simulation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "81694814-a678-48f7-9a11-4bf955e01770",
   "metadata": {},
   "source": [
    "# Simulation on the role of skill and luck in success\n",
    "This is an attempt to reproduce the results in the famous Veritasium video on the role of skill and luck in success."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1d380bb5-f050-4061-a085-19a60bc84269",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from functools import partial"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25216ca2-cf4f-455e-acb0-062ff28c0bb4",
   "metadata": {},
   "source": [
    "## Generating distributions\n",
    "We are going to use a two different generators for our simulation:\n",
    " -  Uniform distribution\n",
    " -  Normal distribution (Mean 50, SD 17) with values clipped below 1 and above 100. ChatGPT suggested SD of 20, but I wanted to choose a value where 3 SD almost lie within the boundaries since we would be clipping at the boundaries.\n",
    "\n",
    "Currently, the luck and scores are generated via the same distribution and it might be worth trying out independent configurations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a2c26017-b4a4-4b88-83f3-d27e0ee14734",
   "metadata": {},
   "outputs": [],
   "source": [
    "class DistributionGenerator:\n",
    "    def __init__(self, seed):\n",
    "        # Adding some determinism to our random generator.\n",
    "        self._rng = np.random.default_rng(seed=seed)\n",
    "\n",
    "    def generate(self, n):\n",
    "        raise NotImplementedError(\"...\")\n",
    "\n",
    "    def _clip(self, data):\n",
    "        return np.clip(data, 0, 100)\n",
    "\n",
    "class NormalDistributionGenerator(DistributionGenerator):\n",
    "    def __init__(self, seed = 42, mean = 50, std_dev = 17):\n",
    "        super().__init__(seed)\n",
    "        self._mean = mean\n",
    "        self._std_dev = std_dev\n",
    "\n",
    "    def generate(self, n):\n",
    "        return self._clip(self._rng.normal(self._mean, self._std_dev, n))\n",
    "\n",
    "class UniformDistributionGenerator(DistributionGenerator):\n",
    "    def __init__(self, seed = 42):\n",
    "        super().__init__(seed)\n",
    "    \n",
    "    def generate(self, n):\n",
    "        return self._clip(self._rng.uniform(0, 100, n))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93b46f58-7891-4faf-96bc-80cb3133ee17",
   "metadata": {},
   "source": [
    "## Simulator\n",
    "\n",
    "We will run the analysis with similar parameters to the one in Veritasium -- 1000 rounds of simulation of 18300 applicants battling it out for 11 positions, where skills play 95% role and luck plays the remaining 5%.\n",
    "\n",
    "We will run both the generators and plot our outputs to try and figure out the role of luck!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "920e5ec2-2332-4891-899a-8e4032166358",
   "metadata": {},
   "outputs": [],
   "source": [
    "# CONSTANTS\n",
    "NUMBER_ROUNDS=1000\n",
    "NUMBER_APPLICANTS = 18300\n",
    "NUMBER_SELECTED = 11\n",
    "SKILL_WEIGHTAGE = 0.95"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fcb9e963-b954-458c-81a0-7bce7794e0dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Simulator:\n",
    "    def __init__(self, generator, skill_weight = SKILL_WEIGHTAGE, applicants = NUMBER_APPLICANTS, selected = NUMBER_SELECTED):\n",
    "        self._generator = generator\n",
    "        self._skill_weight = skill_weight\n",
    "        self._applicants = applicants\n",
    "        self._selected = selected\n",
    "\n",
    "    def run_simulation(self, num_rounds = NUMBER_ROUNDS):\n",
    "        data = np.empty((0, 3), dtype=object)\n",
    "        for _ in range(num_rounds):\n",
    "            current_data = self._run_round()\n",
    "            data = np.vstack((data, current_data))\n",
    "\n",
    "        return data\n",
    "\n",
    "    def _run_round(self):\n",
    "        skill_scores = self._generator.generate(self._applicants)\n",
    "        luck_scores = self._generator.generate(self._applicants)\n",
    "        overall_score = skill_scores * self._skill_weight + luck_scores * (1 - self._skill_weight)\n",
    "    \n",
    "        selected_applicants_indices = np.argsort(overall_score)[-self._selected:]\n",
    "        selected_skill_scores = skill_scores[selected_applicants_indices]\n",
    "        selected_luck_scores = luck_scores[selected_applicants_indices]\n",
    "    \n",
    "        avg_skill = np.mean(selected_skill_scores)\n",
    "        avg_luck = np.mean(selected_luck_scores)\n",
    "    \n",
    "        only_skill_based_selection = np.argsort(skill_scores)[-self._selected:]\n",
    "        common_chosen = np.intersect1d(selected_applicants_indices, only_skill_based_selection).size\n",
    "    \n",
    "        return np.array([avg_skill, avg_luck, common_chosen])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d5779077-ff19-4691-9697-4da68ab9c196",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_item(data, pos, label):\n",
    "    plt.boxplot(data, positions=[pos], labels=[label], showmeans=True)\n",
    "    mean_val = np.mean(data)\n",
    "    plt.annotate(f'Mean: {mean_val:.2f}', xy=(pos, mean_val), xytext=(pos+0.1, mean_val),\n",
    "             arrowprops=dict(facecolor='black', arrowstyle='->'))\n",
    "    \n",
    "\n",
    "def plot_helper(box_a, box_b):\n",
    "    label_a = 'Uniform Distribution'\n",
    "    plot_item(box_a, 1, label_a)\n",
    "    \n",
    "    label_b = 'Normal Distribution'\n",
    "    plot_item(box_b, 2, label_b)\n",
    "\n",
    "    plt.xlabel('Simulation type')\n",
    "    plt.ylabel('Value')\n",
    "    plt.legend([label_a, label_b])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "439671a7-fa7a-4ee3-b53c-2a7fba3c2f8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "normal_gen = NormalDistributionGenerator()\n",
    "normal_sim = Simulator(normal_gen)\n",
    "normal_output = normal_sim.run_simulation()\n",
    "\n",
    "uniform_gen = UniformDistributionGenerator()\n",
    "uniform_sim = Simulator(uniform_gen)\n",
    "uniform_out = uniform_sim.run_simulation()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ca79d6e-7f38-496a-8df2-4c4bc494fa41",
   "metadata": {},
   "source": [
    "## Plotting the data\n",
    "\n",
    "### Average skill level of selected candidates\n",
    "This is a plot of the average skill level of selected candidates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "27282f0b-27c3-4665-ba70-3971a27dd7ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_avg_skill(uniform_dist_skill, normal_dist_skill):\n",
    "    plot_helper(uniform_dist_skill, normal_dist_skill)\n",
    "    plt.title('Average Skill Box Plot')\n",
    "    plt.show()\n",
    "\n",
    "plot_avg_skill(uniform_out[:,0], normal_output[:,0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "334d39ce-f8d2-4a64-88c6-a747cd3680a6",
   "metadata": {},
   "source": [
    "### Average luck for selected candidates\n",
    "This is a plot of luckiness of selected candidates on average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dbbf886d-609e-41f3-b5d0-17852a420363",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_avg_luck(uniform_dist_luck, normal_dist_luck):\n",
    "    plot_helper(uniform_dist_luck, normal_dist_luck)\n",
    "    plt.title('Average Luck Box Plot')\n",
    "    plt.show()\n",
    "\n",
    "plot_avg_luck(uniform_out[:,1], normal_output[:,1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f24d31e6-07d5-4a45-9b8d-e3eeb3bc2a57",
   "metadata": {},
   "source": [
    "### Average count of people who would have been selected regardless of luck weightage\n",
    "This is a plot for the count of people who would have been selected in both (95% skill, 5% luck) as well as (100% skill, 0% luck) cases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d940f66b-1c80-4d2e-87f6-ee437cdf0907",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_top_chosen(uniform_dist_chosen, normal_dist_chosen):\n",
    "    plot_helper(uniform_dist_chosen, normal_dist_chosen)\n",
    "    plt.title('Top Skilled Selected Box Plot')\n",
    "    plt.show()\n",
    "\n",
    "plot_top_chosen(uniform_out[:,2], normal_output[:,2])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7110845b-3847-4c11-8c21-68af66fada68",
   "metadata": {},
   "source": [
    "## Conclusions\n",
    "\n",
    "Our uniform distribution simulation achieved results very close to that of the video, but our normal distribution simulation paints a more merit friendly picture. In both the simulations, we see that luck plays a fairly big part even though the people selected were highly skilled.\n",
    "\n",
    "The simulations can be improved with more realistic data (eg in a lot of cases, mean of distributions tends to be lower than the mid point) as well as generating data that is also less prone to clipping. We can also experiment by changing the parameters in the simulation."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "env",
   "language": "python",
   "name": "env"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}