diff --git a/demos/verifiers_dilemma.ipynb b/demos/verifiers_dilemma.ipynb
index 1bc4dfd..4c7f387 100644
--- a/demos/verifiers_dilemma.ipynb
+++ b/demos/verifiers_dilemma.ipynb
@@ -53,7 +53,7 @@
     "    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 = 1\n",
+    "    off_chain_cheating_signal = 0.1\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",
@@ -231,7 +231,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "single_proc: [<SimCAD.configuration.Configuration object at 0x107f09940>]\n"
+      "single_proc: [<SimCAD.configuration.Configuration object at 0x111e90f98>]\n"
      ]
     }
    ],
@@ -256,7 +256,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 28,
    "metadata": {
     "scrolled": false
    },
@@ -264,26 +264,29 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x108d95be0>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x113d81c18>"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "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",
@@ -302,7 +305,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x108e7b588>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x11318af28>"
       ]
      },
      "execution_count": 4,
@@ -311,12 +314,14 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -791,6 +796,890 @@
     "result.head(20)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result[\"Cumulative_Cheating_Volume\"]= result['Cheats_Volume'].cumsum()\n",
+    "result[\"Cumulative_Cheating_Rewards\"]= result['Cheater_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[\"Running_Cheating_Volume_Fraction\"]=result[\"Cumulative_Cheating_Volume\"]/result[\"Total_Volume\"].cumsum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x113b722e8>"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "result.Cumulative_Cheating_Volume.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.084\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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52+PSeH/NM0ccv2Xk5WxJGMAnq560XNZ9eefxZv/RrCr+G5F8+5rWR0RTNOXHXL5/I/+3411LZf0l6yT+nH0Ky1c8xgDHkf3H76QV8MNRl/Po1je57ID/GTgvZk7gvuEX8O91LzC5rn1Xzk/yL+ad9JG8v/pphvr53b2bls+to67gz1sXcfmBje0er46K54IJN5HmaGTh2heIM74/QaxIGsJV477LneVf8NPyLzs8x3utV1at44/b3kY6PAu2xPfn4vGzuKh6M38peavDc5wId+VfzJsDxvDrHe9xQ+WqDs8ri03hovGzyG88wCsbXiLadPzmuis2le+OmcHB6AT+sXkBJx9qPwZRGZ3IpeNvJMblYOG6F0h3+P40+mrGWO7Lu4AcWw1PbV5AXtORbybVUfFcPfY69kcn8Nr6FxnZ6LtRUh8RzZzhF/DmgDGcdXAbj5QsJtX57Sem2shYrh/zHTYnDGDe5n9zZu1On2UBrEkcyF35l7AjLo1bKpbz87LPiTXugK2LiOaWUVfwVUouD+14nxsrV3ZalhPhuUGT+X3OaUQaw5zST7hu32oigMaIKO7Mv4R30wu4uaKY+3Z9fMS/pY5URifym9yzeHPAGIY21fDAzg84pyaEe1o+/jjop4rICmNMkb/ztKUfoJqoOFIcR37kz7G5/4GUxqYEVNa2+HRGNLXvxjnhUBm74tLYG53UwbM6tjJpCJPqKtr9kSa6mrmguoS3+o/CJpGWyno/LZ/JdXvaBT7A+Qe3MqLxAI8NOcHPPwe3lzPHM7KhikkdBD7Afbs+Icq4+HXuWZ2WY4C5WSeQ01TDJT7ebNIdjfxh29tsScjgt7lndFrWb3LPItNex217lvs8b9rBrfy07HMWZIzjsSEndHiOE+Ge4dNIdtq4f+eHPsuKxPCnbYs5t7qEX+adxysZ49qd0ywR/Ff+JYDh0ZI3fQY+QK6thtfWv8QQ2yFuGn01S9Lyj3jcJpHcNvJy6iJjeHLzvzsNfIAZVet5YeMrHIyK5/Jx1/Nlv5yWxxojorh51JXsjk3m6U0LOg18cP/NPVqyiId2vM/nKcO4eMKNfJPknhBQFxHNTaOvZlNCBo9vecNv4ANMqK9k0dp/ct2+1cwbMpXp425gQ0IGtZGx3DDmOyzrl8MjJW/5DXxwvw7f3/sNS9Y8y8T6vfxi+PlcU3gty5OzuG7MNbyXls8DOz/gl7s+8hv4AAOb6/lrySJe2PAKUcbJzaOv4uZRV7ArNtXvc7uNMaZH/TdlyhTTk53yuw/MXfNXHnGs9EC9yZ29yLy0dJflchxOl8m/7y3z28Ub2j22pqzG5M5eZP6zstxSWXVNzSZvziLzx3c3d/j4R5sqTe7sRebttRV+y6qoaTS5sxeZuR9t9XnOq8VlJnf2IvPBxr2dlrWxotbkzl5knvxse6fnzf1oq8mdvci8v8F3eV9srTK5sxeZ57/a2fkFGGN+/eb6Tq934ardJnf2IvPK8lK/ZblcLnPnS9+Y3NmLzJurd7d7/OnPtwf0WjXaHeb6J782w+a0f87v39lkcmcvMotW77FUljHGVNfZzGV/+9zkzVlk5i/79u9vzuurTe7sRWbxGutlGWPMrv315tw/fmyG3/uWefaLHcbhdJkfPLfcDJtj7e+nrZWlB80pv/vAjLj3LfP3j0vMjMe/NMPvfSuosowx5sONlaboN++Z/PveMqc9/KEpuG9x0GW5XC7zyvJSM+HBJSZ39iIz8n+CL8sYY2zNTvP4xyWm8Jdvm1N+94FxOF1BlxUMoNhYyFht6QeotqG5XZ/t4JQ4oiKE0gCm0lXUNtLsNOSmJ7Z7rHBIP5Jjo1hqcTB3TXktLgOTh3bcujg1fwADkmL4z0r/A7AfbKoE4FzPrJ2OTJ80hKzUeB79oPNZHy8vLyM6UrhiclanP/MHpw6nIDOJ+99Y73NQ97GPt5GRHMvVU7I7fLy1e6aNZnxWCve8trrdNE6bw8nD72yicHC/IwaWfRERfnfVBIpy0/j5K6uPmKddUdvIH5Zs5sxRGVw2cYjfsgDioiOZd0MRJ+Sl89OXV/HmavfMquU7q3ns4xJmTMnm4gmDLZUFkJYYw4s/OIFTCzKY/fpa/vbhVl5aVspLy8r48ZkjWqbcWjW0fwILfnwyZ3mmYV74l095b0MlD1xSyLQ2YzxWTMpJ5a2fnMY5YzL53dubKN5ZzSPXTAqqLICzRmfy7l2nc/7YQRyos/GPWUVBlyUizCjK4f2fncFtpw9n/q0nBl0WQExUBLedMYIP7z6TR66ZRGSErw7B7qWhH4Bmp4vDNkfLHH2vqMgIhqTGUxbADJ6yave5Q1sN4npFRghFw9JYut3aYK53x61J2R2HflRkBJdOHMKHm/b5nQXx/oZKhqYnUJDpu2spOjKCO87OZ1VZDR9vrurwHJvDyb9X7ub8sYNIbzPg11ZMVAS/vXI8u2sa+cv7W9s9vqa8hs9L9vODU/OIi/bfRRUTFcHfrpuMy8Cd81cdMSvlX1+XUn6wkXsvGm35H2VcdCRP3DCFQSlx/OC5Ynbud8/WeHDhepzG8Ovp444YIPUnPiaSp2YdT9GwdO6cv5KXlpVy1/xV7llIl421XI5XYmwUT95YxBWTs/jDu1u4799rOX1kBj8/f1TAZQEkx0Uz74Yibj9rBFsq67jltDxuOiUvqLLAPVPo8eun8IcZE3nihiLLb5C+pCXGMPe641j1wPmc0WYAOBgZybHce9EYJg9NC7ksgIH94ji+1Sy6nsZS6IvINBHZLCIlIjKng8djReRlz+NLRWSY53i0iDwnImtFZKOI3Nu11T+2DnkXW2vT0gfISY8PqKXvvcGmo9AHOGF4f7ZVuZdV8Gdl6UHyBiS2m03R2pWTs7E7XSxc7bu132B38MW2A5w7ZqDfELt6SjY56fH86b0tHbb2311fSU1DMzOPz+ng2e0dPyyda4pyePLzHWysOHL2zbxPt5McG8V1Jwy1VBZAbv9EfnvleFbsOsj/8yz5cLipmb99VMKp+QM4rSCwsOifFMuz35uKMYZZzyzjleVlLFlfyU/OKThi9pVVibFRPHPT8RTlpnPvgrXsPdTEI9dMOuL+j0DEREXwxxkTuf2sERw3NI1HZ4bW0oyIEP77gtEsu+8c7rtoTNDleIkIV0/Jbtk3oitER2qbNRh+f2siEgnMBS4ECoFrRaSwzWk3AweNMfnAI8DDnuMzgFhjzHhgCnCb9w2hN/p2hc324To0PYHyAEK/tLqBCIHBqXEdPj7V4nx9Ywwry2qYnNP5wNG4rH4UDu7HS8vKfHbJfLZ1P3aHi3PHZPqtf3RkBP91dgFrd9fy/sZ97R5/bUU5WanxnDJigN+yvO69aDSp8dHMWbC25Q7W8oMNvL1uL9eeMJTkuPZvtp25bOIQbjgxl398toN31lXwj0+3U11vZ3abKZpW5Q1I5KmbjmdvbRP3vL6GUQOTueW04UGVBZ7g/97xXDJhMA9eWshxIbY0vUH9+o9O7vBvNBiZ/eIC+hSjej4rb5VTgRJjzHZjjB2YD0xvc8504DnP168B54j7L8UAiSISBcQDdqBrVnrqBi3r7iS0D5/stAQO1Nupt7iZSml1A0NS4322VsZnpZAQE+m3i2d3TSNVh21M8tGf7yUiXDs1hw0Vh3yuvPn+hkqS46JabhDz58rJWeT2T+BP7205Yj72vkNNfLa1iismZx0xX9qf1IQY7r+0kNVlNTzzhXtxt2e+2IkAN3luKgrULy4Zw8ScVO5+dQ1Pfr6DiycMblnYLhjHDU3jr9dOZkhKHL+9cnzIrc3E2Cj+dt1x3HDSsJDKUcoqK3+xWUDrScDlnmMdnmOMcQC1QH/cbwD1QAVQCvzBGBPYraY9iLc/vO1ALnzbTVNmcf2XsoMNPrt2wN2SnpKbxtfbO/91rfTstDU5x38rcfrkLOKiI5i/vP2cbpfL8NHmfZw5KtNykEVFRvCTswvYWHGId9Z/u5rnG6v24DJwxXGdD+B25LKJQzh3jPsmqzXlNcxfVsolEwYzJDU+4LLAfQPS3OsmExUp2B0u7g6yn7u188cO4os5ZzMlt2v6gJU6lqz86+6oqda2f8DXOVMBJzAEyAN+LiLtPg+LyK0iUiwixVVVHQ8M9gSHPa34fnHt+129/breAVp/yqo7D32Ak0cMYHPlYSo7WQpgVVkNsVERjB6c7Pdn9ouL5uLxQ1i4ak+7TyTr9tSyv87O2aMD6+uePmkIBZlJ/H7J5pYB0wUrdzMxJ/WINYWsEhH+94pxxERFcO28r6m3O/lBCF0o4P4U9uIPTmTejVPIG9B+tlQwtMtD9VZWQr8caD0alw20XcGr5RxPV04KUA1cB7xjjGk2xuwDvgDa3TFmjJlnjCkyxhRlZIQ+Gn+0NHiCMiGmfeh7A9zKYG69zcH+OrvfAUDvzIRPt/h+I1xZepDxWSmWW+czp+ZQZ3Pw1pojb5by/oxABzijIiO4Z9poduyv5+XlZWysOMTGikNcFUQr32tgvzh+eUkh9XYnJ4/oz7is4LtjvAqH9OPs0V03iKhUb2UlKZYDBSKSJyIxwExgYZtzFgKzPF9fDXzouVmgFDhb3BKBE4HQds/oRvV2963fCTHtpw2mJUSTGBNpadlbbxeQv9AfMziZjORYPvER+jaHk3V7Dvmcn9+Rotw08jOTeGn5kUskf7KlinFZ/RiQFGu5LK9zx2Ry/LA0/vz+Vl74ehfRkcIlE0KbljdjSjYPXlrIby5vf+eqUip4fkPf00d/B7AE2Ai8YoxZLyIPichlntOeAvqLSAnwM8A7rXMukASsw/3m8YwxZk0XX8Mx02j33dIXcS+cZiX0Sw90Pl2zdZmnF2Twecn+Dtdj31hxGLvDxSQL/fmty5x5fA4rS2tadik61NTMN6U1Qc95FhHmXDiG/XU2/rW0lDNHZfqdm2+lzJtOyWN4EF1ESinfLPUJGGMWG2NGGmNGGGP+13PsfmPMQs/XTcaYGcaYfGPMVGPMds/xOs/xscaYQmPM74/epRx99XYn0ZFCTFTHv7ac9ARLA7nem7j8hT7AGaMyqGlo7nA1Re+xiTmBdX/MKMohPjqS577cCcCXnjeVM0b6n6rpy5TcNKZ5Vr4MpWtHKXV06d0NAWiwOTps5XvlpCVQVt3od63usuoGkmKjSOtg6mdbp+UPQIQOu3jW7z5EWkI0WQHObEmJj+aqKVm8sXoPB+psfLKliuTYqIC6iTpy/6WF3H7WCO07V6oH09APQL3dSWIH/fleQ9PjaWx2sr+u831kS6sbyElPsDQDJC0xhgnZqR0O5q7bU8u4rJSgZpLMOmkYdoeL+cvL+HTLfk7O7x/ynPMhqfH89wWjfX4SUkp1P/3XGYAGu4OETm6Tz7E4V7+0uoGcNOut8zNGZrCqrOaITcltDidbKg8zdkhwM1sKBiZzWsEAHv94G7trGkPq2lFK9R4a+gGot/lr6Xvn6vsOfWOMpTn6rZ0xMgOXgc9bbUG3tbKOZqdhXFY/y+W0NeukYS33Hpw+0vpyCUqp3ktDPwAN9s779LPT3EG+64Dv0K86bMPmcDG0v/XQn5idQkp8NB9t+raLZ/0e98ybYFv64F6mdmh6AvmZSS11V0qFt+CW9Ouj6m1OhqT6HnyNj4kkKzWekn11Ps/x3rwVyMqMUZERnDUqgw83VeJwuoiKjGDd7kMkxUaRG8QKj16REcLTNxURwJ7pSqleTlv6AfDX0gcoGJjElsrDPh8v9bOksi8XjB3EwYZmlnk2QF+3p5bCIf0CWtCsI/mZyYwc6H8JB6VUeNDQD0C93UlibOebeIwcmMz2qnoczo73N/WGfqDTLM8YlUFsVATvrq/E6TJsrDjEuBC6dpRSfZOGfgD8zdMHKMhMwu50scvHYG5pdQOD+sVZ2gGqtYSYKE4ryGDJ+r1sq6qjqdkV0iCuUqpv0tC3yOUyNDR3PnsHaOkq2eqji2f3wUayA5iu2dq0cYOoqG3ixaXudXO6YiEypVTfoqFvUZPDiTF0Ok8fIN+zt+yWyo4Hc/ceamJwkGvDnzsmk8gI4cVlpcRFRzC8i5YJVkr1HRr6FtXb3Cts+mvpJ8ZGkZ0W3+FgrjGGitomBqfXl36nAAARaUlEQVR0vEWiP6kJMZyQl47d4WL0oH5E6R6hSqkAaWpY1NDJCpttjRyYzNYOWvoHG5qxO1wM6hdc6IN7Fg+g/flKqaBo6FvU0tL3M3sHPDN49te17CTlVVHrXl0z2JY+uEM/ISaSk4brHbRKqcDpzVkWNTa7W/rxllr6STQ7DbsO1JOf+e0c+Ioa97aHg0II/UEpcRT/4lziA5z9o5RSoC19y6z26cO3M3jaDuZWePa6HZwS3ECuV0JMlO7RqpQKioa+RYH06Y/ISEKEdoO5e2sbiYwQMpID35JQKaW6goa+RYH06cfHRDI0PaHdYG5FbRMDk2OJDHHpBKWUCpaGvkWBtPQBCjKTO2jpN4XUn6+UUqHS0Leo3m69pQ/uwdwd++uxO76dwbO3tink/nyllAqFhr5FDTYHIhAXZTX0k3G4DDsP1APuG7P21DZqS18p1a009C2qtztJiI60vJTx6MHuGTwb9hwCoLaxmaZmV0hz9JVSKlQa+hb52x+3rfyMJOKjI1ldXgO4B3Eh9OmaSikVCg19i/ztj9tWVGQE47NSWF3mDv29taHfmKWUUqHS0LfIyq5ZbU3MSWHdnkM0O12tWvoa+kqp7qOhb1G9zf+uWW1NyE7F7nCxee9h9tY2EiHojVlKqW6loW9RMC39STmpAKwur6GitomM5FiidTlkpVQ30gSyyMr+uG1lp8WTnhjD6jJ36A/SQVylVDfTVTYtsrI/blsiwsTsFFaX1eJwuShoteKmUkp1B23pW2Rlf9yOTMhOZeu+w5QfbGRwqg7iKqW6l4a+RQ02Z0Dz9L0m5aTiMmBz6I1ZSqnuZyn0RWSaiGwWkRIRmdPB47Ei8rLn8aUiMqzVYxNE5CsRWS8ia0Wk1yWf3eHC7nSREMTGJROyU1q+1j59pVR38xv6IhIJzAUuBAqBa0WksM1pNwMHjTH5wCPAw57nRgEvAD80xowFzgSau6z2x0ijZ7G1YFr6/ZNiyUl3h7229JVS3c1KS38qUGKM2W6MsQPzgeltzpkOPOf5+jXgHHFv7XQ+sMYYsxrAGHPAGOPsmqofO/WeZZWD6dMHd78+ENKG6Eop1RWshH4WUNbq+3LPsQ7PMcY4gFqgPzASMCKyRES+EZF7OvoBInKriBSLSHFVVVWg13DUtaylH0RLH+Cc0ZkMSYljoIa+UqqbWUmxjpaVNBbPiQJOBY4HGoAPRGSFMeaDI040Zh4wD6CoqKht2d0ukP1xO3LlcdlceVx2V1ZJKaWCYqWlXw7ktPo+G9jj6xxPP34KUO05/okxZr8xpgFYDBwXaqWPtfoAd81SSqmeykroLwcKRCRPRGKAmcDCNucsBGZ5vr4a+NAYY4AlwAQRSfC8GZwBbOiaqh87DQHsj6uUUj2Z36arMcYhInfgDvBI4GljzHoReQgoNsYsBJ4CnheREtwt/Jme5x4UkT/hfuMwwGJjzFtH6VqOGm3pK6XChaUUM8Ysxt010/rY/a2+bgJm+HjuC7inbfZaDQHuj6uUUj2V3pFrQb1NW/pKqfCgoW+Bt6WfEOTsHaWU6ik09C2otzuIiYrQtfCVUr2eppgFDQHuj6uUUj2Vhr4F9UHsmqWUUj2Rhr4FjUHsmqWUUj2Rhr4F9XantvSVUmFBQ9+CBptDW/pKqbCgoW9Bvd1JfLS29JVSvZ+GvgUNdm3pK6XCg4a+BfU27dNXSoUHDX0LGuwOnaevlAoLGvp+uFyGBrsz6F2zlFKqJ9HQ96OxObRds5RSqifR0PfDG/rxGvpKqTCgoe+H3eECIEYXW1NKhQFNMj9aQj9Kf1VKqd5Pk8wPm4a+UiqMaJL54W3px0Zpn75SqvfT0PfD7nQP5GpLXykVDjTJ/LDpQK5SKoxokvmhA7lKqXCiSebHt336+qtSSvV+mmR+6OwdpVQ40STzQ1v6Sqlwoknmh92pLX2lVPjQJPNDl2FQSoUTTTI/dPaOUiqcaJL5od07Sqlwoknmh82ztLJ27yilwoGlJBORaSKyWURKRGROB4/HisjLnseXisiwNo8PFZE6Ebm7a6p97NicLmKiIhCR7q6KUkqFzG/oi0gkMBe4ECgErhWRwjan3QwcNMbkA48AD7d5/BHg7dCre+zZHS5itZWvlAoTVtJsKlBijNlujLED84Hpbc6ZDjzn+fo14BzxNI1F5HJgO7C+a6p8bNkdLu3PV0qFDStplgWUtfq+3HOsw3OMMQ6gFugvIonAbOBXoVe1e2joK6XCiZU066gz21g851fAI8aYuk5/gMitIlIsIsVVVVUWqnTs2J0a+kqp8BFl4ZxyIKfV99nAHh/nlItIFJACVAMnAFeLyP8DUgGXiDQZY/7W+snGmHnAPICioqK2byjdytbs0iUYlFJhw0roLwcKRCQP2A3MBK5rc85CYBbwFXA18KExxgCneU8QkQeBuraB39NpS18pFU78hr4xxiEidwBLgEjgaWPMehF5CCg2xiwEngKeF5ES3C38mUez0seS3eHSOfpKqbBhpaWPMWYxsLjNsftbfd0EzPBTxoNB1K/b6UCuUiqcaJr54b45SzdFV0qFBw19P7R7RykVTjTN/LA5nMRG669JKRUeNM380GUYlFLhRNPMDx3IVUqFE00zP3SevlIqnGia+aEDuUqpcKJp5od27yilwommWSecLoPDZYjVefpKqTChod8J3RRdKRVuNM06oaGvlAo3mmadsDk9m6Jr6CulwoSmWSe8LX29OUspFS40zTrREvq6DINSKkxomnXC5u3T15a+UipMaJp1QgdylVLhRtOsE3anhr5SKrxomnXCrt07Sqkwo2nWCe3eUUqFG02zTngHcnUZBqVUuNDQ74TNoTdnKaXCi6ZZJ1rm6WvoK6XChKZZJ3T2jlIq3GiadUJn7yilwo2mWSd09o5SKtxomnVC+/SVUuFG06wTNoeLCIEo7d5RSoUJTbNO2J26P65SKrxoonXC7nDpIK5SKqxoonXC5nARo3fjKqXCiIZ+J+wOlw7iKqXCiqVEE5FpIrJZREpEZE4Hj8eKyMuex5eKyDDP8fNEZIWIrPX8/+yurf7RZXdq6CulwovfRBORSGAucCFQCFwrIoVtTrsZOGiMyQceAR72HN8PXGqMGQ/MAp7vqoofC7Zmpw7kKqXCipVEmwqUGGO2G2PswHxgeptzpgPPeb5+DThHRMQYs9IYs8dzfD0QJyKxXVHxY0Fn7yilwo2VRMsCylp9X+451uE5xhgHUAv0b3POVcBKY4yt7Q8QkVtFpFhEiquqqqzW/ajT2TtKqXBjJdGkg2MmkHNEZCzuLp/bOvoBxph5xpgiY0xRRkaGhSodG3aHtvSVUuHFSqKVAzmtvs8G9vg6R0SigBSg2vN9NvBv4EZjzLZQK3ws6UCuUircWEm05UCBiOSJSAwwE1jY5pyFuAdqAa4GPjTGGBFJBd4C7jXGfNFVlT5WtKWvlAo3fhPN00d/B7AE2Ai8YoxZLyIPichlntOeAvqLSAnwM8A7rfMOIB/4pYis8vyX2eVXcZTozVlKqXATZeUkY8xiYHGbY/e3+roJmNHB834D/CbEOnYbHchVSoUbTbRO2LR7RykVZjTROmF3OHUgVykVVjTROqGzd5RS4UYTzQdjjM7eUUqFHU00Hxwug8vopuhKqfCiieaDboqulApHmmg+aOgrpcKRJpoPdqeGvlIq/Gii+eBt6cfqHblKqTCioe+DTbt3lFJhSBPNB5vDCejsHaVUeNFE8+Hb7h39FSmlwocmmg86e0cpFY400XzQ2TtKqXCkieaDdu8opcKRJpoP2r2jlApHmmg+tHTv6OwdpVQY0UTzwdasLX2lVPjRRPPBpgO5SqkwpInmgy7DoJQKRxr6PujsHaVUONJE86Fl9o4O5Cqlwogmmg92p5OoCCEiQrq7Kkop1WU09H2wNev+uEqp8KOp5oPdqaGvlAo/mmo+2B0uHcRVSoUdTTUf7A5t6Sulwo+mmg82p0tn7iilwo6mmg/ulr7emKWUCi8a+j7YtHtHKRWGLKWaiEwTkc0iUiIiczp4PFZEXvY8vlREhrV67F7P8c0ickHXVf3osjucxGr3jlIqzPhNNRGJBOYCFwKFwLUiUtjmtJuBg8aYfOAR4GHPcwuBmcBYYBrwmKe8Hs/ucBEbraGvlAovVlJtKlBijNlujLED84Hpbc6ZDjzn+fo14BwREc/x+cYYmzFmB1DiKa/Hs+tArlIqDEVZOCcLKGv1fTlwgq9zjDEOEakF+nuOf93muVlB1/YoWLJ+L39Ysrnd8V0HGshJS+iGGiml1NFjJfQ7WnzGWDzHynMRkVuBWwGGDh1qoUpdp19cNAUDk9odLxiYxHeKco5pXZRS6mizEvrlQOv0ywb2+DinXESigBSg2uJzMcbMA+YBFBUVtXtTOJpOGtGfk0b0P5Y/Uimluo2VTuvlQIGI5IlIDO6B2YVtzlkIzPJ8fTXwoTHGeI7P9MzuyQMKgGVdU3WllFKB8tvS9/TR3wEsASKBp40x60XkIaDYGLMQeAp4XkRKcLfwZ3qeu15EXgE2AA7gdmOM8yhdi1JKKT/E3SDvOYqKikxxcXF3V0MppXoVEVlhjCnyd57OSVRKqT5EQ18ppfoQDX2llOpDNPSVUqoP0dBXSqk+pMfN3hGRKmDXMf6xA4D9x/hnHkvhfH16bb1XOF9fd1xbrjEmw99JPS70u4OIFFuZ6tRbhfP16bX1XuF8fT352rR7Ryml+hANfaWU6kM09N3mdXcFjrJwvj69tt4rnK+vx16b9ukrpVQfoi19pZTqQ/p86Pvb9L03EZEcEflIRDaKyHoRudNzPF1E3hORrZ7/p3V3XYMlIpEislJEFnm+zxORpZ5re9mz/HevJCKpIvKaiGzyvIYnhctrJyI/9fxNrhORl0Qkrje/diLytIjsE5F1rY51+FqJ26OejFkjIsd1X837eOhb3PS9N3EAPzfGjAFOBG73XM8c4ANjTAHwgef73upOYGOr7x8GHvFc20Hg5m6pVdf4C/COMWY0MBH3dfb6105EsoCfAEXGmHG4l2ifSe9+7Z4FprU55uu1uhD3XiIFuHcI/PsxqmOH+nToY23T917DGFNhjPnG8/Vh3KGRxZEb1z8HXN49NQyNiGQDFwNPer4X4GzgNc8pvfna+gGn496bAmOM3RhTQ5i8drj37oj37KyXAFTQi187Y8ynuPcOac3XazUd+Kdx+xpIFZHBx6am7fX10O9o0/cetXF7sERkGDAZWAoMNMZUgPuNAcjsvpqF5M/APYDL831/oMYY4/B835tfv+FAFfCMp/vqSRFJJAxeO2PMbuAPQCnusK8FVhA+r52Xr9eqR+VMXw99Sxu39zYikgS8DtxljDnU3fXpCiJyCbDPGLOi9eEOTu2tr18UcBzwd2PMZKCeXtiV0xFP3/Z0IA8YAiTi7vJoq7e+dv70qL/Tvh76ljZu701EJBp34P/LGLPAc7jS+3HS8/993VW/EJwCXCYiO3F3w52Nu+Wf6ukygN79+pUD5caYpZ7vX8P9JhAOr925wA5jTJUxphlYAJxM+Lx2Xr5eqx6VM3099K1s+t5rePq4nwI2GmP+1Oqh1hvXzwLeONZ1C5Ux5l5jTLYxZhju1+lDY8x3gY+Aqz2n9cprAzDG7AXKRGSU59A5uPeW7vWvHe5unRNFJMHzN+q9trB47Vrx9VotBG70zOI5Eaj1dgN1C2NMn/4PuAjYAmwD/qe76xPitZyK+2PjGmCV57+LcPd9fwBs9fw/vbvrGuJ1ngks8nw9HFgGlACvArHdXb8QrmsSUOx5/f4DpIXLawf8CtgErAOeB2J782sHvIR7fKIZd0v+Zl+vFe7unbmejFmLexZTt9Vd78hVSqk+pK937yilVJ+ioa+UUn2Ihr5SSvUhGvpKKdWHaOgrpVQfoqGvlFJ9iIa+Ukr1IRr6SinVh/x/9RybOi1yj3IAAAAASUVORK5CYII=\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": 87,
+   "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>130.232558</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>96.448564</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>200.000000</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>200.000000</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>200.000000</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>96.448564</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>200.000000</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>200.000000</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>200.000000</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>128 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  130.232558    0.000000   0.000000\n",
+       "                     std     0.000000   96.448564    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  200.000000    0.000000   0.000000\n",
+       "                     75%     0.000000  200.000000    0.000000   0.000000\n",
+       "                     max     0.000000  200.000000    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   96.448564    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  200.000000    0.000000   0.000000\n",
+       "                     75%     0.000000  200.000000    0.000000   0.000000\n",
+       "                     max     0.000000  200.000000    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",
+       "[128 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data =result.groupby(['Cheaters_On', 'Verifiers_On']).describe()\n",
+    "data.T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "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>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",
+       "    </tr>\n",
+       "  </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": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.Cheater_Reward.T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "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></th>\n",
+       "      <th>count</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>min</th>\n",
+       "      <th>25%</th>\n",
+       "      <th>50%</th>\n",
+       "      <th>75%</th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Cheaters_On</th>\n",
+       "      <th>Verifiers_On</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">False</th>\n",
+       "      <th>0</th>\n",
+       "      <td>15.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>True</th>\n",
+       "      <td>43.0</td>\n",
+       "      <td>13.023256</td>\n",
+       "      <td>9.644856</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>20.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">1</th>\n",
+       "      <th>0</th>\n",
+       "      <td>29.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>True</th>\n",
+       "      <td>14.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "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": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.Cheats_Caught_Volume"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -815,7 +1704,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "version": "3.7.0"
   }
  },
  "nbformat": 4,