Ability Wealth Modelling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ability - Wealth Modelling\n",
    "\n",
    "Ability like IQ can be modelled as a normal distribution, however, wealth in society follows a power law. Why is this the case? Suppose if outcomes are path dependent, with the state in previous steps influencing the later steps, will the final distribution follow a power law? This notebook seeks to model this. \n",
    "\n",
    "Results: Apparently, this is true. Oh wow!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import math as math\n",
    "import scipy.stats \n",
    "\n",
    "np.random.seed(0)\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initial Parameters\n",
    "\n",
    "We have an initial population size of 1000. \n",
    "\n",
    "Consider Ability to be between 0-100 following a truncated normal distribution.\n",
    "\n",
    "Consider Initial wealth to be 0. (To experiment with initial wealth distributions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    1000.000000\n",
      "mean       49.931963\n",
      "std        15.030474\n",
      "min         3.531823\n",
      "25%        39.779987\n",
      "50%        49.298082\n",
      "75%        59.514466\n",
      "max        98.440539\n",
      "Name: Ability, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12e2b4820>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = 1000\n",
    "\n",
    "lower = 0\n",
    "upper = 100\n",
    "mu = 50\n",
    "sigma = 15\n",
    "\n",
    "ability = scipy.stats.truncnorm.rvs(\n",
    "          (lower-mu)/sigma,(upper-mu)/sigma,loc=mu,scale=sigma,size=N)\n",
    "\n",
    "wealth = np.zeros(shape=(N))\n",
    "\n",
    "pd_ability = pd.Series(ability, name=\"Ability\")\n",
    "print(pd_ability.describe())\n",
    "\n",
    "plt.figure(figsize=(10,8))\n",
    "sns.distplot(pd_ability)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transition Function\n",
    "At every time period, conditioned upon given ability and current wealth the wealth goes up or down given some percentage chance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def trans_fn(wealth_el, ability_el):\n",
    "    \"\"\"\n",
    "    Outputs new wealth\n",
    "    \"\"\"\n",
    "    # take the simple average of wealth and ability / 100. \n",
    "    # This gives us the probability of going up\n",
    "    avg_el = (wealth_el + ability_el) / 2\n",
    "    probability = avg_el / 100\n",
    "    \n",
    "    if probability > np.random.rand():\n",
    "        return wealth_el + 1\n",
    "    else:\n",
    "        if wealth_el > 0:\n",
    "            return wealth_el - 1\n",
    "        else:\n",
    "            return 0\n",
    "    \n",
    "\n",
    "vtrans_fn = np.vectorize(trans_fn)\n",
    "\n",
    "# Sanity Check\n",
    "# print(wealth)\n",
    "# new_wealth = vtrans_fn(wealth, ability)\n",
    "# print(new_wealth)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test The Evolution of the System\n",
    "We run the system for 100 epochs and see the way the wealth distribution evolves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = []\n",
    "epochs = 100\n",
    "\n",
    "\n",
    "for i in range(epochs): \n",
    "    results.append(wealth)\n",
    "    wealth = vtrans_fn(wealth, ability)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Observe Final Wealth\n",
    "We get a power law looking like distribution. Interesting !! This is for the case where all wealth starts at 0. There's a tendency for a agent with better ability to do well. But that is not enough either, the agent with max ability didn't have the max wealth. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1000.000000\n",
       "mean        0.712000\n",
       "std         1.561888\n",
       "min         0.000000\n",
       "25%         0.000000\n",
       "50%         0.000000\n",
       "75%         1.000000\n",
       "max        25.000000\n",
       "Name: Wealth, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd_wealth = pd.Series(wealth, name=\"Wealth\")\n",
    "\n",
    "pd_wealth.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1325ee6d0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Frequency of wealth\n",
    "plt.figure(figsize=(10,8))\n",
    "sns.distplot(pd_wealth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Either both or neither of `x` and `y` must be specified (but try passing to `data`, which is more flexible).",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-11-84df21d52b6c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mwealth_sorted\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd_wealth\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlineplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwealth_sorted\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/miniconda3/envs/datasci/lib/python3.8/site-packages/seaborn/relational.py\u001b[0m in \u001b[0;36mlineplot\u001b[0;34m(x, y, hue, size, style, data, palette, hue_order, hue_norm, sizes, size_order, size_norm, dashes, markers, style_order, units, estimator, ci, n_boot, sort, err_style, err_kws, legend, ax, **kwargs)\u001b[0m\n\u001b[1;32m   1070\u001b[0m              legend=\"brief\", ax=None, **kwargs):\n\u001b[1;32m   1071\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1072\u001b[0;31m     p = _LinePlotter(\n\u001b[0m\u001b[1;32m   1073\u001b[0m         \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstyle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstyle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1074\u001b[0m         \u001b[0mpalette\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpalette\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhue_order\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhue_order\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhue_norm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhue_norm\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/datasci/lib/python3.8/site-packages/seaborn/relational.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, x, y, hue, size, style, data, palette, hue_order, hue_norm, sizes, size_order, size_norm, dashes, markers, style_order, units, estimator, ci, n_boot, sort, err_style, err_kws, legend)\u001b[0m\n\u001b[1;32m    660\u001b[0m                  sort=True, err_style=None, err_kws=None, legend=None):\n\u001b[1;32m    661\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 662\u001b[0;31m         plot_data = self.establish_variables(\n\u001b[0m\u001b[1;32m    663\u001b[0m             \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstyle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munits\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    664\u001b[0m         )\n",
      "\u001b[0;32m~/miniconda3/envs/datasci/lib/python3.8/site-packages/seaborn/relational.py\u001b[0m in \u001b[0;36mestablish_variables\u001b[0;34m(self, x, y, hue, size, style, units, data)\u001b[0m\n\u001b[1;32m    164\u001b[0m             err = (\"Either both or neither of `x` and `y` must be specified \"\n\u001b[1;32m    165\u001b[0m                    \"(but try passing to `data`, which is more flexible).\")\n\u001b[0;32m--> 166\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    167\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    168\u001b[0m         \u001b[0;31m# ---- Post-processing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Either both or neither of `x` and `y` must be specified (but try passing to `data`, which is more flexible)."
     ]
    }
   ],
   "source": [
    "# Wealth from low to high\n",
    "wealth_sorted = pd_wealth.sort_values()\n",
    "\n",
    "sns.lineplot(wealth_sorted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "ax = sns.kdeplot(pd_wealth, pd_ability, n_levels=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>Wealth</th>\n",
       "      <th>Ability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>51.838377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>58.520980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>53.904165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>51.689700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>47.114225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>0</td>\n",
       "      <td>30.606395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>1</td>\n",
       "      <td>50.560715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>1</td>\n",
       "      <td>73.077379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>0</td>\n",
       "      <td>38.861865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>0</td>\n",
       "      <td>56.889438</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Wealth    Ability\n",
       "0         0  51.838377\n",
       "1         0  58.520980\n",
       "2         0  53.904165\n",
       "3         0  51.689700\n",
       "4         0  47.114225\n",
       "..      ...        ...\n",
       "995       0  30.606395\n",
       "996       1  50.560715\n",
       "997       1  73.077379\n",
       "998       0  38.861865\n",
       "999       0  56.889438\n",
       "\n",
       "[1000 rows x 2 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wealth_ability = pd.concat([pd_wealth, pd_ability], axis=1)\n",
    "wealth_ability"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Agent with Maximum Ability and Wealth\n",
    "The agent with maximum ability did pretty well considering and is well above the 75 percentile. Suggesting that ability does correlate with final wealth outcomes. However, it cannot tell you whether you will be the wealthiest.\n",
    "\n",
    "The wealthiest agent has a pretty high ability too. It's very likely that the final wealth outcomes are dependent on luck."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Wealth      7.000000\n",
       "Ability    98.440539\n",
       "Name: 974, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Max Ability\n",
    "wealth_ability.iloc[wealth_ability['Ability'].idxmax()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Wealth     25.000000\n",
       "Ability    87.661499\n",
       "Name: 478, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Max Wealth\n",
    "wealth_ability.iloc[wealth_ability['Wealth'].idxmax()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Agent with Minimum Ability and Wealth\n",
    "Many agents with had an eventual wealth outcome of 0. You can have a decently high ability and end up with poor outcomes too. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Wealth     0.000000\n",
       "Ability    3.531823\n",
       "Name: 821, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Min Ability\n",
    "wealth_ability.iloc[wealth_ability['Ability'].idxmin()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Wealth      0.000000\n",
       "Ability    51.838377\n",
       "Name: 0, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Min Wealth\n",
    "wealth_ability.iloc[wealth_ability['Wealth'].idxmin()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apparently there are alot of 0 values for wealth. You can have very high ability and still have poor expected outcomes. **Big oof**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "83.83524493575716"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Apparently there are alot of 0 values. Let's find the maximum ability given 0 wealth.\n",
    "filtered = wealth_ability[wealth_ability['Wealth'] == 0]\n",
    "\n",
    "max(filtered[\"Ability\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Todo: Do an animation to visualize the evolution of wealth over the epoch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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