diff --git a/demos/verifiers_dilemma.ipynb b/demos/verifiers_dilemma.ipynb
index 4c7f387..bbae821 100644
--- a/demos/verifiers_dilemma.ipynb
+++ b/demos/verifiers_dilemma.ipynb
@@ -44,7 +44,7 @@
     "    return alfa * (s['Total_Volume'])\n",
     "\n",
     "# Verifier's reward per cheat caught\n",
-    "beta = 10 \n",
+    "beta = 4 \n",
     "def verifier_reward(s):\n",
     "    return beta * s['Cheats_Volume']\n",
     "\n",
@@ -53,27 +53,29 @@
     "    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 = 0.1\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\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 = 10\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) > verifier_cost(s)):\n",
+    "    if (verifier_expected_reward(s) > (1+theta)*verifier_cost(s)):\n",
     "        act = True\n",
     "    return {'verifier': act}\n",
     "\n",
@@ -231,7 +233,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "single_proc: [<SimCAD.configuration.Configuration object at 0x111e90f98>]\n"
+      "single_proc: [<SimCAD.configuration.Configuration object at 0x120dd51d0>]\n"
      ]
     }
    ],
@@ -256,7 +258,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 3,
    "metadata": {
     "scrolled": false
    },
@@ -264,10 +266,10 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x113d81c18>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x121dcc470>"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -305,7 +307,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x11318af28>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1220e62e8>"
       ]
      },
      "execution_count": 4,
@@ -314,7 +316,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -471,7 +473,7 @@
        "      <td>100</td>\n",
        "      <td>0.1</td>\n",
        "      <td>1</td>\n",
-       "      <td>200.0</td>\n",
+       "      <td>80.0</td>\n",
        "      <td>1</td>\n",
        "      <td>1</td>\n",
        "      <td>5</td>\n",
@@ -480,7 +482,7 @@
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>0.0</td>\n",
-       "      <td>200.0</td>\n",
+       "      <td>60.0</td>\n",
        "      <td>0</td>\n",
        "      <td>20.0</td>\n",
        "      <td>0.0</td>\n",
@@ -488,7 +490,7 @@
        "      <td>100</td>\n",
        "      <td>0.1</td>\n",
        "      <td>1</td>\n",
-       "      <td>200.0</td>\n",
+       "      <td>80.0</td>\n",
        "      <td>1</td>\n",
        "      <td>1</td>\n",
        "      <td>6</td>\n",
@@ -497,7 +499,7 @@
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td>0.0</td>\n",
-       "      <td>200.0</td>\n",
+       "      <td>60.0</td>\n",
        "      <td>0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -590,7 +592,7 @@
        "      <td>100</td>\n",
        "      <td>0.1</td>\n",
        "      <td>1</td>\n",
-       "      <td>200.0</td>\n",
+       "      <td>80.0</td>\n",
        "      <td>1</td>\n",
        "      <td>1</td>\n",
        "      <td>12</td>\n",
@@ -599,7 +601,7 @@
        "    <tr>\n",
        "      <th>13</th>\n",
        "      <td>0.0</td>\n",
-       "      <td>200.0</td>\n",
+       "      <td>60.0</td>\n",
        "      <td>0</td>\n",
        "      <td>20.0</td>\n",
        "      <td>0.0</td>\n",
@@ -607,7 +609,7 @@
        "      <td>100</td>\n",
        "      <td>0.1</td>\n",
        "      <td>1</td>\n",
-       "      <td>200.0</td>\n",
+       "      <td>80.0</td>\n",
        "      <td>1</td>\n",
        "      <td>1</td>\n",
        "      <td>13</td>\n",
@@ -616,7 +618,7 @@
        "    <tr>\n",
        "      <th>14</th>\n",
        "      <td>0.0</td>\n",
-       "      <td>200.0</td>\n",
+       "      <td>60.0</td>\n",
        "      <td>0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -709,7 +711,7 @@
        "      <td>100</td>\n",
        "      <td>0.1</td>\n",
        "      <td>1</td>\n",
-       "      <td>200.0</td>\n",
+       "      <td>80.0</td>\n",
        "      <td>1</td>\n",
        "      <td>1</td>\n",
        "      <td>19</td>\n",
@@ -727,15 +729,15 @@
        "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          200.0           0                  20.0   \n",
-       "7              0.0          200.0           0                   0.0   \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          200.0           0                  20.0   \n",
-       "14             0.0          200.0           0                   0.0   \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",
@@ -770,21 +772,21 @@
        "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              200.0          1    1          5  2018-01-01 00:00:05  \n",
-       "6              200.0          1    1          6  2018-01-01 00:00:06  \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             200.0          1    1         12  2018-01-01 00:00:12  \n",
-       "13             200.0          1    1         13  2018-01-01 00:00:13  \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             200.0          1    1         19  2018-01-01 00:00:19  "
+       "19              80.0          1    1         19  2018-01-01 00:00:19  "
       ]
      },
      "execution_count": 5,
@@ -798,37 +800,45 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "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[\"Running_Cheating_Volume_Fraction\"]=result[\"Cumulative_Cheating_Volume\"]/result[\"Total_Volume\"].cumsum()"
+    "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": 25,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x113b722e8>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1221ebc18>"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 21,
      "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>"
       ]
@@ -840,12 +850,12 @@
     }
    ],
    "source": [
-    "result.Cumulative_Cheating_Volume.plot()"
+    "result[['Cumulative_Cheating_Profit', \"Cumulative_Verifier_Profit\", \"Cumulative_Net_Profit\"]].plot()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -878,7 +888,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "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": [
     {
@@ -984,14 +1027,14 @@
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td>0.000000</td>\n",
-       "      <td>130.232558</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>96.448564</td>\n",
+       "      <td>28.934569</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
@@ -1012,21 +1055,21 @@
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td>0.000000</td>\n",
-       "      <td>200.000000</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>200.000000</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>200.000000</td>\n",
+       "      <td>60.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
@@ -1142,7 +1185,7 @@
        "      <th rowspan=\"6\" valign=\"top\">Verifiers_Reward</th>\n",
        "      <th>std</th>\n",
        "      <td>0.000000</td>\n",
-       "      <td>96.448564</td>\n",
+       "      <td>38.579426</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
@@ -1163,21 +1206,21 @@
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td>0.000000</td>\n",
-       "      <td>200.000000</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>200.000000</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>200.000000</td>\n",
+       "      <td>80.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
@@ -1354,78 +1397,78 @@
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>128 rows × 4 columns</p>\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  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",
+       "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",
-       "[128 rows x 4 columns]"
+       "[160 rows x 4 columns]"
       ]
      },
-     "execution_count": 87,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1437,7 +1480,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -1546,7 +1589,7 @@
        "max            0.0  20.000000   0.0  20.0"
       ]
      },
-     "execution_count": 64,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1557,7 +1600,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -1671,7 +1714,7 @@
        "            True           0.0  "
       ]
      },
-     "execution_count": 61,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }