{ "metadata": { "name": "", "signature": "sha256:ab49a504c736cb8343633aa317db2e45b61927a578421ffbcf99d54e2df73a10" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "#Who will be the next Mormon prophet?\n", "\n", "It just so happens that there's a a convenient pattern to whom is usually called to be the next prophet of *The Church of Jesus Christ of Latter-day Saints*. For most of the 20th century and all of the 21st century so far, the pattern has been that the prophet of the church is the most senior apostle (by date of calling to the Quorum of the Twelve) becomes the prophet when the previous prophet dies. There are 15 of these apostles at any given time: three in the \"First Presidency\", comprising the prophet and his two counselors, and twelve in the Quorum of the Twelve Apostles.\n", "\n", "Given the ages of the apostles and some average actuarial life tables (which we use here despite knowing that these men are generally far healthier than the average population), we can fairly easily calculate the likely age of death of the current apostles and rank them by seniority to find some likely scenarios for new prophets." ] }, { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "from datetime import datetime, timedelta\n", "from collections import defaultdict" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we load some actuarial life tables which give the population-level probability of death at any particular age." ] }, { "cell_type": "code", "collapsed": false, "input": [ "full_life_table = pd.read_csv('data/life_table.csv')\n", "full_life_table.set_index('Age',inplace=True)\n", "\n", "full_life_table[80:90]" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
MF
Age
80 0.061620 0.043899
81 0.068153 0.048807
82 0.075349 0.054374
83 0.083230 0.060661
84 0.091933 0.067751
85 0.101625 0.075729
86 0.112448 0.084673
87 0.124502 0.094645
88 0.137837 0.105694
89 0.152458 0.117853
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 2, "text": [ " M F\n", "Age \n", "80 0.061620 0.043899\n", "81 0.068153 0.048807\n", "82 0.075349 0.054374\n", "83 0.083230 0.060661\n", "84 0.091933 0.067751\n", "85 0.101625 0.075729\n", "86 0.112448 0.084673\n", "87 0.124502 0.094645\n", "88 0.137837 0.105694\n", "89 0.152458 0.117853" ] } ], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "# grab the values for the \"Male\" column\n", "prob_death = full_life_table.M.values" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "It would be slightly more convient to work with these values if we knew, for a man of any particular age, the probability of being alive at any age. We calculate a new life table which has along each row $x$ the probability that a man of age $x$ will live to reach age $y$." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Create a new life table\n", "l = len(prob_death)\n", "life_table = np.ones((l,l))\n", "for i in range(0,l,1):\n", " for j in range(i,l,1):\n", " life_table[i][j]=np.prod(1 - prob_death[i:j+1])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "# Load current apostle ages and seniority\n", "apostle_data = pd.read_csv('data/apostles.csv')\n", "apostle_data" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
NameBirthTwelveOrdainedSeniority
0 Thomas S. Monson 08/21/1927 10/04/1963 10/10/1963 1
1 Boyd K. Packer 09/10/1924 04/06/1970 04/09/1970 2
2 L. Tom Perry 08/05/1922 04/06/1974 04/11/1974 3
3 Russell M. Nelson 09/09/1924 04/07/1984 04/12/1984 4
4 Dallin H. Oaks 08/12/1932 04/07/1984 05/03/1984 5
5 M. Russell Ballard 10/08/1928 10/06/1985 10/10/1985 6
6 Richard G. Scott 11/07/1928 10/01/1988 10/06/1988 7
7 Robert D. Hales 08/24/1932 04/07/1994 04/07/1994 8
8 Jeffrey R. Holland 12/03/1940 06/23/1994 06/23/1994 9
9 Henry B. Eyring 05/31/1933 04/01/1995 04/01/1995 10
10 Dieter F. Uchtdorf 11/06/1940 10/02/2004 10/07/2004 11
11 David A. Bednar 06/15/1952 10/07/2004 10/07/2004 12
12 Quentin L. Cook 09/08/1940 10/06/2007 10/11/2007 13
13 D. Todd Christofferson 01/24/1945 04/05/2008 04/10/2008 14
14 Neil L. Andersen 08/09/1951 04/04/2009 04/09/2009 15
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 5, "text": [ " Name Birth Twelve Ordained Seniority\n", "0 Thomas S. Monson 08/21/1927 10/04/1963 10/10/1963 1\n", "1 Boyd K. Packer 09/10/1924 04/06/1970 04/09/1970 2\n", "2 L. Tom Perry 08/05/1922 04/06/1974 04/11/1974 3\n", "3 Russell M. Nelson 09/09/1924 04/07/1984 04/12/1984 4\n", "4 Dallin H. Oaks 08/12/1932 04/07/1984 05/03/1984 5\n", "5 M. Russell Ballard 10/08/1928 10/06/1985 10/10/1985 6\n", "6 Richard G. Scott 11/07/1928 10/01/1988 10/06/1988 7\n", "7 Robert D. Hales 08/24/1932 04/07/1994 04/07/1994 8\n", "8 Jeffrey R. Holland 12/03/1940 06/23/1994 06/23/1994 9\n", "9 Henry B. Eyring 05/31/1933 04/01/1995 04/01/1995 10\n", "10 Dieter F. Uchtdorf 11/06/1940 10/02/2004 10/07/2004 11\n", "11 David A. Bednar 06/15/1952 10/07/2004 10/07/2004 12\n", "12 Quentin L. Cook 09/08/1940 10/06/2007 10/11/2007 13\n", "13 D. Todd Christofferson 01/24/1945 04/05/2008 04/10/2008 14\n", "14 Neil L. Andersen 08/09/1951 04/04/2009 04/09/2009 15" ] } ], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Helper class\n", "\n", "For programming convenience, this class captures some simple calculations we will need to do for each prophet." ] }, { "cell_type": "code", "collapsed": false, "input": [ "class Apostle:\n", " def __init__(self,name,birth,twelve,ordained,seniority):\n", " self.name=name\n", " self.birth=datetime.strptime(birth,'%m/%d/%Y')\n", " self.twelve=datetime.strptime(twelve,'%m/%d/%Y')\n", " self.ordained=datetime.strptime(ordained,'%m/%d/%Y')\n", " self.seniority=seniority\n", " \n", " def __str__(self):\n", " return self.name\n", " \n", " def age(self,year):\n", " \"\"\"Return the apostle's age in a particular year.\"\"\"\n", " return year-self.birth.year\n", " \n", " def current_age(self):\n", " \"\"\"Return the apostle's age in the current year.\"\"\"\n", " current_year = datetime.now().year\n", " return self.age(current_year)\n", " \n", " def most_probable_death_year(self,life_table):\n", " \"\"\"Return the apostle's most likely year of death given\n", " a particular life table.\"\"\"\n", " current_age = self.current_age()\n", " age_of_death = np.argmax(life_table[current_age]<.5)\n", " return self.birth.year + age_of_death\n", " \n", " def most_probable_life_state(self,year,life_table):\n", " \"\"\"Return True if, given the most probable year of death,\n", " the apostle is alive in any part of a particular year\n", " given a particular life table.\"\"\"\n", " return year <= self.most_probable_death_year(life_table)\n", " \n", " def simulate_death_year(self,life_table):\n", " \"\"\"Return a death year randomly drawn from the distribution\n", " of likely death years given a particular life table.\"\"\"\n", " death_year = np.argmin(life_table[self.current_age()] > np.random.random(len(life_table[0])))\n", " return self.birth.year + death_year\n", " \n", " def simulate_life_state(self,year,life_table):\n", " \"\"\"Return True if the apostle is alive given a year of death\n", " drawn from the distribution of likely death years given\n", " by a particular life table.\"\"\"\n", " life_state = self.simulate_death_year(life_table)\n", " return year <= self.simulate_death_year(life_table)\n", " " ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "# Create apostle objects and print their ages\n", "apostles = []\n", "for i,row in apostle_data.iterrows():\n", " apostle = Apostle(row.Name,row.Birth,row.Twelve,row.Ordained,row.Seniority)\n", " apostles.append(apostle)\n", " print \"{}. {} is {} years old\".format(apostle.seniority,apostle.name,apostle.current_age())\n", " " ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1. Thomas S. Monson is 87 years old\n", "2. Boyd K. Packer is 90 years old\n", "3. L. Tom Perry is 92 years old\n", "4. Russell M. Nelson is 90 years old\n", "5. Dallin H. Oaks is 82 years old\n", "6. M. Russell Ballard is 86 years old\n", "7. Richard G. Scott is 86 years old\n", "8. Robert D. Hales is 82 years old\n", "9. Jeffrey R. Holland is 74 years old\n", "10. Henry B. Eyring is 81 years old\n", "11. Dieter F. Uchtdorf is 74 years old\n", "12. David A. Bednar is 62 years old\n", "13. Quentin L. Cook is 74 years old\n", "14. D. Todd Christofferson is 69 years old\n", "15. Neil L. Andersen is 63 years old\n" ] } ], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Methodology\n", "Now we define two more functions to help us calculate who is prophet in a particular year.\n", "Each of these functions uses a different method to calculate who will be prophet:\n", "\n", "#### Method 1: Winner takes all\n", "The simplest method simply calculates which year each apostle is likely to die in (by\n", "taking the first year they are more likely to be dead than alive) and returns the most\n", "senior living apostle who is more likely to be alive than dead.\n", "\n", "#### Method 2: Monte-Carlo simulation\n", "A slightly more interesting (and more robust) method runs a simulation for each apostle,\n", "making draws from the whole distribution of probable years of death. If we run this\n", "simulation many many times, we will end up with estimates of the probability that each\n", "apostle will be prophet in any particular year. This method will end up giving us a\n", "clearer picture than the winner takes all method.\n", "\n", "#### Method 3: Accounting for health\n", "This method repeats the Monte-Carlo simulation but allows for manual adjustment of the health of\n", "the apostles be adding or subtracting years from their life. By default, since all have lived very healthy\n", "lives, eight years are subtracted from each of their ages (to simulate an average life expectancy of 85. From there, up to two years is added or subtracted to account for perceived health status (very unhealthy, unhealty, normal, healthy, very healthy)." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# define some helper functions for calculating who is prophet in a particular year\n", "# using two different methods\n", "\n", "def most_probable_prophet_in_year(apostles,year,life_table):\n", " \"\"\"Return the apostle (given a list of apostles) most likely\n", " to be prophet in a particular year\"\"\"\n", " apostles_alive = [apostle for apostle in apostles\n", " if apostle.most_probable_life_state(year,life_table)]\n", " \n", " if len(apostles_alive) == 0:\n", " return None\n", " \n", " apostle_index = np.argmin([apostle.seniority for apostle in apostles_alive])\n", " return apostles_alive[apostle_index]\n", "\n", "def simulate_prophet_in_year(apostles,year,life_table):\n", " \"\"\"Return the apostle (given a list of apostles) who is prophet\n", " in a particular year after simulating each apostle's life state\n", " in the given year.\"\"\"\n", " apostles_alive = [apostle for apostle in apostles\n", " if apostle.simulate_life_state(year,life_table)]\n", " if len(apostles_alive) == 0:\n", " return None\n", " \n", " apostle_index = np.argmin([apostle.seniority for apostle in apostles_alive])\n", " return apostles_alive[apostle_index]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "# Plot a histogram of each apostle's likely death years\n", "for apostle in apostles:\n", " death_year_dist = []\n", " for i in range(10000):\n", " death_year_dist.append(apostle.simulate_death_year(life_table))\n", "\n", " plt.hist(death_year_dist,bins=range(2014,2040))\n", " plt.title(\"Histogram of year of death of {}\".format(apostle.name))\n", " plt.show()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "# Given our model, who is most likely to be prophet in the year 2020?\n", "print most_probable_prophet_in_year(apostles,2020,life_table)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Dallin H. Oaks\n" ] } ], "prompt_number": 10 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Method 1" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Given our model, who is most likely to be prophet in every year from now until 2040?\n", "for year in range(2014,2040):\n", " print \"{}: {}\".format(year, most_probable_prophet_in_year(apostles,year,life_table))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "2014: Thomas S. Monson\n", "2015: Thomas S. Monson\n", "2016: Thomas S. Monson\n", "2017: Thomas S. Monson\n", "2018: Thomas S. Monson\n", "2019: Dallin H. Oaks\n", "2020: Dallin H. Oaks\n", "2021: Jeffrey R. Holland\n", "2022: Jeffrey R. Holland\n", "2023: Jeffrey R. Holland\n", "2024: Jeffrey R. Holland\n", "2025: Jeffrey R. Holland\n", "2026: David A. Bednar\n", "2027: David A. Bednar\n", "2028: David A. Bednar\n", "2029: David A. Bednar\n", "2030: David A. Bednar\n", "2031: David A. Bednar\n", "2032: David A. Bednar\n", "2033: David A. Bednar\n", "2034: David A. Bednar\n", "2035: None\n", "2036: None\n", "2037: None\n", "2038: None\n", "2039: None\n" ] } ], "prompt_number": 11 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Method 2 - simulation:\n", "\n", "What if we looked at the probabilities over 10000 trials? This would give us a\n", "more accurate picture of how likely it is that any of the current apostles\n", "will be prophet in any particular year. Note that it is possible (and probable)\n", "that in some of the later years none of the current apostles will be alive. In\n", "these cases, I have assigned a probability of prophethood to \"other\"." ] }, { "cell_type": "code", "collapsed": false, "input": [ "def run_simulation(n_trials,apostles,life_table):\n", " for year in range(2014,2040):\n", " prophets = defaultdict(list)\n", " for i in range(n_trials):\n", " prophet = simulate_prophet_in_year(apostles,year,life_table)\n", " if prophet is not None:\n", " prophets[prophet.name].append(1)\n", " else:\n", " prophets[\"other\"].append(1)\n", " probabilities = []\n", " for key,count in prophets.items():\n", " probabilities.append((key,float(len(count))/n_trials))\n", " probabilities = sorted(probabilities,key=lambda x: x[1],reverse=True)\n", " print year\n", " for name,probability in probabilities:\n", " print \" {} ({})\".format(name,probability)\n", " print" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 12 }, { "cell_type": "code", "collapsed": false, "input": [ "run_simulation(10000,apostles,life_table)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "2014\n", " Thomas S. Monson (1.0)\n", "\n", "2015" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Thomas S. Monson (0.8719)\n", " Boyd K. Packer (0.1071)\n", " L. Tom Perry (0.017)\n", " Russell M. Nelson (0.0029)\n", " Dallin H. Oaks (0.001)\n", " M. Russell Ballard (0.0001)\n", "\n", "2016" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Thomas S. Monson (0.6626)\n", " Boyd K. Packer (0.1932)\n", " L. Tom Perry (0.0702)\n", " Russell M. Nelson (0.042)\n", " Dallin H. Oaks (0.0249)\n", " M. Russell Ballard (0.0046)\n", " Richard G. Scott (0.0018)\n", " Robert D. Hales (0.0007)\n", "\n", "2017" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Thomas S. Monson (0.4135)\n", " Boyd K. Packer (0.1755)\n", " Dallin H. Oaks (0.1323)\n", " Russell M. Nelson (0.0947)\n", " L. Tom Perry (0.0934)\n", " M. Russell Ballard (0.0395)\n", " Richard G. Scott (0.0224)\n", " Robert D. Hales (0.0184)\n", " Jeffrey R. Holland (0.0085)\n", " Henry B. Eyring (0.0012)\n", " Dieter F. Uchtdorf (0.0003)\n", " David A. Bednar (0.0003)\n", "\n", "2018" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Dallin H. Oaks (0.2291)\n", " Thomas S. Monson (0.2185)\n", " Boyd K. Packer (0.0996)\n", " M. Russell Ballard (0.0817)\n", " Russell M. Nelson (0.0817)\n", " Robert D. Hales (0.074)\n", " Jeffrey R. Holland (0.0675)\n", " Richard G. Scott (0.0607)\n", " L. Tom Perry (0.056)\n", " Henry B. Eyring (0.0152)\n", " Dieter F. Uchtdorf (0.0101)\n", " David A. Bednar (0.0051)\n", " Quentin L. Cook (0.0006)\n", " D. Todd Christofferson (0.0002)\n", "\n", "2019" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Dallin H. Oaks (0.2114)\n", " Jeffrey R. Holland (0.19)\n", " Robert D. Hales (0.1189)\n", " Thomas S. Monson (0.0977)\n", " M. Russell Ballard (0.0727)\n", " Richard G. Scott (0.064)\n", " Dieter F. Uchtdorf (0.0612)\n", " Henry B. Eyring (0.0483)\n", " David A. Bednar (0.0403)\n", " Boyd K. Packer (0.0355)\n", " Russell M. Nelson (0.0338)\n", " L. Tom Perry (0.016)\n", " Quentin L. Cook (0.0046)\n", " D. Todd Christofferson (0.0043)\n", " Neil L. Andersen (0.0009)\n", " other (0.0004)\n", "\n", "2020" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Jeffrey R. Holland (0.265)\n", " Dallin H. Oaks (0.132)\n", " David A. Bednar (0.1312)\n", " Dieter F. Uchtdorf (0.125)\n", " Robert D. Hales (0.1039)\n", " Henry B. Eyring (0.0652)\n", " Richard G. Scott (0.04)\n", " M. Russell Ballard (0.0351)\n", " Thomas S. Monson (0.0331)\n", " Quentin L. Cook (0.019)\n", " D. Todd Christofferson (0.0188)\n", " Boyd K. Packer (0.0088)\n", " Neil L. Andersen (0.0084)\n", " Russell M. Nelson (0.0077)\n", " other (0.0036)\n", " L. Tom Perry (0.0032)\n", "\n", "2021" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " David A. Bednar (0.2577)\n", " Jeffrey R. Holland (0.249)\n", " Dieter F. Uchtdorf (0.1565)\n", " Dallin H. Oaks (0.0629)\n", " Robert D. Hales (0.0594)\n", " Henry B. Eyring (0.0473)\n", " D. Todd Christofferson (0.041)\n", " Quentin L. Cook (0.0364)\n", " Neil L. Andersen (0.0331)\n", " other (0.0195)\n", " Richard G. Scott (0.0133)\n", " M. Russell Ballard (0.0123)\n", " Thomas S. Monson (0.0082)\n", " Russell M. Nelson (0.0016)\n", " Boyd K. Packer (0.0015)\n", " L. Tom Perry (0.0003)\n", "\n", "2022" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " David A. Bednar (0.3282)\n", " Jeffrey R. Holland (0.1894)\n", " Dieter F. Uchtdorf (0.137)\n", " Neil L. Andersen (0.0747)\n", " D. Todd Christofferson (0.0725)\n", " other (0.0573)\n", " Quentin L. Cook (0.0505)\n", " Henry B. Eyring (0.0305)\n", " Dallin H. Oaks (0.0249)\n", " Robert D. Hales (0.0241)\n", " M. Russell Ballard (0.0041)\n", " Richard G. Scott (0.0038)\n", " Thomas S. Monson (0.0025)\n", " Boyd K. Packer (0.0004)\n", " Russell M. Nelson (0.0001)\n", "\n", "2023" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " David A. Bednar (0.364)\n", " other (0.1301)\n", " Jeffrey R. Holland (0.121)\n", " Neil L. Andersen (0.1134)\n", " Dieter F. Uchtdorf (0.1074)\n", " D. Todd Christofferson (0.0892)\n", " Quentin L. Cook (0.046)\n", " Henry B. Eyring (0.0103)\n", " Dallin H. Oaks (0.0088)\n", " Robert D. Hales (0.0087)\n", " M. Russell Ballard (0.0004)\n", " Richard G. Scott (0.0004)\n", " Thomas S. Monson (0.0003)\n", "\n", "2024" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " David A. Bednar (0.3459)\n", " other (0.2551)\n", " Neil L. Andersen (0.1403)\n", " D. Todd Christofferson (0.0896)\n", " Jeffrey R. Holland (0.0663)\n", " Dieter F. Uchtdorf (0.0576)\n", " Quentin L. Cook (0.0363)\n", " Henry B. Eyring (0.0038)\n", " Dallin H. Oaks (0.0025)\n", " Robert D. Hales (0.0025)\n", " Richard G. Scott (0.0001)\n", "\n", "2025" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " other (0.3829)\n", " David A. Bednar (0.2886)\n", " Neil L. Andersen (0.1573)\n", " D. Todd Christofferson (0.078)\n", " Dieter F. Uchtdorf (0.0362)\n", " Jeffrey R. Holland (0.0348)\n", " Quentin L. Cook (0.0206)\n", " Henry B. Eyring (0.001)\n", " Robert D. Hales (0.0005)\n", " Dallin H. Oaks (0.0001)\n", "\n", "2026" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " other (0.5042)\n", " David A. Bednar (0.2446)\n", " Neil L. Andersen (0.1506)\n", " D. Todd Christofferson (0.0578)\n", " Jeffrey R. Holland (0.0168)\n", " Dieter F. Uchtdorf (0.0144)\n", " Quentin L. Cook (0.0115)\n", " Dallin H. Oaks (0.0001)\n", "\n", "2027" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " other (0.6426)\n", " David A. Bednar (0.1838)\n", " Neil L. Andersen (0.1191)\n", " D. Todd Christofferson (0.0353)\n", " Dieter F. Uchtdorf (0.0075)\n", " Jeffrey R. Holland (0.0063)\n", " Quentin L. Cook (0.0054)\n", "\n", "2028" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " other (0.7438)\n", " David A. Bednar (0.1305)\n", " Neil L. Andersen (0.0975)\n", " D. Todd Christofferson (0.021)\n", " Dieter F. Uchtdorf (0.003)\n", " Jeffrey R. Holland (0.0021)\n", " Quentin L. Cook (0.0021)\n", "\n", "2029" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " other (0.8325)\n", " David A. Bednar (0.0903)\n", " Neil L. Andersen (0.065)\n", " D. Todd Christofferson (0.0103)\n", " Dieter F. Uchtdorf (0.0007)\n", " Quentin L. Cook (0.0007)\n", " Jeffrey R. Holland (0.0005)\n", "\n", "2030" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " other (0.8883)\n", " David A. Bednar (0.0609)\n", " Neil L. Andersen (0.0451)\n", " D. Todd Christofferson (0.0053)\n", " Quentin L. Cook (0.0002)\n", " Jeffrey R. Holland (0.0001)\n", " Dieter F. Uchtdorf (0.0001)\n", "\n", "2031" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " other (0.9302)\n", " David A. Bednar (0.0399)\n", " Neil L. Andersen (0.0282)\n", " D. Todd Christofferson (0.0016)\n", " Jeffrey R. Holland (0.0001)\n", "\n", "2032" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " other (0.9614)\n", " David A. Bednar (0.0248)\n", " Neil L. Andersen (0.0128)\n", " D. Todd Christofferson (0.001)\n", "\n", "2033" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " other (0.9788)\n", " David A. Bednar (0.0126)\n", " Neil L. Andersen (0.0083)\n", " D. Todd Christofferson (0.0003)\n", "\n", "2034" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " other (0.99)\n", " David A. Bednar (0.0067)\n", " Neil L. Andersen (0.0033)\n", "\n", "2035" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " other (0.9938)\n", " David A. Bednar (0.0043)\n", " Neil L. Andersen (0.0019)\n", "\n", "2036" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " other (0.9979)\n", " David A. Bednar (0.0011)\n", " Neil L. Andersen (0.001)\n", "\n", "2037" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " other (0.9991)\n", " David A. Bednar (0.0007)\n", " Neil L. Andersen (0.0002)\n", "\n", "2038" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " other (0.9997)\n", " Neil L. Andersen (0.0002)\n", " David A. Bednar (0.0001)\n", "\n", "2039" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " other (0.9999)\n", " David A. Bednar (0.0001)\n", "\n" ] } ], "prompt_number": 13 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Method 2 - conclusions\n", "\n", "Interestingly, for many of the years far down the line, the apostle most likely to be prophet is far from clear.\n", "\n", "By 2026, only twelve years from now, our model says that it is more likely that someone other than one of the current apostles will be prophet. However, since our model systematically underestimates the age of death of these men, we should take this number with a grain of salt. By 2039, 25 years from now, it's extraordinarily unlikely that any of the current apostles will still be alive.\n", "\n", "#### Caveats\n", "This model did not explicitly take health state into account, but approximated it using current age. Method 3 is one way of accounting for this.\n", "\n", "### Method 3 - adjusting for health and systematic mortality biases.\n", "One way to approximate of actual health status is to adjust their ages to reflect both their current health and the greater-than-average overall health of their demographic." ] }, { "cell_type": "code", "collapsed": false, "input": [ "default = 8\n", "apostle_age_adjustments = {\n", " \"Thomas S. Monson\": default - 2,\n", " \"Boyd K. Packer\": default - 2,\n", " \"L. Tom Perry\": default + 1,\n", " \"Russell M. Nelson\": default + 2,\n", " \"Dallin H. Oaks\": default + 1,\n", " \"M. Russell Ballard\": default,\n", " \"Richard G. Scott\": default - 2,\n", " \"Robert D. Hales\": default + 2,\n", " \"Jeffrey R. Holland\": default,\n", " \"Henry B. Eyring\": default,\n", " \"Dieter F. Uchtdorf\": default,\n", " \"David A. Bednar\": default,\n", " \"Quentin L. Cook\": default,\n", " \"D. Todd Christofferson\": default,\n", " \"Neil L. Andersen\": default}" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "adj_apostles = []\n", "for apostle in apostles:\n", " age_adj = apostle_age_adjustments[apostle.name]\n", " adj_apostles.append(Apostle(apostle.name,\n", " (apostle.birth + timedelta(days=age_adj*365)).strftime(\"%m/%d/%Y\"),\n", " apostle.twelve.strftime(\"%m/%d/%Y\"),\n", " apostle.ordained.strftime(\"%m/%d/%Y\"),\n", " apostle.seniority))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "run_simulation(10000,adj_apostles,life_table)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "2014\n", " Thomas S. Monson (1.0)\n", "\n", "2015" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Thomas S. Monson (0.9338)\n", " Boyd K. Packer (0.0598)\n", " L. Tom Perry (0.0055)\n", " Russell M. Nelson (0.0009)\n", "\n", "2016" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Thomas S. Monson (0.8046)\n", " Boyd K. Packer (0.1447)\n", " L. Tom Perry (0.04)\n", " Russell M. Nelson (0.0092)\n", " Dallin H. Oaks (0.0015)\n", "\n", "2017" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Thomas S. Monson (0.6346)\n", " Boyd K. Packer (0.1939)\n", " L. Tom Perry (0.1002)\n", " Russell M. Nelson (0.0493)\n", " Dallin H. Oaks (0.0174)\n", " M. Russell Ballard (0.0035)\n", " Jeffrey R. Holland (0.0004)\n", " Robert D. Hales (0.0004)\n", " Richard G. Scott (0.0003)\n", "\n", "2018" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Thomas S. Monson (0.4512)\n", " Boyd K. Packer (0.1879)\n", " L. Tom Perry (0.1355)\n", " Russell M. Nelson (0.1124)\n", " Dallin H. Oaks (0.0801)\n", " M. Russell Ballard (0.02)\n", " Richard G. Scott (0.0063)\n", " Robert D. Hales (0.0051)\n", " Jeffrey R. Holland (0.0012)\n", " Dieter F. Uchtdorf (0.0003)\n", "\n", "2019" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Thomas S. Monson (0.2942)\n", " Dallin H. Oaks (0.1737)\n", " Russell M. Nelson (0.1458)\n", " L. Tom Perry (0.1322)\n", " Boyd K. Packer (0.1306)\n", " M. Russell Ballard (0.0502)\n", " Robert D. Hales (0.03)\n", " Richard G. Scott (0.0225)\n", " Jeffrey R. Holland (0.0157)\n", " Henry B. Eyring (0.0031)\n", " Dieter F. Uchtdorf (0.0013)\n", " David A. Bednar (0.0006)\n", " Quentin L. Cook (0.0001)\n", "\n", "2020" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Dallin H. Oaks (0.2416)\n", " Thomas S. Monson (0.1635)\n", " Russell M. Nelson (0.125)\n", " Robert D. Hales (0.0874)\n", " L. Tom Perry (0.0866)\n", " M. Russell Ballard (0.0837)\n", " Boyd K. Packer (0.0747)\n", " Jeffrey R. Holland (0.0623)\n", " Richard G. Scott (0.0458)\n", " Henry B. Eyring (0.0131)\n", " Dieter F. Uchtdorf (0.0098)\n", " David A. Bednar (0.0054)\n", " Quentin L. Cook (0.0006)\n", " D. Todd Christofferson (0.0003)\n", " Neil L. Andersen (0.0002)\n", "\n", "2021" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Dallin H. Oaks (0.2492)\n", " Robert D. Hales (0.1389)\n", " Jeffrey R. Holland (0.132)\n", " Russell M. Nelson (0.0902)\n", " Thomas S. Monson (0.0865)\n", " M. Russell Ballard (0.0791)\n", " Richard G. Scott (0.0433)\n", " L. Tom Perry (0.0414)\n", " Dieter F. Uchtdorf (0.0385)\n", " Henry B. Eyring (0.038)\n", " Boyd K. Packer (0.0294)\n", " David A. Bednar (0.026)\n", " Quentin L. Cook (0.0041)\n", " D. Todd Christofferson (0.0021)\n", " Neil L. Andersen (0.0012)\n", " other (0.0001)\n", "\n", "2022" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Dallin H. Oaks (0.2046)\n", " Jeffrey R. Holland (0.198)\n", " Robert D. Hales (0.1533)\n", " Dieter F. Uchtdorf (0.085)\n", " David A. Bednar (0.0739)\n", " Henry B. Eyring (0.0588)\n", " M. Russell Ballard (0.0586)\n", " Russell M. Nelson (0.048)\n", " Thomas S. Monson (0.0371)\n", " Richard G. Scott (0.0305)\n", " L. Tom Perry (0.0158)\n", " Quentin L. Cook (0.0115)\n", " D. Todd Christofferson (0.0096)\n", " Boyd K. Packer (0.009)\n", " Neil L. Andersen (0.0044)\n", " other (0.0019)\n", "\n", "2023" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Jeffrey R. Holland (0.2333)\n", " David A. Bednar (0.1466)\n", " Dallin H. Oaks (0.139)\n", " Robert D. Hales (0.1301)\n", " Dieter F. Uchtdorf (0.1267)\n", " Henry B. Eyring (0.0578)\n", " M. Russell Ballard (0.0356)\n", " Quentin L. Cook (0.0294)\n", " D. Todd Christofferson (0.024)\n", " Russell M. Nelson (0.0192)\n", " Richard G. Scott (0.0165)\n", " Neil L. Andersen (0.0149)\n", " Thomas S. Monson (0.0105)\n", " other (0.0081)\n", " L. Tom Perry (0.0051)\n", " Boyd K. Packer (0.0032)\n", "\n", "2024" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " David A. Bednar (0.2269)\n", " Jeffrey R. Holland (0.2171)\n", " Dieter F. Uchtdorf (0.1474)\n", " Robert D. Hales (0.0925)\n", " Dallin H. Oaks (0.0851)\n", " D. Todd Christofferson (0.047)\n", " Henry B. Eyring (0.0461)\n", " Quentin L. Cook (0.0377)\n", " Neil L. Andersen (0.0358)\n", " other (0.0265)\n", " M. Russell Ballard (0.017)\n", " Russell M. Nelson (0.0077)\n", " Richard G. Scott (0.0067)\n", " Thomas S. Monson (0.0049)\n", " L. Tom Perry (0.0013)\n", " Boyd K. Packer (0.0003)\n", "\n", "2025" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " David A. Bednar (0.2883)\n", " Jeffrey R. Holland (0.1835)\n", " Dieter F. Uchtdorf (0.1288)\n", " Neil L. Andersen (0.0688)\n", " D. Todd Christofferson (0.0671)\n", " other (0.0634)\n", " Robert D. Hales (0.0604)\n", " Quentin L. Cook (0.0494)\n", " Dallin H. Oaks (0.0459)\n", " Henry B. Eyring (0.0335)\n", " M. Russell Ballard (0.0054)\n", " Russell M. Nelson (0.0018)\n", " Richard G. Scott (0.0015)\n", " Thomas S. Monson (0.0015)\n", " L. Tom Perry (0.0005)\n", " Boyd K. Packer (0.0002)\n", "\n", "2026" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " David A. Bednar (0.3145)\n", " Jeffrey R. Holland (0.1304)\n", " other (0.1249)\n", " Dieter F. Uchtdorf (0.1111)\n", " Neil L. Andersen (0.0989)\n", " D. Todd Christofferson (0.0832)\n", " Quentin L. Cook (0.0564)\n", " Robert D. Hales (0.0314)\n", " Dallin H. Oaks (0.0254)\n", " Henry B. Eyring (0.0203)\n", " M. Russell Ballard (0.0023)\n", " Richard G. Scott (0.0006)\n", " Russell M. Nelson (0.0005)\n", " Boyd K. Packer (0.0001)\n", "\n", "2027" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " David A. Bednar (0.3186)\n", " other (0.2093)\n", " Neil L. Andersen (0.1241)\n", " Jeffrey R. Holland (0.096)\n", " D. Todd Christofferson (0.0873)\n", " Dieter F. Uchtdorf (0.0834)\n", " Quentin L. Cook (0.044)\n", " Robert D. Hales (0.0158)\n", " Dallin H. Oaks (0.0108)\n", " Henry B. Eyring (0.0101)\n", " M. 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Andersen (0.1383)\n", " D. Todd Christofferson (0.0548)\n", " Dieter F. Uchtdorf (0.0189)\n", " Jeffrey R. Holland (0.017)\n", " Quentin L. Cook (0.0145)\n", " Robert D. Hales (0.0009)\n", " Henry B. Eyring (0.0005)\n", " Dallin H. Oaks (0.0005)\n", "\n", "2031" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " other (0.6445)\n", " David A. Bednar (0.1731)\n", " Neil L. Andersen (0.1179)\n", " D. Todd Christofferson (0.0385)\n", " Jeffrey R. Holland (0.0093)\n", " Dieter F. Uchtdorf (0.0084)\n", " Quentin L. Cook (0.0076)\n", " Robert D. Hales (0.0004)\n", " Henry B. Eyring (0.0002)\n", " Dallin H. Oaks (0.0001)\n", "\n", "2032" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " other (0.7402)\n", " David A. Bednar (0.1286)\n", " Neil L. Andersen (0.0933)\n", " D. Todd Christofferson (0.0249)\n", " Jeffrey R. Holland (0.0054)\n", " Dieter F. Uchtdorf (0.0048)\n", " Quentin L. Cook (0.0027)\n", " Dallin H. Oaks (0.0001)\n", "\n", "2033" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " other (0.8066)\n", " David A. Bednar (0.0965)\n", " Neil L. Andersen (0.0729)\n", " D. Todd Christofferson (0.0185)\n", " Dieter F. Uchtdorf (0.0024)\n", " Jeffrey R. Holland (0.0018)\n", " Quentin L. Cook (0.0013)\n", "\n", "2034" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " other (0.866)\n", " David A. Bednar (0.0697)\n", " Neil L. Andersen (0.0553)\n", " D. Todd Christofferson (0.0077)\n", " Dieter F. Uchtdorf (0.0008)\n", " Jeffrey R. Holland (0.0004)\n", " Quentin L. Cook (0.0001)\n", "\n", "2035" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " other (0.9032)\n", " David A. Bednar (0.0509)\n", " Neil L. Andersen (0.0395)\n", " D. Todd Christofferson (0.0056)\n", " Quentin L. Cook (0.0004)\n", " Jeffrey R. Holland (0.0002)\n", " Dieter F. Uchtdorf (0.0002)\n", "\n", "2036" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " other (0.9322)\n", " David A. Bednar (0.0368)\n", " Neil L. Andersen (0.0284)\n", " D. Todd Christofferson (0.0024)\n", " Jeffrey R. Holland (0.0001)\n", " Dieter F. Uchtdorf (0.0001)\n", "\n", "2037" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " other (0.9626)\n", " David A. Bednar (0.0197)\n", " Neil L. Andersen (0.0169)\n", " D. Todd Christofferson (0.0008)\n", "\n", "2038" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " other (0.976)\n", " David A. Bednar (0.014)\n", " Neil L. Andersen (0.0097)\n", " D. Todd Christofferson (0.0003)\n", "\n", "2039" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " other (0.9871)\n", " David A. Bednar (0.0074)\n", " Neil L. Andersen (0.0054)\n", " D. Todd Christofferson (0.0001)\n", "\n" ] } ], "prompt_number": 16 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Next steps:\n", "\n", "- Model validation - test on past prophetic outcomes.\n", "- Adjust life tables instead of ages, or use specific life tables for each individual." ] } ], "metadata": {} } ] }