diff --git a/docs/tutorials/intro_tutorial.ipynb b/docs/tutorials/intro_tutorial.ipynb
index 63b79978b5e..3127a7fddae 100644
--- a/docs/tutorials/intro_tutorial.ipynb
+++ b/docs/tutorials/intro_tutorial.ipynb
@@ -183,9 +183,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "empty_model = MoneyModel(10)\n",
@@ -235,9 +233,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "class MoneyAgent(Agent):\n",
@@ -294,15 +290,13 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(array([ 4.,  0.,  0.,  0.,  0.,  2.,  0.,  0.,  0.,  4.]),\n",
-       " array([ 0. ,  0.2,  0.4,  0.6,  0.8,  1. ,  1.2,  1.4,  1.6,  1.8,  2. ]),\n",
+       "(array([ 4.,  0.,  5.,  0.,  0.,  0.,  0.,  0.,  0.,  1.]),\n",
+       " array([ 0. ,  0.5,  1. ,  1.5,  2. ,  2.5,  3. ,  3.5,  4. ,  4.5,  5. ]),\n",
        " <a list of 10 Patch objects>)"
       ]
      },
@@ -312,9 +306,9 @@
     },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXkAAAEACAYAAABWLgY0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n1DBDXpIaZshLUsMMeUlq2P8BrOKkBgdtOTYAAAAASUVORK5CYII=\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAAD8CAYAAABXe05zAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x106cd7b38>"
+       "<matplotlib.figure.Figure at 0x110782ba8>"
       ]
      },
      "metadata": {},
@@ -350,16 +344,14 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(array([ 437.,  303.,  144.,   75.,   28.,    9.,    4.]),\n",
-       " array([0, 1, 2, 3, 4, 5, 6, 7]),\n",
-       " <a list of 7 Patch objects>)"
+       "(array([ 429.,  306.,  160.,   63.,   26.,   16.]),\n",
+       " array([0, 1, 2, 3, 4, 5, 6]),\n",
+       " <a list of 6 Patch objects>)"
       ]
      },
      "execution_count": 8,
@@ -368,9 +360,9 @@
     },
     {
      "data": {
-      "image/png": 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+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD8CAYAAAB5Pm/hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10cee86d8>"
+       "<matplotlib.figure.Figure at 0x11901ae80>"
       ]
      },
      "metadata": {},
@@ -602,14 +594,12 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.colorbar.Colorbar at 0x10fb446a0>"
+       "<matplotlib.colorbar.Colorbar at 0x119326b00>"
       ]
      },
      "execution_count": 13,
@@ -618,9 +608,9 @@
     },
     {
      "data": {
-      "image/png": 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+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAScAAAD8CAYAAAA11GIZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkf4fThnIruhslcgAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10fa4b860>"
+       "<matplotlib.figure.Figure at 0x1191a07b8>"
       ]
      },
      "metadata": {},
@@ -737,9 +727,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "model = MoneyModel(50, 10, 10)\n",
@@ -757,14 +745,12 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x10fa4b278>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1192d1518>"
       ]
      },
      "execution_count": 16,
@@ -773,9 +759,9 @@
     },
     {
      "data": {
-      "image/png": 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ArhofSdcAb1buzKzz+lfWp/Lrj3Yxc0hPXrw+rk1DvaY+If7cdlZ/Pt2ZeXJx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\nAf0/IYYG4Jtr1ywAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10fa6c400>"
+       "<matplotlib.figure.Figure at 0x11934cdd8>"
       ]
      },
      "metadata": {},
@@ -797,14 +783,25 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -874,14 +871,12 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x10fa5a978>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x11af58c18>"
       ]
      },
      "execution_count": 18,
@@ -890,9 +885,9 @@
     },
     {
      "data": {
-      "image/png": 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+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x110e7d6d8>"
+       "<matplotlib.figure.Figure at 0x119132898>"
       ]
      },
      "metadata": {},
@@ -914,14 +909,12 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x10f311438>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x11ae5a588>"
       ]
      },
      "execution_count": 19,
@@ -930,9 +923,9 @@
     },
     {
      "data": {
-      "image/png": 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089iXP/sErXPv6LRwzu5j8f0njuL7TxzV+tkkGjXCj56x+e+x2nJw/mnH497Hejh0eF3s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\nlB+FtkxWaQAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x110eadc50>"
+       "<matplotlib.figure.Figure at 0x11afe9a20>"
       ]
      },
      "metadata": {},
@@ -1009,7 +1002,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We instantiate a BatchRunner with a model class to run, and a dictionary mapping parameters to values for them to take. If any of these parameters are assigned more than one value, as a list or an iterator, the BatchRunner will know to run all the combinations of these values and the other ones. The BatchRunner also takes an argument for how many model instantiations to create and run at each combination of parameter values, and how many steps to run each instantiation for. Finally, like the DataCollector, it takes dictionaries of model- and agent-level reporters to collect. Unlike the DataCollector, it won't collect the data every step of the model, but only at the end of each run.\n",
+    "We instantiate a BatchRunner with a model class to run, and two dictionaries: one of the fixed parameters (mapping model arguments to values) and one of varying parameters (mapping each parameter name to a sequence of values for it to take).  The BatchRunner also takes an argument for how many model instantiations to create and run at each combination of parameter values, and how many steps to run each instantiation for. Finally, like the DataCollector, it takes dictionaries of model- and agent-level reporters to collect. Unlike the DataCollector, it won't collect the data every step of the model, but only at the end of each run.\n",
     "\n",
     "In the following example, we hold the height and width fixed, and vary the number of agents. We tell the BatchRunner to run 5 instantiations of the model with each number of agents, and to run each for 100 steps. We have it collect the final Gini coefficient value.\n",
     "\n",
@@ -1019,17 +1012,24 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "245it [00:37,  6.58it/s]\n"
+     ]
+    }
+   ],
    "source": [
-    "parameters = {\"width\": 10,\n",
-    "              \"height\": 10,  \n",
-    "              \"N\": range(10, 500, 10)}\n",
+    "fixed_params = {\"width\": 10,\n",
+    "               \"height\": 10}\n",
+    "variable_params = {\"N\": range(10, 500, 10)}\n",
     "\n",
     "batch_run = BatchRunner(MoneyModel, \n",
-    "                        parameters, \n",
+    "                        variable_params,\n",
+    "                        fixed_params,\n",
     "                        iterations=5, \n",
     "                        max_steps=100,\n",
     "                        model_reporters={\"Gini\": compute_gini})\n",
@@ -1046,14 +1046,12 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x114ab80f0>"
+       "<matplotlib.collections.PathCollection at 0x11b28bac8>"
       ]
      },
      "execution_count": 23,
@@ -1062,9 +1060,9 @@
     },
     {
      "data": {
-      "image/png": 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CskjVM3MhjGuUCUBu+62JbV6+8gcV2KvAgy0Dk2ld/6RHAkkWu0syaOol+u5lu3Z9Ub0e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\n",
+      "image/png": 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DRzPzWGaeAvYAN0+2WZKkSRgk9K8Anu5Znq/K+v1KRHw1Ij4REVf2lF8UEXMR8cWI+OWV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\nYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x111145518>"
+       "<matplotlib.figure.Figure at 0x11f4bb048>"
       ]
      },
      "metadata": {},
@@ -1107,9 +1105,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python [mesa_dev]",
    "language": "python",
-   "name": "python3"
+   "name": "Python [mesa_dev]"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1121,9 +1119,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1+"
+   "version": "3.5.3"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }
diff --git a/docs/tutorials/intro_tutorial.rst b/docs/tutorials/intro_tutorial.rst
index 95de4112d26..f31d9a539b0 100644
--- a/docs/tutorials/intro_tutorial.rst
+++ b/docs/tutorials/intro_tutorial.rst
@@ -804,16 +804,15 @@ indefinitely in ``__init__``.
             self.running = True
             # ...
 
-We instantiate a BatchRunner with a model class to run, and a dictionary
-mapping parameters to values for them to take. If any of these
-parameters are assigned more than one value, as a list or an iterator,
-the BatchRunner will know to run all the combinations of these values
-and the other ones. The BatchRunner also takes an argument for how many
-model instantiations to create and run at each combination of parameter
-values, and how many steps to run each instantiation for. Finally, like
-the DataCollector, it takes dictionaries of model- and agent-level
-reporters to collect. Unlike the DataCollector, it won't collect the
-data every step of the model, but only at the end of each run.
+We instantiate a BatchRunner with a model class to run, and two dictionaries: 
+one of the fixed parameters (mapping model arguments to values) and one of
+varying parameters (mapping each parameter name to a sequence of values for it
+to take). The BatchRunner also takes an argument for how many model
+instantiations to create and run at each combination of parameter values, and 
+how many steps to run each instantiation for. Finally, like the DataCollector, 
+it takes dictionaries of model- and agent-level reporters to collect. Unlike
+the DataCollector, it won't collect the data every step of the model, but only 
+at the end of each run.
 
 In the following example, we hold the height and width fixed, and vary
 the number of agents. We tell the BatchRunner to run 5 instantiations of
@@ -827,12 +826,13 @@ Now, we can set up and run the BatchRunner:
     # run.py
     from mesa.batchrunner import BatchRunner
 
-    parameters = {"width": 10,
-                  "height": 10,  
-                  "N": range(10, 500, 10)}
+    fixed_params = {"width": 10,
+                    "height": 10}  
+    variable_params = {"N": range(10, 500, 10)}
     
     batch_run = BatchRunner(MoneyModel, 
-                            parameters, 
+                            fixed_parameters=fixed_params,
+                            variable_parameters=variable_params, 
                             iterations=5, 
                             max_steps=100,
                             model_reporters={"Gini": compute_gini})
@@ -855,8 +855,6 @@ DataFrame.
     <matplotlib.collections.PathCollection at 0x114ab80f0>
 
 
-
-
 .. image:: intro_tutorial_files/intro_tutorial_52_1.png
 
 
diff --git a/examples/Schelling/analysis.ipynb b/examples/Schelling/analysis.ipynb
index 38f07ae9d85..f60068a9b39 100644
--- a/examples/Schelling/analysis.ipynb
+++ b/examples/Schelling/analysis.ipynb
@@ -17,10 +17,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 1,
+   "metadata": {},
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
@@ -38,9 +36,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {
-    "collapsed": false
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -56,10 +54,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 3,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -84,9 +80,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {
-    "collapsed": false
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -95,15 +91,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 5,
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -114,7 +121,7 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>66</td>\n",
+       "      <td>61</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
@@ -122,15 +129,15 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>67</td>\n",
+       "      <td>69</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>66</td>\n",
+       "      <td>71</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>66</td>\n",
+       "      <td>71</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -138,14 +145,14 @@
       ],
       "text/plain": [
        "   happy\n",
-       "0     66\n",
+       "0     61\n",
        "1     67\n",
-       "2     67\n",
-       "3     66\n",
-       "4     66"
+       "2     69\n",
+       "3     71\n",
+       "4     71"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -163,26 +170,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 6,
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x10672dd30>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x110917ac8>"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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Ycf3czKxAcgn0XqA3IvYl27vIBvx2YHey71vAnJOiEbEzIjojorOtre3M/qkpr0E3Myuk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\nAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1066d15f8>"
+       "<matplotlib.figure.Figure at 0x110917400>"
       ]
      },
      "metadata": {},
@@ -202,9 +207,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {
-    "collapsed": false
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -213,15 +218,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 8,
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -241,28 +257,28 @@
        "    <tr>\n",
        "      <th rowspan=\"5\" valign=\"top\">0</th>\n",
        "      <th>(0, 0)</th>\n",
-       "      <td>1</td>\n",
+       "      <td>2</td>\n",
        "      <td>5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>(0, 1)</th>\n",
-       "      <td>4</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
        "      <th>(0, 2)</th>\n",
-       "      <td>6</td>\n",
-       "      <td>0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>(0, 3)</th>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>(0, 4)</th>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>(0, 5)</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -271,14 +287,14 @@
       "text/plain": [
        "              x  y\n",
        "Step AgentID      \n",
-       "0    (0, 0)   1  5\n",
-       "     (0, 1)   4  7\n",
-       "     (0, 2)   6  0\n",
-       "     (0, 3)   2  1\n",
-       "     (0, 4)   2  2"
+       "0    (0, 0)   2  5\n",
+       "     (0, 2)   8  4\n",
+       "     (0, 3)   5  4\n",
+       "     (0, 4)   4  9\n",
+       "     (0, 5)   1  0"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -300,10 +316,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 9,
+   "metadata": {},
    "outputs": [],
    "source": [
     "from mesa.batchrunner import BatchRunner"
@@ -311,7 +325,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {
     "collapsed": true
    },
@@ -342,21 +356,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {
     "collapsed": true
    },
    "outputs": [],
    "source": [
-    "parameters = {\"height\": 10, \"width\": 10, \"density\": 0.8, \"minority_pc\": 0.2, \n",
-    "              \"homophily\": range(1,9)}"
+    "fixed_params = {\"height\": 10, \"width\": 10, \"density\": 0.8, \"minority_pc\": 0.2} \n",
+    "variable_parms = {\"homophily\": range(1,9)}"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "metadata": {
-    "collapsed": false
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -365,33 +379,41 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "metadata": {
     "collapsed": true
    },
    "outputs": [],
    "source": [
-    "param_sweep = BatchRunner(SchellingModel, parameters, iterations=10, \n",
+    "param_sweep = BatchRunner(SchellingModel, \n",
+    "                          variable_parameters=variable_parms, fixed_parameters=fixed_params,\n",
+    "                          iterations=10, \n",
     "                          max_steps=200,\n",
     "                          model_reporters=model_reporters)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "80it [00:02, 27.26it/s] \n"
+     ]
+    }
+   ],
    "source": [
     "param_sweep.run_all()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "metadata": {
-    "collapsed": false
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -400,16 +422,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 16,
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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tW7nc9u7dy7XX3sLrr+8bfG7mzGU88cTDXHHFFYnlSqfTzJu3gNOnv0dmPy8v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\n",
+      "image/png": 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yOsk7Jp1pJUm+MMmfJ/nLQdb/OelMoyTZluQvknx40llGSXI2yaNJHkny0KTzrCbJK5M8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\nl6QG/RdKeUiSHYeeDwAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1082ba400>"
+       "<matplotlib.figure.Figure at 0x112f4d128>"
       ]
      },
      "metadata": {},
@@ -424,9 +444,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python [mesa_dev]",
    "language": "python",
-   "name": "python3"
+   "name": "Python [mesa_dev]"
   },
   "language_info": {
    "codemirror_mode": {
@@ -438,9 +458,13 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.5.3"
+  },
+  "widgets": {
+   "state": {},
+   "version": "1.1.2"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }
diff --git a/examples/forest_fire/Forest Fire Model.ipynb b/examples/forest_fire/Forest Fire Model.ipynb
index 59e4d8479c1..56332b83d52 100644
--- a/examples/forest_fire/Forest Fire Model.ipynb	
+++ b/examples/forest_fire/Forest Fire Model.ipynb	
@@ -24,9 +24,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import random\n",
@@ -59,9 +57,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "class TreeCell(Agent):\n",
@@ -117,9 +113,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "class ForestFire(Model):\n",
@@ -193,9 +187,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "fire = ForestFire(100, 100, 0.6)"
@@ -211,9 +203,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "fire.run_model()"
@@ -227,17 +217,13 @@
     "\n",
     "But... so what? This code doesn't include a visualization, after all. \n",
     "\n",
-    "**TODO: Add a MatPlotLib visualization**\n",
-    "\n",
     "Remember the data collector? Now we can put the data it collected into a pandas DataFrame:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "results = fire.dc.get_model_vars_dataframe()"
@@ -253,14 +239,12 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x10a022710>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x119756a90>"
       ]
      },
      "execution_count": 7,
@@ -269,9 +253,9 @@
     },
     {
      "data": {
-      "image/png": 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tVAZ/N5i0jDRuaHkDN7S6gUvqX4KP0WMIbyECv/8O//ufrbtffjmMHw+XXqpH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\nYh6d9SiC8EqPV+jSoIuWcQq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RrSQAfFn/SXDmXbDub7Drw2DnRoiwE8rVypHmdL9rCQDHc+69kD4c/nmnVAUJcQrcbjfl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\nAUAIIXqp/w8TtkNp4lrx8wAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10a019eb8>"
+       "<matplotlib.figure.Figure at 0x119756320>"
       ]
      },
      "metadata": {},
@@ -294,14 +278,12 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x10a4ddc50>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x11c105a20>"
       ]
      },
      "execution_count": 8,
@@ -310,9 +292,9 @@
     },
     {
      "data": {
-      "image/png": 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YDz9Y9fcM/vM1CrjOnTsTEhJCiRIlePnll5k6deqVJP/ss89SuHBhypUrx6BB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\nj4josB9MSBmuscOXqgAAAABJRU5ErkJggg==\n",
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EyCNJAEXEoQvxDJm6kTSHiwXj29Kyell3h1S0BNWAUd9A73/DyfXw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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x109efdbe0>"
+       "<matplotlib.figure.Figure at 0x11c0ffdd8>"
       ]
      },
      "metadata": {},
@@ -342,23 +324,19 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "param_set = dict(height=50, # Height and width are constant\n",
-    "                 width=50,\n",
-    "                 # Vary density from 0.01 to 1, in 0.01 increments:\n",
-    "                 density=np.linspace(0,1,101)[1:])"
+    "fixed_params = dict(height=50, # Height and width are constant\n",
+    "                 width=50)\n",
+    "# Vary density from 0.01 to 1, in 0.01 increments:\n",
+    "variable_params = dict(density=np.linspace(0,1,101)[1:])"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# At the end of each model run, calculate the fraction of trees which are Burned Out\n",
@@ -369,13 +347,12 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Create the batch runner\n",
-    "param_run = BatchRunner(ForestFire, param_set, model_reporters=model_reporter)"
+    "param_run = BatchRunner(ForestFire, variable_parameters=variable_params, \n",
+    "                        fixed_parameters=fixed_params, model_reporters=model_reporter)"
    ]
   },
   {
@@ -388,15 +365,13 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 100/100 [00:08<00:00,  4.78it/s]\n"
+      "100it [00:06,  6.45it/s]\n"
      ]
     }
    ],
@@ -414,9 +389,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "df = param_run.get_model_vars_dataframe()"
@@ -425,77 +398,76 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>BurnedOut</th>\n",
-       "      <th>Run</th>\n",
        "      <th>density</th>\n",
-       "      <th>height</th>\n",
-       "      <th>width</th>\n",
+       "      <th>Run</th>\n",
+       "      <th>BurnedOut</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td> 0.933628</td>\n",
-       "      <td> 63</td>\n",
-       "      <td> 0.64</td>\n",
-       "      <td> 50</td>\n",
-       "      <td> 50</td>\n",
+       "      <th>6</th>\n",
+       "      <td>0.01</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td> 1.000000</td>\n",
-       "      <td> 86</td>\n",
-       "      <td> 0.87</td>\n",
-       "      <td> 50</td>\n",
-       "      <td> 50</td>\n",
+       "      <th>98</th>\n",
+       "      <td>0.02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td> 0.793289</td>\n",
-       "      <td> 58</td>\n",
-       "      <td> 0.59</td>\n",
-       "      <td> 50</td>\n",
-       "      <td> 50</td>\n",
+       "      <th>73</th>\n",
+       "      <td>0.03</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.030303</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td> 1.000000</td>\n",
-       "      <td> 81</td>\n",
-       "      <td> 0.82</td>\n",
-       "      <td> 50</td>\n",
-       "      <td> 50</td>\n",
+       "      <th>75</th>\n",
+       "      <td>0.04</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.021053</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td> 0.052219</td>\n",
-       "      <td> 30</td>\n",
-       "      <td> 0.31</td>\n",
-       "      <td> 50</td>\n",
-       "      <td> 50</td>\n",
+       "      <th>81</th>\n",
+       "      <td>0.05</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0.030303</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "   BurnedOut  Run  density  height  width\n",
-       "0   0.933628   63     0.64      50     50\n",
-       "1   1.000000   86     0.87      50     50\n",
-       "2   0.793289   58     0.59      50     50\n",
-       "3   1.000000   81     0.82      50     50\n",
-       "4   0.052219   30     0.31      50     50"
+       "    density  Run  BurnedOut\n",
+       "6      0.01    0   0.000000\n",
+       "98     0.02    1   0.000000\n",
+       "73     0.03    2   0.030303\n",
+       "75     0.04    3   0.021053\n",
+       "81     0.05    4   0.030303"
       ]
      },
      "execution_count": 14,
@@ -517,9 +489,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -533,9 +503,9 @@
     },
     {
      "data": {
-      "image/png": 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evXCSmVMxNQrs4WtkdbL2fC9q7k7F1Kgy8DWSulndsshiaq04FVOjypKORtIwr3B1KqZG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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10ad75c18>"
+       "<matplotlib.figure.Figure at 0x11c183c50>"
       ]
      },
      "metadata": {},
@@ -561,15 +531,13 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 500/500 [00:37<00:00,  6.89it/s]\n"
+      "500it [00:06, 71.59it/s]  \n"
      ]
     },
     {
@@ -584,9 +552,9 @@
     },
     {
      "data": {
-      "image/png": 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4cSpdo5SNTBYK/DImMlM6O/Fu8QinbQDm50yrNDXVs317ZmqnqcmbKuG5514m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\nu82Vedy9LgAAAABJRU5ErkJggg==\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAD8CAYAAAB0IB+mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10a8b6f60>"
+       "<matplotlib.figure.Figure at 0x11cbe6630>"
       ]
      },
      "metadata": {},
@@ -594,7 +562,8 @@
     }
    ],
    "source": [
-    "param_run = BatchRunner(ForestFire, param_set, iterations=5, model_reporters=model_reporter)\n",
+    "param_run = BatchRunner(ForestFire, variable_params, fixed_params, \n",
+    "                        iterations=5, model_reporters=model_reporter)\n",
     "param_run.run_all()\n",
     "df = param_run.get_model_vars_dataframe()\n",
     "plt.scatter(df.density, df.BurnedOut)\n",
@@ -605,9 +574,9 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python [mesa_dev]",
    "language": "python",
-   "name": "python3"
+   "name": "Python [mesa_dev]"
   },
   "language_info": {
    "codemirror_mode": {
@@ -619,7 +588,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.4.2"
+   "version": "3.5.3"
   },
   "widgets": {
    "state": {},
@@ -627,5 +596,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }
diff --git a/mesa/batchrunner.py b/mesa/batchrunner.py
index 897255c6c36..184fedb3d6c 100644
--- a/mesa/batchrunner.py
+++ b/mesa/batchrunner.py
@@ -6,11 +6,24 @@
 A single class to manage a batch run or parameter sweep of a given model.
 
 """
-from itertools import product
+import collections
+import copy
+from itertools import product, count
 import pandas as pd
 from tqdm import tqdm
 
 
+class VariableParameterError(TypeError):
+    MESSAGE = ('variable_parameters must map a name to a sequence of values. '
+            'These parameters were given with non-sequence values: {}')
+
+    def __init__(self, bad_names):
+        self.bad_names = bad_names
+
+    def __str__(self):
+        return self.MESSAGE.format(self.bad_names)
+
+
 class BatchRunner:
     """ This class is instantiated with a model class, and model parameters
     associated with one or more values. It is also instantiated with model and
@@ -23,19 +36,26 @@ class BatchRunner:
     entire DataCollector object.
 
     """
-    def __init__(self, model_cls, parameter_values, iterations=1,
-                 max_steps=1000, model_reporters=None, agent_reporters=None,
-                 display_progress=True):
+    def __init__(self, model_cls, variable_parameters=None,
+            fixed_parameters=None, iterations=1, max_steps=1000,
+            model_reporters=None, agent_reporters=None, display_progress=True):
         """ Create a new BatchRunner for a given model with the given
         parameters.
 
         Args:
             model_cls: The class of model to batch-run.
-            parameter_values: Dictionary of parameters to their values or
-                ranges of values. For example:
+            variable_parameters: Dictionary of parameters to lists of values.
+                The model will be run with every combination of these paramters.
+                For example, given variable_parameters of
                     {"param_1": range(5),
-                     "param_2": [1, 5, 10],
-                      "const_param": 100}
+                     "param_2": [1, 5, 10]}
+                models will be run with {param_1=1, param_2=1},
+                    {param_1=2, param_2=1}, ..., {param_1=4, param_2=10}.
+            fixed_parameters: Dictionary of parameters that stay same through
+                all batch runs. For example, given fixed_parameters of
+                    {"constant_parameter": 3},
+                every instantiated model will be passed constant_parameter=3
+                as a kwarg.
             iterations: The total number of times to run the model for each
                 combination of parameters.
             max_steps: The upper limit of steps above which each run will be halted
@@ -51,8 +71,8 @@ def __init__(self, model_cls, parameter_values, iterations=1,
 
         """
         self.model_cls = model_cls
-        self.parameter_values = {param: self.make_iterable(vals)
-                                 for param, vals in parameter_values.items()}
+        self.variable_parameters = self._process_parameters(variable_parameters)
+        self.fixed_parameters = fixed_parameters or {}
         self.iterations = iterations
         self.max_steps = max_steps
 
@@ -67,36 +87,42 @@ def __init__(self, model_cls, parameter_values, iterations=1,
 
         self.display_progress = display_progress
 
+    def _process_parameters(self, params):
+        params = copy.deepcopy(params)
+        bad_names = []
+        for name, values in params.items():
+            if (isinstance(values, str) or
+                    not hasattr(values, "__iter__")):
+                bad_names.append(name)
+        if bad_names:
+            raise VariableParameterError(bad_names)
+        return params
+
     def run_all(self):
         """ Run the model at all parameter combinations and store results. """
-        params = self.parameter_values.keys()
-        param_ranges = self.parameter_values.values()
-        run_count = 0
-
-        if self.display_progress:
-            pbar = tqdm(total=len(list(product(*param_ranges))) * self.iterations)
-
-        for param_values in list(product(*param_ranges)):
-            kwargs = dict(zip(params, param_values))
-            for _ in range(self.iterations):
+        param_names, param_ranges = zip(*self.variable_parameters.items())
+        run_count = count()
+        total_iterations = self.iterations
+        for param_range in param_ranges:
+            total_iterations *= len(param_range)
+        with tqdm(total_iterations, disable=not self.display_progress) as pbar:
+            for param_values in product(*param_ranges):
+                kwargs = dict(zip(param_names, param_values))
+                kwargs.update(self.fixed_parameters)
                 model = self.model_cls(**kwargs)
-                self.run_model(model)
-                # Collect and store results:
-                if self.model_reporters:
-                    key = tuple(list(param_values) + [run_count])
-                    self.model_vars[key] = self.collect_model_vars(model)
-                if self.agent_reporters:
-                    agent_vars = self.collect_agent_vars(model)
-                    for agent_id, reports in agent_vars.items():
-                        key = tuple(list(param_values) + [run_count, agent_id])
-                        self.agent_vars[key] = reports
-                if self.display_progress:
-                    pbar.update()
-
-                run_count += 1
 
-        if self.display_progress:
-            pbar.close()
+                for _ in range(self.iterations):
+                    self.run_model(model)
+                    # Collect and store results:
+                    model_key = param_values + (next(run_count),)
+                    if self.model_reporters:
+                        self.model_vars[model_key] = self.collect_model_vars(model)
+                    if self.agent_reporters:
+                        agent_vars = self.collect_agent_vars(model)
+                        for agent_id, reports in agent_vars.items():
+                            agent_key = model_key + (agent_id,)
+                            self.agent_vars[agent_key] = reports
+                    pbar.update()
 
     def run_model(self, model):
         """ Run a model object to completion, or until reaching max steps.
@@ -126,38 +152,36 @@ def collect_agent_vars(self, model):
         return agent_vars
 
     def get_model_vars_dataframe(self):
-        """ Generate a pandas DataFrame from the model-level variables collected.
+        """ Generate a pandas DataFrame from the model-level variables
+        collected.
 
         """
-        index_col_names = list(self.parameter_values.keys())
-        index_col_names.append("Run")
-        records = []
-        for key, val in self.model_vars.items():
-            record = dict(zip(index_col_names, key))
-            for k, v in val.items():
-                record[k] = v
-            records.append(record)
-        return pd.DataFrame(records)
+        return self._prepare_report_table(self.model_vars)
 
     def get_agent_vars_dataframe(self):
         """ Generate a pandas DataFrame from the agent-level variables
         collected.
 
         """
-        index_col_names = list(self.parameter_values.keys())
-        index_col_names += ["Run", "AgentID"]
+        return self._prepare_report_table(self.agent_vars,
+                extra_cols=['AgentId'])
+
+    def _prepare_report_table(self, vars_dict, extra_cols=None):
+        """
+        Creates a dataframe from collected records and sorts it using 'Run'
+        column as a key.
+        """
+        extra_cols = ['Run'] + (extra_cols or [])
+        index_cols = list(self.variable_parameters.keys()) + extra_cols
+
         records = []
-        for key, val in self.agent_vars.items():
-            record = dict(zip(index_col_names, key))
-            for k, v in val.items():
-                record[k] = v
+        for param_key, values in vars_dict.items():
+            record = dict(zip(index_cols, param_key))
+            record.update(values)
             records.append(record)
-        return pd.DataFrame(records)
-
-    @staticmethod
-    def make_iterable(val):
-        """ Helper method to ensure a value is a non-string iterable. """
-        if hasattr(val, "__iter__") and not isinstance(val, str):
-            return val
-        else:
-            return [val]
+
+        df = pd.DataFrame(records)
+        rest_cols = set(df.columns) - set(index_cols)
+        ordered = df[index_cols + list(sorted(rest_cols))]
+        ordered.sort_values(by='Run', inplace=True)
+        return ordered
diff --git a/mesa/time.py b/mesa/time.py
index 75bac08339c..ad5af978754 100644
--- a/mesa/time.py
+++ b/mesa/time.py
@@ -139,7 +139,7 @@ class StagedActivation(BaseScheduler):
     shuffle_between_stages = False
     stage_time = 1
 
-    def __init__(self, model, stage_list=["step"], shuffle=False,
+    def __init__(self, model, stage_list=None, shuffle=False,
                  shuffle_between_stages=False):
         """ Create an empty Staged Activation schedule.
 
@@ -154,7 +154,7 @@ def __init__(self, model, stage_list=["step"], shuffle=False,
 
         """
         super().__init__(model)
-        self.stage_list = stage_list
+        self.stage_list = stage_list or ["step"]
         self.shuffle = shuffle
         self.shuffle_between_stages = shuffle_between_stages
         self.stage_time = 1 / len(self.stage_list)
diff --git a/tests/test_batchrunner.py b/tests/test_batchrunner.py
index 2e9242c4c7b..e9b9904b9f8 100644
--- a/tests/test_batchrunner.py
+++ b/tests/test_batchrunner.py
@@ -1,27 +1,28 @@
 """
 Test the BatchRunner
 """
+from functools import reduce
+from operator import mul
 import unittest
 
 from mesa import Agent, Model
-from mesa.batchrunner import BatchRunner
 from mesa.time import BaseScheduler
+from mesa.batchrunner import BatchRunner
+
 
 NUM_AGENTS = 7
 
 
 class MockAgent(Agent):
     """
-    Minimalistic model for testing purposes
+    Minimalistic agent implementation for testing purposes
     """
-    def __init__(self, unique_id, val):
+    def __init__(self, unique_id, model, val):
+        super().__init__(unique_id, model)
         self.unique_id = unique_id
         self.val = val
 
     def step(self):
-        """
-        increment val by 1
-        """
         self.val += 1
 
 
@@ -29,18 +30,34 @@ class MockModel(Model):
     """
     Minimalistic model for testing purposes
     """
-    def __init__(self, model_param, agent_param):
-        """
-        Args:
-            model_param (any): parameter specific to the model
-            agent_param (int): parameter specific to the agent
-        """
-        self.schedule = BaseScheduler(None)
-        self.model_param = model_param
+    def __init__(self, variable_model_param, variable_agent_param,
+                 fixed_model_param=None, schedule=None, **kwargs):
+        super().__init__()
+        self.schedule = BaseScheduler(None) if schedule is None else schedule
+        self.variable_model_param = variable_model_param
+        self.variable_agent_param = variable_agent_param
+        self.fixed_model_param = fixed_model_param
+        self.n_agents = kwargs.get('n_agents', NUM_AGENTS)
+        self.running = True
+        self.init_agents()
+
+    def init_agents(self):
+        for i in range(self.n_agents):
+            self.schedule.add(MockAgent(i, self, self.variable_agent_param))
+
+    def step(self):
+        self.schedule.step()
+
+
+class MockMixedModel(Model):
+
+    def __init__(self, **other_params):
+        super().__init__()
+        self.variable_name = other_params.get('variable_name', 42)
+        self.fixed_name = other_params.get('fixed_name')
         self.running = True
-        for i in range(NUM_AGENTS):
-            a = MockAgent(i, agent_param)
-            self.schedule.add(a)
+        self.schedule = BaseScheduler(None)
+        self.schedule.add(MockAgent(1, self, 0))
 
     def step(self):
         self.schedule.step()
@@ -51,44 +68,95 @@ class TestBatchRunner(unittest.TestCase):
     Test that BatchRunner is running batches
     """
     def setUp(self):
-        """
-        Create the model and run it for some steps
-        """
-        self.model_reporter = {"model": lambda m: m.model_param}
-        self.agent_reporter = {
+        self.mock_model = MockModel
+        self.model_reporters = {
+            "reported_variable_value": lambda m: m.variable_model_param,
+            "reported_fixed_value": lambda m: m.fixed_model_param
+        }
+        self.agent_reporters = {
             "agent_id": lambda a: a.unique_id,
-            "agent_val": lambda a: a.val}
-        self.params = {
-            'model_param': range(3),
-            'agent_param': [1, 8],
+            "agent_val": lambda a: a.val
+        }
+        self.variable_params = {
+            "variable_model_param": range(3),
+            "variable_agent_param": [1, 8]
         }
+        self.fixed_params = None
         self.iterations = 17
-        self.batch = BatchRunner(
-            MockModel,
-            self.params,
+        self.max_steps = 3
+
+    def launch_batch_processing(self):
+        batch = BatchRunner(
+            self.mock_model,
+            variable_parameters=self.variable_params,
+            fixed_parameters=self.fixed_params,
             iterations=self.iterations,
-            max_steps=3,
-            model_reporters=self.model_reporter,
-            agent_reporters=self.agent_reporter)
-        self.batch.run_all()
+            max_steps=self.max_steps,
+            model_reporters=self.model_reporters,
+            agent_reporters=self.agent_reporters)
+        batch.run_all()
+        return batch
+
+    @property
+    def model_runs(self):
+        """
+        Returns total number of batch runner's iterations.
+        """
+        return (reduce(mul, map(len, self.variable_params.values())) *
+                self.iterations)
 
     def test_model_level_vars(self):
         """
         Test that model-level variable collection is of the correct size
         """
-        model_vars = self.batch.get_model_vars_dataframe()
-        rows = len(self.params['model_param']) * \
-            len(self.params['agent_param']) * \
-            self.iterations
-        assert model_vars.shape == (rows, 4)
+        batch = self.launch_batch_processing()
+        model_vars = batch.get_model_vars_dataframe()
+        expected_cols = (len(self.variable_params) +
+                         len(self.model_reporters) +
+                         1)  # extra column with run index
+
+        self.assertEqual(model_vars.shape, (self.model_runs, expected_cols))
 
     def test_agent_level_vars(self):
         """
         Test that agent-level variable collection is of the correct size
         """
-        agent_vars = self.batch.get_agent_vars_dataframe()
-        rows = NUM_AGENTS * \
-            len(self.params['agent_param']) * \
-            len(self.params['model_param']) * \
-            self.iterations
-        assert agent_vars.shape == (rows, 6)
+        batch = self.launch_batch_processing()
+        agent_vars = batch.get_agent_vars_dataframe()
+        expected_cols = (len(self.variable_params) +
+                         len(self.agent_reporters) +
+                         2)  # extra columns with run index and agentId
+
+        self.assertEqual(agent_vars.shape,
+             (self.model_runs * NUM_AGENTS, expected_cols))
+
+    def test_model_with_fixed_parameters_as_kwargs(self):
+        """
+        Test that model with fixed parameters passed like kwargs is
+        properly handled
+        """
+        self.fixed_params = {'fixed_model_param': 'Fixed', 'n_agents': 1}
+        batch = self.launch_batch_processing()
+        model_vars = batch.get_model_vars_dataframe()
+        agent_vars = batch.get_agent_vars_dataframe()
+
+        self.assertEqual(len(model_vars), len(agent_vars))
+        self.assertEqual(len(model_vars), self.model_runs)
+        self.assertEqual(model_vars['reported_fixed_value'].unique(), ['Fixed'])
+
+    def test_model_with_variable_and_fixed_kwargs(self):
+        self.mock_model = MockMixedModel
+        self.model_reporters = {
+            'reported_fixed_param': lambda m: m.fixed_name,
+            'reported_variable_param': lambda m: m.variable_name
+        }
+        self.fixed_params = {'fixed_name': 'Fixed'}
+        self.variable_params = {'variable_name': [1, 2, 3]}
+        batch = self.launch_batch_processing()
+        model_vars = batch.get_model_vars_dataframe()
+        expected_cols = (len(self.variable_params) +
+                         len(self.model_reporters) +
+                         1)
+        self.assertEqual(model_vars.shape, (self.model_runs, expected_cols))
+        self.assertEqual(model_vars['reported_fixed_param'].iloc[0],
+                self.fixed_params['fixed_name'])