lahdjirayhan/veritasium-hardwork-vs-luck.ipynb

## veritasium-hardwork-vs-luck.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## INTRODUCTION\n",
    "\n",
    "This notebook is inspired by Veritasium's [video](https://www.youtube.com/watch?v=3LopI4YeC4I) about acknowledging the contribution of luck in one's life and success.\n",
    "\n",
    "Veritasium is a YouTube channel posting videos explaining science, run by Derek Muller.\n",
    "\n",
    "**Is success the result of luck or hard work?**\n",
    "\n",
    "It is certainly a mix of both. However, most of already successful people often attribute their success to their own hard work (including also their skills, experience, endurance, persistence, and so on). Basically, any positive attribute that can be credited to oneself.\n",
    "\n",
    "Almost no one acknowledge their good luck as the cause of their success. It is because attributing success to chance feels like all the work done to achieve success is for nothing. Nevertheless, luck **does** play a role.\n",
    "\n",
    "Veritasium demonstrates that luck -- however small -- is still a considerable factor in success by running a simulation of a journey into success. He did a simulation of 2017 NASA astronaut program. From 18300 applicants, came out 11 astronauts graduated from the program.\n",
    "\n",
    "Derek runs a simple model by assigning random `hardwork/skill` score and `luck` score for each of 18300 people. The overall score is determined by 95% skill and 5% luck. Eleven candidate with highest overall score \"graduated\" to become astronauts. He shows that beside having high skill score, candidates that graduate also has high luck. In the event that luck plays no role (candidates only judged by their skill score alone), almost all the graduated candidates would have been replaced.\n",
    "\n",
    "## THIS NOTEBOOK\n",
    "\n",
    "Here I'd like to run the simulations like what Derek did in his video, along with some of my additions/improvements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import scipy.stats as ss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Definition of an Astronaut\n",
    "from collections import namedtuple\n",
    "Astronaut = namedtuple('Astronaut', ['id', 'skill', 'luck', 'overall'])\n",
    "\n",
    "# Definition of one Selection Process\n",
    "class SelectionProcess:\n",
    "    def __init__(self, skills, lucks, overalls):\n",
    "        array = np.stack([skills, lucks, overalls]).T\n",
    "        self.participants = [Astronaut(i, skill, luck, overall)\n",
    "                             for i, (skill, luck, overall) in enumerate(array)]\n",
    "        \n",
    "        self.selected_overall = sorted(self.participants,\n",
    "                                       key = lambda astronaut: astronaut.overall,\n",
    "                                       reverse = True)[0:11]\n",
    "        \n",
    "        self.selected_skill = sorted(self.participants,\n",
    "                                     key = lambda astronaut: astronaut.skill,\n",
    "                                     reverse = True)[0:11]\n",
    "        \n",
    "        self.lucky_participants = [astronaut for astronaut in self.selected_overall\n",
    "                                   if astronaut not in self.selected_skill]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Uniform distribution for both skill and luck\n",
    "# 1_000 different simulations of astronaut selection process.\n",
    "# 18_300 candidates each.\n",
    "# Weight of skill 95 : 5 luck\n",
    "\n",
    "N_CANDIDATE = 18_300\n",
    "N_SIMUL = 1_000\n",
    "\n",
    "skills = ss.uniform.rvs(size = (N_CANDIDATE, N_SIMUL), random_state = 302)\n",
    "lucks = ss.uniform.rvs(size = (N_CANDIDATE, N_SIMUL), random_state = 2207)\n",
    "overalls = 0.95 * skills + 0.05 * lucks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# I run the simulation 1000 times and record these values for each simulation:\n",
    "\n",
    "# 1. The average skill of graduated candidates\n",
    "# 2. The average luck of graduated candidates\n",
    "# 3. How many candidates are lucky, i.e. if luck is not involved at all they won't graduate to\n",
    "#    become astronauts.\n",
    "\n",
    "mean_skill_per_selectionprocess = []\n",
    "mean_luck_per_selectionprocess = []\n",
    "n_lucky_participants = []\n",
    "\n",
    "for batch in range(N_SIMUL):\n",
    "    sp = SelectionProcess(skills[:, batch], lucks[:, batch], overalls[:, batch])\n",
    "    \n",
    "    mean_skill_per_selectionprocess.append(\n",
    "        np.average([astronaut.skill for astronaut in sp.selected_overall])\n",
    "    )\n",
    "    \n",
    "    mean_luck_per_selectionprocess.append(\n",
    "        np.average([astronaut.luck for astronaut in sp.selected_overall])\n",
    "    )\n",
    "    \n",
    "    n_lucky_participants.append(\n",
    "        len(sp.lucky_participants)\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Count'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# This histogram shows the average skill of graduated candidates on each simulated selection process.\n",
    "# Note that the graduated candidates are incredibly skilled, with average of skill no less than 0.99\n",
    "\n",
    "sns.histplot(mean_skill_per_selectionprocess, kde = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Count'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# However, this histogram shows that the average luck of graduated candidates is also high.\n",
    "# Most selection processes end up with graduated candidates having about 0.95 luck score.\n",
    "\n",
    "sns.histplot(mean_luck_per_selectionprocess, kde = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.432"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This is the average number of lucky participants. This represents how many candidates would have\n",
    "# been replaced if luck is not considered in calculating the overall score, only skill.\n",
    "np.average(n_lucky_participants)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Rayhan\\Anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='count'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
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   ],
   "source": [
    "# And this histogram shows that most of the time, 9 or 10 candidates would have not graduated if\n",
    "# their (and all other's) luck is not accounted for. There are even cases where all 11 candidates\n",
    "# would not have graduated and replaced by other candidates entirely!\n",
    "\n",
    "sns.countplot(n_lucky_participants)"
   ]
  },
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   "metadata": {},
   "source": [
    "## CLOSING\n",
    "\n",
    "**Okay, so luck is something to be acknowledged. So what?**\n",
    "\n",
    "Derek argues in his video that attributing success entirely to oneself makes it easier to accept inequality. It makes someone easier to blame others unsuccessful for simply being less talented or working less hard. It makes one easier to have the behavior, \"I am successful because my hard work, others are not because they don't work as hard\". This is even *before* we consider malice, unequal privilege, or systematic injustice.\n",
    "\n",
    "Simple but extreme example of luck can be demonstrated by **where we are born**. If we happen to be born in the poorest nation on Earth, no matter how hard we work, we'll still be poor in comparison to someone born in first-world country. And we don't even control where we are born, thus, it can be considered a matter of how *lucky* we are to be born to a first-world country.\n",
    "\n",
    "Even if we're born in a first-world country, say, Western European one, we're not automatically rich nor successful. **Which class we are born into** is another coin flip.\n",
    "\n",
    "Even before we consider upbringing, education, and hardwork, luck already plays such a big role to give us a headstart or a handicap. And if you ask me, *I doubt that big of difference between headstart/handicap is worth attributing puny 5%.*\n",
    "\n",
    "**I'm interested in knowing more from the video you said.**\n",
    "\n",
    "Many of my points above are drawn/rephrased from Veritasium's video. If you're interested, you can check it here:\n",
    "\n",
    "https://www.youtube.com/watch?v=3LopI4YeC4I"
   ]
  }
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