{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evolution of cooperation\n",
    "\n",
    "Code examples from [Think Complexity, 2nd edition](https://thinkcomplex.com).\n",
    "\n",
    "Copyright 2016 Allen Downey, [MIT License](http://opensource.org/licenses/MIT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from os.path import basename, exists\n",
    "\n",
    "def download(url):\n",
    "    filename = basename(url)\n",
    "    if not exists(filename):\n",
    "        from urllib.request import urlretrieve\n",
    "        local, _ = urlretrieve(url, filename)\n",
    "        print('Downloaded ' + local)\n",
    "    \n",
    "download('https://github.com/AllenDowney/ThinkComplexity2/raw/master/notebooks/utils.py')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils import decorate, savefig\n",
    "# make a directory for figures\n",
    "!mkdir -p figs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Chapter 11 code\n",
    "\n",
    "From the Chapter 11 notebook, we will reuse `Simulation` and `Instrument`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Simulation:\n",
    "    \n",
    "    def __init__(self, fit_land, agents):\n",
    "        \"\"\"Create the simulation:\n",
    "        \n",
    "        fit_land: fit_land\n",
    "        num_agents: int number of agents\n",
    "        agent_maker: function that makes agents\n",
    "        \"\"\"\n",
    "        self.fit_land = fit_land\n",
    "        self.agents = np.asarray(agents)\n",
    "        self.instruments = []\n",
    "        \n",
    "    def add_instrument(self, instrument):\n",
    "        \"\"\"Adds an instrument to the list.\n",
    "        \n",
    "        instrument: Instrument object\n",
    "        \"\"\"\n",
    "        self.instruments.append(instrument)\n",
    "        \n",
    "    def plot(self, index, *args, **kwargs):\n",
    "        \"\"\"Plot the results from the indicated instrument.\n",
    "        \"\"\"\n",
    "        self.instruments[index].plot(*args, **kwargs)\n",
    "        \n",
    "    def run(self, num_steps=500):\n",
    "        \"\"\"Run the given number of steps.\n",
    "        \n",
    "        num_steps: integer\n",
    "        \"\"\"\n",
    "        # initialize any instruments before starting\n",
    "        self.update_instruments()\n",
    "        \n",
    "        for _ in range(num_steps):\n",
    "            self.step()\n",
    "        \n",
    "    def step(self):\n",
    "        \"\"\"Simulate a time step and update the instruments.\n",
    "        \"\"\"\n",
    "        n = len(self.agents)\n",
    "        fits = self.get_fitnesses()\n",
    "        \n",
    "        # see who dies\n",
    "        index_dead = self.choose_dead(fits)\n",
    "        num_dead = len(index_dead)\n",
    "        \n",
    "        # replace the dead with copies of the living\n",
    "        replacements = self.choose_replacements(num_dead, fits)\n",
    "        self.agents[index_dead] = replacements\n",
    "\n",
    "        # update any instruments\n",
    "        self.update_instruments()\n",
    "        \n",
    "    def update_instruments(self):\n",
    "        for instrument in self.instruments:\n",
    "            instrument.update(self)\n",
    "            \n",
    "    def get_locs(self):\n",
    "        \"\"\"Returns a list of agent locations.\"\"\"\n",
    "        return [tuple(agent.loc) for agent in self.agents]\n",
    "    \n",
    "    def get_fitnesses(self):\n",
    "        \"\"\"Returns an array of agent fitnesses.\"\"\"\n",
    "        fits = [agent.fitness for agent in self.agents]\n",
    "        return np.array(fits)\n",
    "    \n",
    "    def choose_dead(self, ps):\n",
    "        \"\"\"Choose which agents die in the next timestep.\n",
    "        \n",
    "        ps: probability of survival for each agent\n",
    "        \n",
    "        returns: indices of the chosen ones\n",
    "        \"\"\"\n",
    "        n = len(self.agents)\n",
    "        is_dead = np.random.random(n) < 0.1\n",
    "        index_dead = np.nonzero(is_dead)[0]\n",
    "        return index_dead\n",
    "        \n",
    "    def choose_replacements(self, n, weights):\n",
    "        \"\"\"Choose which agents reproduce in the next timestep.\n",
    "        \n",
    "        n: number of choices\n",
    "        weights: array of weights\n",
    "        \n",
    "        returns: sequence of Agent objects\n",
    "        \"\"\"\n",
    "        agents = np.random.choice(self.agents, size=n, replace=True)\n",
    "        replacements = [agent.copy() for agent in agents]\n",
    "        return replacements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Instrument:\n",
    "    \"\"\"Computes a metric at each timestep.\"\"\"\n",
    "    \n",
    "    def __init__(self):\n",
    "        self.metrics = []\n",
    "        \n",
    "    def update(self, sim):\n",
    "        \"\"\"Compute the current metric.\n",
    "        \n",
    "        Appends to self.metrics.\n",
    "        \n",
    "        sim: Simulation object\n",
    "        \"\"\"\n",
    "        # child classes should implement this method\n",
    "        pass\n",
    "        \n",
    "    def plot(self, **options):\n",
    "        plt.plot(self.metrics, **options)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MeanFitness(Instrument):\n",
    "    \"\"\"Computes mean fitness at each timestep.\"\"\"\n",
    "    label = 'Mean fitness'\n",
    "    \n",
    "    def update(self, sim):\n",
    "        mean = np.nanmean(sim.get_fitnesses())\n",
    "        self.metrics.append(mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PD Agent\n",
    "\n",
    "The `Agent` class defines a strategy for iterated prisoner's dilemma.\n",
    "\n",
    "The genome of a Prisoner's Dilemma-playing agent is a map from the previous choices of the opponent to the agent's next choice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Agent:\n",
    "    \n",
    "    keys = [(None, None),\n",
    "            (None, 'C'),\n",
    "            (None, 'D'),\n",
    "            ('C', 'C'),\n",
    "            ('C', 'D'),\n",
    "            ('D', 'C'),\n",
    "            ('D', 'D')]\n",
    "    \n",
    "    def __init__(self, values, fitness=np.nan):\n",
    "        \"\"\"Initialize the agent.\n",
    "        \n",
    "        values: sequence of 'C' and 'D'\n",
    "        \"\"\"\n",
    "        self.values = values\n",
    "        self.responses = dict(zip(self.keys, values))\n",
    "        self.fitness = fitness\n",
    "        \n",
    "    def reset(self):\n",
    "        \"\"\"Reset variables before a sequence of games.\n",
    "        \"\"\"\n",
    "        self.hist = [None, None]\n",
    "        self.score = 0\n",
    "        \n",
    "    def past_responses(self, num=2):\n",
    "        \"\"\"Select the given number of most recent responses.\n",
    "        \n",
    "        num: integer number of responses\n",
    "        \n",
    "        returns: sequence of 'C' and 'D'\n",
    "        \"\"\"\n",
    "        return tuple(self.hist[-num:])\n",
    "    \n",
    "    def respond(self, other):\n",
    "        \"\"\"Choose a response based on the opponent's recent responses.\n",
    "        \n",
    "        other: Agent\n",
    "        \n",
    "        returns: 'C' or 'D'\n",
    "        \"\"\"\n",
    "        key = other.past_responses()\n",
    "        resp = self.responses[key]\n",
    "        return resp\n",
    "        \n",
    "    def append(self, resp, pay):\n",
    "        \"\"\"Update based on the last response and payoff.\n",
    "        \n",
    "        resp: 'C' or 'D'\n",
    "        pay: number\n",
    "        \"\"\"\n",
    "        self.hist.append(resp)\n",
    "        self.score += pay\n",
    "        \n",
    "    def copy(self, prob_mutate=0.05):\n",
    "        \"\"\"Make a copy of this agent.\n",
    "        \"\"\"\n",
    "        if np.random.random() > prob_mutate:\n",
    "            values = self.values\n",
    "        else:\n",
    "            values = self.mutate()\n",
    "        return Agent(values, self.fitness)\n",
    "    \n",
    "    def mutate(self):\n",
    "        \"\"\"Makes a copy of this agent's values, with one mutation.\n",
    "        \n",
    "        returns: sequence of 'C' and 'D'\n",
    "        \"\"\"\n",
    "        values = list(self.values)\n",
    "        index = np.random.choice(len(values))\n",
    "        values[index] = 'C' if values[index] == 'D' else 'D'\n",
    "        return values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's the genome for \"always cooperate\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{(None, None): 'C',\n",
       " (None, 'C'): 'C',\n",
       " (None, 'D'): 'C',\n",
       " ('C', 'C'): 'C',\n",
       " ('C', 'D'): 'C',\n",
       " ('D', 'C'): 'C',\n",
       " ('D', 'D'): 'C'}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_c = Agent('CCCCCCC')\n",
    "all_c.responses"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And for \"always defect\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{(None, None): 'D',\n",
       " (None, 'C'): 'D',\n",
       " (None, 'D'): 'D',\n",
       " ('C', 'C'): 'D',\n",
       " ('C', 'D'): 'D',\n",
       " ('D', 'C'): 'D',\n",
       " ('D', 'D'): 'D'}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_d = Agent('DDDDDDD')\n",
    "all_d.responses"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And for \"tit for tat\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{(None, None): 'C',\n",
       " (None, 'C'): 'C',\n",
       " (None, 'D'): 'D',\n",
       " ('C', 'C'): 'C',\n",
       " ('C', 'D'): 'D',\n",
       " ('D', 'C'): 'C',\n",
       " ('D', 'D'): 'D'}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tft = Agent('CCDCDCD')\n",
    "tft.responses"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `copy` method has some probability of generating a mutation (in this example, `values` is initially a string; after mutation, it's a NumPy array of letters)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DDDDDDD\n",
      "DDDDDDD\n",
      "DDDDDDD\n",
      "DDDDDDD\n",
      "DDDDDDD\n",
      "DDDDDDD\n",
      "DDDDDDD\n",
      "DDDDDDD\n",
      "['D', 'C', 'D', 'D', 'D', 'D', 'D']\n",
      "['D', 'D', 'C', 'D', 'D', 'D', 'D']\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(17)\n",
    "for i in range(10):\n",
    "    print(all_d.copy().values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following cell makes 1000 copies and counts how many of them are mutants."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "57"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum([all_d.copy().values != all_d.values for i in range(1000)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Tournament\n",
    "\n",
    "`Tournament` encapsulates the rules for the tournament."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Tournament:\n",
    "    \n",
    "    payoffs = {('C', 'C'): (3, 3),\n",
    "               ('C', 'D'): (0, 5),\n",
    "               ('D', 'C'): (5, 0),\n",
    "               ('D', 'D'): (1, 1)}\n",
    "    \n",
    "    num_rounds = 6\n",
    "\n",
    "    def play(self, agent1, agent2):\n",
    "        \"\"\"Play a sequence of iterated PD rounds.\n",
    "        \n",
    "        agent1: Agent\n",
    "        agent2: Agent\n",
    "\n",
    "        returns: tuple of agent1's score, agent2's score \n",
    "        \"\"\"\n",
    "        agent1.reset()\n",
    "        agent2.reset()\n",
    "        \n",
    "        for i in range(self.num_rounds):\n",
    "            resp1 = agent1.respond(agent2)\n",
    "            resp2 = agent2.respond(agent1)\n",
    "\n",
    "            pay1, pay2 = self.payoffs[resp1, resp2]\n",
    "            \n",
    "            agent1.append(resp1, pay1)\n",
    "            agent2.append(resp2, pay2)\n",
    "            \n",
    "        return agent1.score, agent2.score\n",
    "                \n",
    "    def melee(self, agents, randomize=True):\n",
    "        \"\"\"Play each agent against two others.\n",
    "        \n",
    "        Assigns the average score from the two games to agent.fitness\n",
    "        \n",
    "        agents: sequence of Agents\n",
    "        randomize: boolean, whether to shuffle the agents\n",
    "        \"\"\"\n",
    "        if randomize:\n",
    "            agents = np.random.permutation(agents)\n",
    "            \n",
    "        n = len(agents)\n",
    "        i_row = np.arange(n)\n",
    "        j_row = (i_row + 1) % n\n",
    "        \n",
    "        totals = np.zeros(n)\n",
    "        \n",
    "        for i, j in zip(i_row, j_row):\n",
    "            agent1, agent2 = agents[i], agents[j]\n",
    "            score1, score2 = self.play(agent1, agent2)\n",
    "            totals[i] += score1\n",
    "            totals[j] += score2\n",
    "            \n",
    "        for i in i_row:\n",
    "            agents[i].fitness = totals[i] / self.num_rounds / 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can test `Tournament` with a few known scenarios."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30, 0)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tour = Tournament()\n",
    "tour.play(all_d, all_c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 5)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tour.play(all_d, tft)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18, 18)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tour.play(tft, all_c)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And then test `melee` with a list of three agents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<__main__.Agent at 0x7f3f46fb95d0>,\n",
       " <__main__.Agent at 0x7f3f46f30fd0>,\n",
       " <__main__.Agent at 0x7f3f46f30ad0>]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agents = [all_c, all_d, tft]\n",
    "agents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "tour.melee(agents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this population, \"always defect\" does best."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CCCCCCC 1.5\n",
      "DDDDDDD 3.3333333333333335\n",
      "CCDCDCD 1.9166666666666667\n"
     ]
    }
   ],
   "source": [
    "for agent in agents:\n",
    "    print(agent.values, agent.fitness)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Probability of survival\n",
    "\n",
    "We need a function to map from points per round (0 to 5) to probability of survival (0 to 1).  I'll use a logistic curve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def logistic(x, A=0, B=1, C=1, M=0, K=1, Q=1, nu=1):\n",
    "    \"\"\"Computes the generalize logistic function.\n",
    "    \n",
    "    A: controls the lower bound\n",
    "    B: controls the steepness of the transition \n",
    "    C: not all that useful, AFAIK\n",
    "    M: controls the location of the transition\n",
    "    K: controls the upper bound\n",
    "    Q: shift the transition left or right\n",
    "    nu: affects the symmetry of the transition\n",
    "    \n",
    "    returns: float or array\n",
    "    \"\"\"\n",
    "    exponent = -B * (x - M)\n",
    "    denom = C + Q * np.exp(exponent)\n",
    "    return A + (K-A) / denom ** (1/nu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prob_survive(scores):\n",
    "    \"\"\"Probability of survival, based on fitness.\n",
    "    \n",
    "    scores: sequence of scores, 0-60\n",
    "    \n",
    "    returns: probability\n",
    "    \"\"\"\n",
    "    return logistic(scores, A=0.7, B=1.5, M=2.5, K=0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "scores = np.linspace(0, 5)\n",
    "probs = prob_survive(scores)\n",
    "plt.plot(scores, probs)\n",
    "decorate(xlabel='Score', ylabel='Probability of survival')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The simulator\n",
    "\n",
    "The biggest change in the simulator is in `step`, which runs `melee` to determine the fitness of each agent, and `prob_survive` to map from fitness to probability of surviving."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "class PDSimulation(Simulation):\n",
    "    \n",
    "    def __init__(self, tournament, agents):\n",
    "        \"\"\"Create the simulation:\n",
    "        \n",
    "        tournament: Tournament object\n",
    "        agents: sequence of agents\n",
    "        \"\"\"\n",
    "        self.tournament = tournament\n",
    "        self.agents = np.asarray(agents)\n",
    "        self.instruments = []\n",
    "        \n",
    "    def step(self):\n",
    "        \"\"\"Simulate a time step and update the instruments.\n",
    "        \"\"\"\n",
    "        self.tournament.melee(self.agents)\n",
    "        Simulation.step(self)\n",
    "        \n",
    "    def choose_dead(self, fits):\n",
    "        \"\"\"Choose which agents die in the next timestep.\n",
    "        \n",
    "        fits: fitness of each agent\n",
    "        \n",
    "        returns: indices of the chosen ones\n",
    "        \"\"\"\n",
    "        ps = prob_survive(fits)\n",
    "        n = len(self.agents)\n",
    "        is_dead = np.random.random(n) < ps\n",
    "        index_dead = np.nonzero(is_dead)[0]\n",
    "        return index_dead"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We might want to start with random agents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_random_agents(n):\n",
    "    \"\"\"Make agents with random genotype.\n",
    "    \n",
    "    n: number of agents\n",
    "    \n",
    "    returns: sequence of agents\n",
    "    \"\"\"\n",
    "    agents = [Agent(np.random.choice(['C', 'D'], size=7)) \n",
    "              for _ in range(n)]\n",
    "    return agents"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or with all identical agents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_identical_agents(n, values):\n",
    "    \"\"\"Make agents with the given genotype.\n",
    "    \n",
    "    n: number of agents\n",
    "    values: sequence of 'C' and 'D'\n",
    "    \n",
    "    returns: sequence of agents\n",
    "    \"\"\"\n",
    "    agents = [Agent(values) for _ in range(n)]\n",
    "    return agents"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are the instruments to compute various metrics.\n",
    "\n",
    "`Niceness` is the average number of `C` across the genotypes in the population."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Niceness(Instrument):\n",
    "    \"\"\"Fraction of cooperation in all genotypes.\"\"\"\n",
    "    label = 'Niceness'\n",
    "        \n",
    "    def update(self, sim):\n",
    "        responses = np.array([agent.values for agent in sim.agents])\n",
    "        metric = np.mean(responses == 'C')\n",
    "        self.metrics.append(metric)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Opening` is the fraction of agents that cooperate in the first round."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Opening(Instrument):\n",
    "    \"\"\"Fraction of agents that cooperate on the first round.\"\"\"\n",
    "    label = 'Opening'\n",
    "        \n",
    "    def update(self, sim):\n",
    "        responses = np.array([agent.values[0] for agent in sim.agents])\n",
    "        metric = np.mean(responses == 'C')\n",
    "        self.metrics.append(metric)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Retaliating` is the difference between (1) the fraction of agents that defect after the opponent defects and (2) the fraction of agents that defect after the opponent cooperates. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Retaliating(Instrument):\n",
    "    \"\"\"Tendency to defect after opponent defects.\"\"\"\n",
    "    label = 'Retaliating'\n",
    "        \n",
    "    def update(self, sim):\n",
    "        after_d = np.array([agent.values[2::2] for agent in sim.agents])\n",
    "        after_c = np.array([agent.values[1::2] for agent in sim.agents])\n",
    "        metric = np.mean(after_d == 'D') - np.mean(after_c == 'D')\n",
    "        self.metrics.append(metric)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Forgiving is the difference between the number of agents that cooperate after DC minus the number that cooperate after CD."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Forgiving(Instrument):\n",
    "    \"\"\"Tendency to cooperate if opponent cooperates after defecting.\"\"\"\n",
    "    label = 'Forgiving'\n",
    "        \n",
    "    def update(self, sim):\n",
    "        after_dc = np.array([agent.values[5] for agent in sim.agents])\n",
    "        after_cd = np.array([agent.values[4] for agent in sim.agents])\n",
    "        metric = np.mean(after_dc == 'C') - np.mean(after_cd == 'C')\n",
    "        self.metrics.append(metric)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's another metric intended to measure forgiveness."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Forgiving2(Instrument):\n",
    "    \"\"\"Ability to cooperate after the first two rounds.\"\"\"\n",
    "    label = 'Forgiving2'\n",
    "        \n",
    "    def update(self, sim):\n",
    "        after_two = np.array([agent.values[3:] for agent in sim.agents])\n",
    "        metric = np.mean(np.any(after_two=='C', axis=1))\n",
    "        self.metrics.append(metric)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results\n",
    "\n",
    "Here's a simulation that starts with 100 defectors and runs 5000 timesteps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "tour = Tournament()\n",
    "\n",
    "agents = make_identical_agents(100, list('DDDDDDD'))\n",
    "sim = PDSimulation(tour, agents)\n",
    "\n",
    "sim.add_instrument(MeanFitness())\n",
    "sim.add_instrument(Niceness())\n",
    "sim.add_instrument(Opening())\n",
    "sim.add_instrument(Retaliating())\n",
    "sim.add_instrument(Forgiving())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the simulation.  If you get a warning about `Mean of empty slice`, that's ok."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/downey/anaconda3/envs/ThinkComplexity2/lib/python3.7/site-packages/ipykernel_launcher.py:6: RuntimeWarning: Mean of empty slice\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "np.random.seed(17)\n",
    "sim.run(5000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And let's look at some results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_result(index, **options):\n",
    "    \"\"\"Plots the results of the indicated instrument.\n",
    "    \n",
    "    index: integer\n",
    "    \"\"\"\n",
    "    sim.plot(index, **options)\n",
    "    instrument = sim.instruments[index]\n",
    "    print(np.mean(instrument.metrics[1000:]))\n",
    "    decorate(xlabel='Time steps', \n",
    "                     ylabel=instrument.label)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initially, mean fitness is 1, because that's the number of points each defector gets per round when facing another defector.\n",
    "\n",
    "After a few hundred steps, mean fitness climbs to a steady state near 2.5, although it oscillates around this point substantially.\n",
    "\n",
    "In a world of all cooperators, mean fitness would be 3, so this steady state is hardly utopia, but it's not that far off."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.5006129717570604\n",
      "Saving figure to file figs/chap12-1\n"
     ]
    },
    {
     "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": [
    "plot_result(0, color='C0')\n",
    "savefig('figs/chap12-1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The average number of C's, across all agents and all locations in the genome, is generally more than half, with a long range mean above 0.6."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6453083157781982\n"
     ]
    },
    {
     "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": [
    "plot_result(1, color='C1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results are similar for the opening move: the fraction of agents who start out cooperating is generally more than half, with a long-range mean above 0.6.  This fraction varies widely in time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.67127968007998\n"
     ]
    },
    {
     "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": [
    "plot_result(2, color='C2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6453083157781982\n",
      "0.67127968007998\n",
      "Saving figure to file figs/chap12-2\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,4))\n",
    "plt.subplot(1,2,1)\n",
    "\n",
    "plot_result(1, color='C1')\n",
    "decorate(ylim=[0, 1.05])\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plot_result(2, color='C2')\n",
    "decorate(ylim=[0, 1.05])\n",
    "\n",
    "savefig('figs/chap12-2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There might be a weak inclination toward retaliation, with slightly more defections after the opponent defects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.07278680329917521\n"
     ]
    },
    {
     "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": [
    "plot_result(3, color='C3')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All of the strategies are forgiving in the sense that they have a short memory, so most of them are capable of cooperating at some point after an opponent has defected.  But there is no evidence that they are specifically more likely to forgive a defection from two rounds ago, compared to a defection in the previous round."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.009162709322669325\n"
     ]
    },
    {
     "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": [
    "plot_result(4, color='C4')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following cells explore the composition of the final population.  But because the distribution of agents varies so much over time, the details of a single timestep might not mean much.\n",
    "\n",
    "Here are the final genomes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['D', 'C', 'D', 'C', 'C', 'C', 'C']\n",
      "['D', 'C', 'D', 'C', 'D', 'C', 'D']\n",
      "['C', 'C', 'D', 'C', 'C', 'C', 'D']\n",
      "['D', 'C', 'D', 'C', 'D', 'C', 'D']\n",
      "['D', 'C', 'D', 'C', 'D', 'C', 'D']\n",
      "['D', 'D', 'D', 'C', 'D', 'C', 'D']\n",
      "['D', 'C', 'D', 'C', 'C', 'C', 'C']\n",
      "['D', 'C', 'D', 'C', 'C', 'C', 'D']\n",
      "['D', 'D', 'D', 'C', 'D', 'C', 'D']\n",
      "['D', 'C', 'D', 'C', 'D', 'C', 'C']\n",
      "['D', 'C', 'D', 'D', 'C', 'C', 'C']\n",
      "['C', 'C', 'D', 'C', 'C', 'C', 'D']\n",
      "['D', 'C', 'D', 'C', 'C', 'C', 'D']\n",
      "['C', 'C', 'D', 'C', 'C', 'C', 'D']\n",
      "['D', 'C', 'D', 'C', 'D', 'C', 'C']\n",
      "['D', 'C', 'D', 'C', 'C', 'C', 'D']\n",
      "['C', 'C', 'D', 'C', 'C', 'C', 'D']\n",
      "['C', 'C', 'D', 'C', 'C', 'C', 'D']\n",
      "['D', 'C', 'D', 'C', 'D', 'C', 'C']\n",
      "['D', 'C', 'D', 'C', 'D', 'C', 'C']\n",
      "['D', 'C', 'C', 'C', 'D', 'C', 'D']\n",
      "['D', 'C', 'C', 'C', 'D', 'C', 'D']\n",
      "['D', 'C', 'D', 'C', 'D', 'C', 'D']\n",
      "['D', 'C', 'D', 'C', 'C', 'C', 'D']\n",
      "['D', 'C', 'D', 'C', 'C', 'C', 'D']\n",
      "['D', 'C', 'D', 'C', 'D', 'C', 'D']\n",
      "['C', 'C', 'D', 'C', 'D', 'C', 'C']\n",
      "['D', 'C', 'D', 'C', 'D', 'C', 'D']\n",
      "['D', 'C', 'C', 'C', 'D', 'C', 'D']\n",
      "['D', 'C', 'D', 'C', 'D', 'C', 'D']\n",
      "['D', 'C', 'D', 'C', 'D', 'C', 'D']\n",
      "['D', 'C', 'C', 'C', 'D', 'C', 'D']\n",
      "['C', 'C', 'D', 'C', 'C', 'C', 'D']\n",
      "['D', 'C', 'C', 'C', 'D', 'C', 'D']\n",
      "['C', 'C', 'D', 'C', 'D', 'C', 'C']\n",
      "['D', 'C', 'D', 'C', 'C', 'C', 'D']\n",
      "['D', 'C', 'D', 'C', 'C', 'C', 'D']\n",
      "['D', 'D', 'D', 'C', 'D', 'C', 'C']\n",
      "['D', 'D', 'D', 'C', 'D', 'C', 'D']\n",
      "['C', 'C', 'D', 'C', 'D', 'C', 'C']\n",
      "['C', 'C', 'D', 'D', 'C', 'C', 'D']\n",
      "['D', 'C', 'D', 'C', 'C', 'C', 'C']\n",
      "['D', 'C', 'D', 'C', 'C', 'C', 'D']\n",
      "['D', 'C', 'D', 'C', 'C', 'C', 'D']\n",
      "['D', 'C', 'D', 'C', 'D', 'C', 'C']\n",
      "['D', 'C', 'D', 'C', 'D', 'C', 'D']\n",
      "['D', 'C', 'D', 'C', 'C', 'C', 'D']\n",
      "['D', 'C', 'D', 'C', 'D', 'C', 'D']\n",
      "['D', 'C', 'D', 'C', 'C', 'C', 'C']\n",
      "['D', 'C', 'D', 'C', 'C', 'C', 'C']\n",
      "['C', 'C', 'D', 'D', 'C', 'C', 'D']\n",
      "['D', 'C', 'D', 'C', 'D', 'C', 'D']\n",
      "['D', 'C', 'C', 'C', 'D', 'C', 'D']\n",
      "['D', 'C', 'D', 'C', 'C', 'C', 'D']\n",
      "['D', 'C', 'C', 'C', 'D', 'C', 'C']\n",
      "['D', 'D', 'D', 'C', 'D', 'C', 'D']\n",
      "['D', 'D', 'C', 'C', 'C', 'D', 'C']\n",
      "['C', 'C', 'D', 'C', 'C', 'C', 'D']\n",
      "['D', 'C', 'D', 'C', 'D', 'C', 'D']\n",
      "['D', 'C', 'D', 'C', 'D', 'C', 'D']\n",
      "['D', 'C', 'D', 'C', 'D', 'C', 'D']\n",
      "['D', 'C', 'D', 'C', 'D', 'C', 'D']\n",
      "['C', 'C', 'D', 'C', 'C', 'C', 'D']\n",
      "['D', 'C', 'D', 'C', 'C', 'C', 'C']\n",
      "['D', 'C', 'D', 'C', 'C', 'C', 'D']\n",
      "['D', 'C', 'D', 'C', 'D', 'C', 'C']\n",
      "['D', 'C', 'D', 'D', 'C', 'C', 'C']\n",
      "['D', 'D', 'C', 'C', 'C', 'C', 'D']\n",
      "['D', 'C', 'D', 'C', 'C', 'C', 'C']\n",
      "['D', 'C', 'D', 'C', 'C', 'C', 'D']\n",
      "['D', 'C', 'D', 'C', 'C', 'C', 'D']\n",
      "['D', 'C', 'D', 'C', 'D', 'C', 'D']\n",
      "['C', 'C', 'D', 'C', 'C', 'C', 'D']\n",
      "['D', 'C', 'D', 'C', 'C', 'C', 'C']\n",
      "['D', 'D', 'D', 'C', 'D', 'C', 'D']\n",
      "['D', 'C', 'D', 'C', 'C', 'C', 'D']\n",
      "['C', 'C', 'D', 'C', 'D', 'C', 'C']\n",
      "['D', 'C', 'C', 'C', 'D', 'C', 'D']\n",
      "['D', 'C', 'D', 'C', 'C', 'C', 'C']\n",
      "['D', 'D', 'C', 'C', 'C', 'D', 'C']\n",
      "['D', 'C', 'D', 'C', 'D', 'C', 'D']\n",
      "['D', 'C', 'D', 'C', 'D', 'C', 'D']\n",
      "['C', 'C', 'D', 'C', 'C', 'C', 'D']\n",
      "['D', 'C', 'D', 'D', 'C', 'C', 'C']\n",
      "['D', 'C', 'D', 'C', 'C', 'C', 'D']\n",
      "['C', 'C', 'D', 'C', 'D', 'C', 'C']\n",
      "['C', 'C', 'D', 'C', 'D', 'C', 'C']\n",
      "['D', 'C', 'D', 'C', 'C', 'C', 'D']\n",
      "['D', 'C', 'D', 'C', 'D', 'C', 'C']\n",
      "['D', 'C', 'C', 'C', 'D', 'C', 'D']\n",
      "['D', 'C', 'C', 'C', 'D', 'C', 'D']\n",
      "['D', 'C', 'D', 'C', 'D', 'C', 'D']\n",
      "['C', 'C', 'C', 'C', 'D', 'C', 'D']\n",
      "['D', 'C', 'D', 'C', 'D', 'C', 'D']\n",
      "['C', 'C', 'D', 'C', 'D', 'C', 'C']\n",
      "['D', 'C', 'D', 'C', 'C', 'C', 'D']\n",
      "['D', 'C', 'D', 'C', 'C', 'C', 'D']\n",
      "['D', 'D', 'D', 'C', 'D', 'C', 'D']\n",
      "['D', 'C', 'D', 'C', 'C', 'C', 'C']\n",
      "['D', 'C', 'D', 'C', 'C', 'C', 'D']\n"
     ]
    }
   ],
   "source": [
    "for agent in sim.agents:\n",
    "    print(agent.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And here are the most common genomes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DCDCDCD    20\n",
       "DCDCCCD    20\n",
       "DCDCCCC    10\n",
       "CCDCCCD    10\n",
       "DCCCDCD     9\n",
       "DCDCDCC     7\n",
       "CCDCDCC     7\n",
       "DDDCDCD     6\n",
       "DCDDCCC     3\n",
       "CCDDCCD     2\n",
       "DDCCCDC     2\n",
       "DDDCDCC     1\n",
       "DCCCDCC     1\n",
       "DDCCCCD     1\n",
       "CCCCDCD     1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pandas import Series\n",
    "\n",
    "responses = [''.join(agent.values) for agent in sim.agents]\n",
    "Series(responses).value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise:** The simulation in this notebook depends on a number of conditions and parameters I chose arbitrarily.  As an exercise, I encourage you to explore other conditions to see what effect they have on the results.  Here are some suggestions:\n",
    "\n",
    "1.  Vary the initial conditions: instead of starting with all defectors, see what happens if you start with all cooperators, all TFT, or random agents.\n",
    "\n",
    "2.  In `Tournament.melee`, I shuffle the agents at the beginning of each time step, so each agent plays against two randomly-chosen agents.  What happens if you don't shuffle?  In that case, each agent would play against the same neighbors repeatedly.  That might make it easier for a minority strategy to invade a majority, by taking advantage of locality.\n",
    "\n",
    "3.  Since each agent only plays against two other agents, the outcome of each round is highly variable: an agent that would do well against most other agents might get unlucky during any given round, or the other way around.  What happens if you increase the number of opponents each agent plays against during each round?  Or what if an agent's fitness at the end of each step is the average of its current score and its fitness at the end of the previous round?\n",
    "\n",
    "4.  The function I chose for `prob_survival` varies from 0.7 to 0.9, so the least fit agent, with `p=0.7`, lives for `3.33` timesteps, on average, and the most fit agent lives for `10` timesteps.  What happens if you make `prob_survival` more or less \"aggressive\".\n",
    "\n",
    "5.  I chose `num_rounds=6` so that each element of the genome has roughly the same impact on the outcome of a match.  But that is substantially shorter than what Alexrod used in his tournaments.  What happens if you increase `num_rounds`?  Note: if you explore the effect of this parameter, you might also want to create an instrument to measure the niceness of the last 4 elements of the genome, which will be under more selective pressure as `num_rounds` increases.\n",
    "\n",
    "6.  My implementation has differential survival but just random reproduction.  What happens if you add differential reproduction?\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise:**  In these simulations, the population never converges to a state where a majority share the same, presumably optimal, genotype.  There are two possible explanations for this outcome: one is that there is no optimal strategy, because whenever the population is dominated by a majority genotype, that condition creates an opportunity for a minority to invade; the other possibility is that the mutation rate is high enough to maintain a diversity of genotypes even if the majority is non-optimal.  To distinguish between these explanations, try lowering the mutation rate to see what happens.  Alternatively, start with a random population and run without mutation until only one genotype survives.  Or run with mutation until the system reaches something like a steady state; then turn off mutation and run until there is only one surviving genotype.  What are the characteristics of the genotypes that prevail in these conditions? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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