.000000    0.000000   0.000000\n",
       "                     75%     0.000000  20.000000    0.000000   0.000000\n",
       "                     max     0.000000  20.000000    0.000000   0.000000\n",
       "Cheats_Volume        count  15.000000  43.000000   29.000000  14.000000\n",
       "                     mean    0.000000   6.511628    9.655172  20.000000\n",
       "                     std     0.000000   9.482746   10.170953   0.000000\n",
       "                     min     0.000000   0.000000    0.000000  20.000000\n",
       "                     25%     0.000000   0.000000    0.000000  20.000000\n",
       "                     50%     0.000000   0.000000    0.000000  20.000000\n",
       "...                               ...        ...         ...        ...\n",
       "Verifiers_Reward     std     0.000000  38.579426    0.000000   0.000000\n",
       "                     min     0.000000   0.000000    0.000000   0.000000\n",
       "                     25%     0.000000   0.000000    0.000000   0.000000\n",
       "                     50%     0.000000  80.000000    0.000000   0.000000\n",
       "                     75%     0.000000  80.000000    0.000000   0.000000\n",
       "                     max     0.000000  80.000000    0.000000   0.000000\n",
       "mech_step            count  15.000000  43.000000   29.000000  14.000000\n",
       "                     mean    1.000000   0.976744    1.000000   1.000000\n",
       "                     std     0.000000   0.152499    0.000000   0.000000\n",
       "                     min     1.000000   0.000000    1.000000   1.000000\n",
       "                     25%     1.000000   1.000000    1.000000   1.000000\n",
       "                     50%     1.000000   1.000000    1.000000   1.000000\n",
       "                     75%     1.000000   1.000000    1.000000   1.000000\n",
       "                     max     1.000000   1.000000    1.000000   1.000000\n",
       "run                  count  15.000000  43.000000   29.000000  14.000000\n",
       "                     mean    1.000000   1.000000    1.000000   1.000000\n",
       "                     std     0.000000   0.000000    0.000000   0.000000\n",
       "                     min     1.000000   1.000000    1.000000   1.000000\n",
       "                     25%     1.000000   1.000000    1.000000   1.000000\n",
       "                     50%     1.000000   1.000000    1.000000   1.000000\n",
       "                     75%     1.000000   1.000000    1.000000   1.000000\n",
       "                     max     1.000000   1.000000    1.000000   1.000000\n",
       "time_step            count  15.000000  43.000000   29.000000  14.000000\n",
       "                     mean   50.000000  50.302326   49.793103  49.500000\n",
       "                     std    31.304952  29.301824   29.828533  29.283101\n",
       "                     min     1.000000   0.000000    2.000000   4.000000\n",
       "                     25%    25.500000  26.500000   24.000000  26.750000\n",
       "                     50%    50.000000  49.000000   51.000000  49.500000\n",
       "                     75%    74.500000  75.500000   73.000000  72.250000\n",
       "                     max    99.000000  98.000000  100.000000  95.000000\n",
       "\n",
       "[160 rows x 4 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data =result.groupby(['Cheaters_On', 'Verifiers_On']).describe()\n",
    "data.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>Cheaters_On</th>\n",
       "      <th colspan=\"2\" halign=\"left\">False</th>\n",
       "      <th colspan=\"2\" halign=\"left\">1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Verifiers_On</th>\n",
       "      <th>0</th>\n",
       "      <th>True</th>\n",
       "      <th>0</th>\n",
       "      <th>True</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>15.0</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>29.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.0</td>\n",
       "      <td>6.511628</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>9.482746</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.0</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Cheaters_On  False                1      \n",
       "Verifiers_On     0       True     0  True\n",
       "count         15.0  43.000000  29.0  14.0\n",
       "mean           0.0   6.511628   0.0  20.0\n",
       "std            0.0   9.482746   0.0   0.0\n",
       "min            0.0   0.000000   0.0  20.0\n",
       "25%            0.0   0.000000   0.0  20.0\n",
       "50%            0.0   0.000000   0.0  20.0\n",
       "75%            0.0  20.000000   0.0  20.0\n",
       "max            0.0  20.000000   0.0  20.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Cheater_Reward.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "                          count       mean       std  min  25%   50%   75%  \\\n",
       "Cheaters_On Verifiers_On                                                     \n",
       "False       0              15.0   0.000000  0.000000  0.0  0.0   0.0   0.0   \n",
       "            True           43.0  13.023256  9.644856  0.0  0.0  20.0  20.0   \n",
       "1           0              29.0   0.000000  0.000000  0.0  0.0   0.0   0.0   \n",
       "            True           14.0   0.000000  0.000000  0.0  0.0   0.0   0.0   \n",
       "\n",
       "                           max  \n",
       "Cheaters_On Verifiers_On        \n",
       "False       0              0.0  \n",
       "            True          20.0  \n",
       "1           0              0.0  \n",
       "            True           0.0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Cheats_Caught_Volume"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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