simlang_13_answered.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simulating Language 13, Iterated Learning (lab) (some answers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The simulation we'll build in this notebook explores the cultural evolution of a signalling system in a population of agents. In particular, we’ll look at the effects on communicative accuracy of:\n",
    "- different learning biases (weight update rules),\n",
    "- different ways of communicating,\n",
    "- different populations models\n",
    "\n",
    "There's a lot in this lab, so it's probably worth having a play with the simulation runs first before going back and understanding the code. However, make sure you understand what the new parameters do first!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we'll most of the code from the last lab. (We're going to replace the `learn` and `pop_produce` functions later on, so we're leaving them out)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import set_matplotlib_formats\n",
    "set_matplotlib_formats('svg', 'pdf')\n",
    "\n",
    "def wta(items):\n",
    "    maxweight = max(items)\n",
    "    candidates = []\n",
    "    for i in range(len(items)):\n",
    "        if items[i] == maxweight:\n",
    "            candidates.append(i)\n",
    "    return random.choice(candidates)\n",
    "\n",
    "def reception_weights(system, signal):\n",
    "    weights = []\n",
    "    for row in system:\n",
    "        weights.append(row[signal])\n",
    "    return weights\n",
    "\n",
    "def communicate(speaker_system, hearer_system, meaning):\n",
    "    speaker_signal = wta(speaker_system[meaning])\n",
    "    hearer_meaning = wta(reception_weights(hearer_system, speaker_signal))\n",
    "    if meaning == hearer_meaning: \n",
    "        return 1\n",
    "    else: \n",
    "        return 0\n",
    "    \n",
    "def train(system, ms_pair_list):\n",
    "    for pair in ms_pair_list:\n",
    "        learn(system, pair[0], pair[1])\n",
    "\n",
    "def pop_learn(population, data, no_learning_episodes):\n",
    "    for n in range(no_learning_episodes):\n",
    "        ms_pair = random.choice(data)\n",
    "        learn(random.choice(population), ms_pair[0], ms_pair[1])\n",
    "\n",
    "def ca_monte_pop(population, trials):\n",
    "    total = 0.\n",
    "    for n in range(trials):\n",
    "        speaker = random.choice(population)\n",
    "        hearer = random.choice(population)\n",
    "        total += communicate(speaker, hearer, random.randrange(len(speaker)))\n",
    "    return total / trials"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The new part of our code starts with a set of parameter declarations; most of their meanings should all be straightforward by now. Note that the interactions parameter has two purposes in this simulation:\n",
    "- to specify the number of utterances produced to create the data.\n",
    "- to specify the number of times the data is randomly sampled in training.\n",
    "\n",
    "We've added a parameter to control how the population is updated through iterated learning (see below), and one that specifies the type of the initial language. We've also added two slightly mysterious parameters. These both can be either `True` or `False`. If a variable takes one of these forms it can be used whenever you use an `if` in python. For example, this should print \"yes, it is true!\" out on the screen:\n",
    "\n",
    "```python\n",
    "x = True\n",
    "if x:\n",
    "    print('yes, it is true!')\n",
    "```\n",
    "\n",
    "`inhibition` controls whether learning involves \"lateral inhibition\", which we'll explain below. `communication` controls whether the sender tries to avoid signals that would be communicatively unsuccessful. We'll also see how this is done below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "meanings = 5                     # number of meanings\n",
    "signals = 5                      # number of signals\n",
    "interactions = 100               # both the number of utterances produced and the number\n",
    "                                 # of times this set is randomly sampled for training.\n",
    "size = 100                       # size of population\n",
    "method = 'replacement'           # method of population update\n",
    "initial_language_type = 'random' # either 'optimal' or 'random'\n",
    "rule = [1, 0, 0, 0]              # learning rule (alpha, beta, gamma, delta)\n",
    "inhibition = False               # apply lateral inhibition (can be True or False)\n",
    "communication = False            # speaker tries to avoid ambiguity (can be True or False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function `new_agent` below creates a new agent. The weights in this agent’s network depend on the parameter `initial_language_type`. If this parameter is set to `random` (or in fact to anything other than `optimal`), every cell in the new agent’s signalling matrix is set initially to zero. If the `initial_language_type` parameter is set to `optimal`, then the weights are configured such that the agent’s initial weights ensure it signals optimally (`m1` is conveyed using `s1`, `m2` is conveyed using `s2`, etc). The function `new_population` then uses the `new_agent` function to create a population of new agents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def new_agent(initial_language_type):\n",
    "    system = []\n",
    "    for row_n in range(meanings):\n",
    "        row = []\n",
    "        for column_n in range(signals):\n",
    "            if initial_language_type == 'optimal' and row_n == column_n:\n",
    "                row.append(1)\n",
    "            else:\n",
    "                row.append(0)\n",
    "        system.append(row)\n",
    "    return system\n",
    "\n",
    "def new_population(size, initial_language_type):\n",
    "    population = []\n",
    "    for i in range(size):\n",
    "        population.append(new_agent(initial_language_type))\n",
    "    return population"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lateral inhibition in learning\n",
    "\n",
    "This is a slightly more complicated learning function compared to what we had before. Now, instead of just counting the number of times an agent sees a meaning with a signal, the association weights between all the (wrong) signals that are associated with that meaning can be reduced, and all the (wrong) meanings that are associated with that signal can be reduced. This is called \"lateral inhibition\" in that all the associations sideways along the rows and columns are inhibited each time a pair is strengthened. If `inhibition` is `False` then `learn` works just as before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def learn(system, meaning, signal):\n",
    "    system[meaning][signal] += 1\n",
    "    if inhibition:\n",
    "        for m in range(len(system)):\n",
    "            for s in range(len(system[m])):\n",
    "                if (m != meaning and s == signal) or (m == meaning and s != signal):\n",
    "                    system[m][s] -= 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Communicatively rational speakers\n",
    "\n",
    "So far in this course, speakers haven't \"tried\" to be communicative. They have simply produced what their signalling matrix told them to. But a more rational speaker would not blindly produce a signal if it is going to be misunderstood. How could a speaker know that they are going to be misunderstood if they don't know what the hearer has in their signalling matrix? One way is to assume that the hearer is like them and imagine what *they* would understand if they heard a given signal. This what the following code does. If `communication` is `True` then the speaker checks whether the signal they are about to produce would allow them themselves to understand the right meaning. If it doesn't they just try a random other signal instead. If `communication` is `False`, then it works exactly as before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def produce(speaker, meaning):\n",
    "    signal = wta(speaker[meaning])\n",
    "    if communication:\n",
    "        speaker_meaning = wta(reception_weights(speaker, signal))\n",
    "        if (meaning != speaker_meaning):\n",
    "            signal = random.randrange(len(speaker[0]))\n",
    "    return signal\n",
    "\n",
    "def pop_produce(population, no_productions):\n",
    "    ms_pairs = []\n",
    "    for n in range(no_productions):\n",
    "        speaker = random.choice(population)     \n",
    "        meaning = random.randrange(len(speaker))\n",
    "        signal = produce(speaker, meaning)\n",
    "        ms_pairs.append([meaning, signal])\n",
    "    return ms_pairs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Try out the `produce` function with different speaker matrices and meanings to make sure it behaves as you would expect when you use a matrix that has homonymy in it when you change the `communication` parameter. (Hint: even when the speaker is being communicatively helpful, they may still use an unhelpful signal by chance, so you might have to run `produce` thousands of times and count the results to see the difference in practice. If you sit down with a piece of paper and a pen, you could even come up with a prediction of exactly how much better the communicatively rational speaker should be for a given signalling matrix!)*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.498\n",
      "0.241\n"
     ]
    }
   ],
   "source": [
    "speaker = [[1, 1], [0, 2]]\n",
    "\n",
    "communication = False\n",
    "bad_signal_count = 0\n",
    "for i in range(1000):\n",
    "    if produce(speaker, 0) == 1:\n",
    "        bad_signal_count += 1\n",
    "print(bad_signal_count / 1000)\n",
    "\n",
    "communication = True\n",
    "bad_signal_count = 0\n",
    "for i in range(1000):\n",
    "    if produce(speaker, 0) == 1:\n",
    "        bad_signal_count += 1\n",
    "print(bad_signal_count / 1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def simulation(generations, mc_trials, report_every):\n",
    "    population = new_population(size, initial_language_type)\n",
    "    data_accumulator = []\n",
    "    for i in range(generations + 1):\n",
    "        if (i % report_every == 0):\n",
    "            data_accumulator.append(ca_monte_pop(population, mc_trials))\n",
    "        data = pop_produce(population, interactions)\n",
    "        if method == 'chain':\n",
    "            population = new_population(size, 'random')\n",
    "            pop_learn(population, data, interactions)\n",
    "        if method == 'replacement':\n",
    "            population = population[1:]\n",
    "            learner = new_agent('random')\n",
    "            pop_learn([learner], data, interactions)\n",
    "            population.append(learner)\n",
    "        if method == 'closed':\n",
    "            pop_learn(population, data, interactions)\n",
    "    return [population, data_accumulator]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The simulation function runs the simulation. It takes three parameters, as follows:\n",
    "- `generations`: the number of generations in the simulation\n",
    "- `mc_trials`: the number of trials used to calculate communicative accuracy \n",
    "- `report_every`: the frequency with which data points (for printing in a graph) are returned\n",
    "\n",
    "It then runs through the following steps:\n",
    "1. Initialise the population\n",
    "2. For each generation:\n",
    "    1. Evaluate the population’s communicative accuracy\n",
    "    2. Produce some data\n",
    "    3. Update the population (by adding new agents)\n",
    "    4. Get (some of) the new population to learn from the data produced in 2A.\n",
    "3. Output the final state of the population, and the list of communicative accuracy scores\n",
    "\n",
    "The parameter `method` defines exactly how the population is updated, based on the scheme outlined by Mesoudi & Whiten:\n",
    "- `chain`: create a completely new population\n",
    "- `replacement`: remove one agent from the population, and replace with a new agent\n",
    "- `closed`: do not change the population at all"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are two things to note about the Python code in this function.\n",
    "\n",
    "### Review: slicing a list\n",
    "\n",
    "The slice `[start : end]` operator allows us to take a slice of sequential elements between start and end from a list.\n",
    "As usual in Python, the sequence extracts starts at start, and continues up to, but not including, end. Either the start or end (or both) indexes can be omitted, in which case the start or end of the list is assumed, respectively.\n",
    "\n",
    "For example, try the following:\n",
    "\n",
    "```python\n",
    "x = ['a', 'b', 'c', 'd']\n",
    "print(x[1:])\n",
    "print(x[:3])\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['b', 'c', 'd']\n",
      "['a', 'b', 'c']\n"
     ]
    }
   ],
   "source": [
    "x = ['a', 'b', 'c', 'd']\n",
    "print(x[1:])\n",
    "print(x[:3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modulus (remainder)\n",
    "\n",
    "The `x % y` operator returns the remainder of the division of x by y.\n",
    "\n",
    "For example, `7 % 3` gives us `1`, `11 % 4` gives us `3`, and `10 % 2` gives us `0`.\n",
    "\n",
    "In the `simulation` function, the modulus operator is used to decide whether or not to calculate the value of `ca_monte_pop` and output it for the graphs. Can you see how it works? (We do this because calculating the communicative accuracy actually takes quite a bit of time, so we don't want to slow it down too much!)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Questions\n",
    "\n",
    "Everyone should try questions 1-4. Question 5 is more open-ended, and needn’t involve coding (but could).\n",
    "\n",
    "1. Run the simulation for 500 generations, with 1000 mc_trials per generation, outputting communicative accuracy every 10 generations. Plot the values on a graph. (Hint: be careful with your x-axis. Are you sure you understand what it means!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'communicative accuracy')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "inhibition = False\n",
    "communication = False\n",
    "plt.plot(range(0, 501, 10),simulation(500, 1000, 10)[1])\n",
    "plt.xlabel('generations')\n",
    "plt.ylabel('communicative accuracy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Experiment with turning on and off lateral inhibition and communicatively rational speakers (by changing the `inhibition` and `communication` parameters to `True`). Which combinations construct perfect construct perfect communicative systems from random languages?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "inhibition = False\n",
    "communication = False\n",
    "results_noi_nor = []\n",
    "for i in range(50):\n",
    "    results_noi_nor.append(simulation(500, 1000, 10)[1])\n",
    "\n",
    "inhibition = True\n",
    "communication = False\n",
    "results_i_nor = []\n",
    "for i in range(50):\n",
    "    results_i_nor.append(simulation(500, 1000, 10)[1])\n",
    "\n",
    "inhibition = False\n",
    "communication = True\n",
    "results_noi_r = []\n",
    "for i in range(50):\n",
    "    results_noi_r.append(simulation(500, 1000, 10)[1])\n",
    "\n",
    "inhibition = True\n",
    "communication = True\n",
    "results_i_r = []\n",
    "for i in range(50):\n",
    "    results_i_r.append(simulation(500, 1000, 10)[1])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5,0,'communicative accuracy')]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
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       "<Figure size 432x288 with 4 Axes>"
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     "metadata": {},
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   ],
   "source": [
    "def final_results(results):\n",
    "    final = []\n",
    "    for r in results:\n",
    "        final.append(r[len(r)-1])\n",
    "    return final\n",
    "\n",
    "fig, axs = plt.subplots(nrows=4, ncols=1, sharex='all')\n",
    "\n",
    "ax = axs[0]\n",
    "ax.hist(final_results(results_noi_nor), range=(0,1), bins=10, label='no inhibition, no rationality')\n",
    "ax.legend()\n",
    "\n",
    "ax = axs[1]\n",
    "ax.hist(final_results(results_i_nor), range=(0,1), bins=10, label='inhibition, no rationality')\n",
    "ax.legend()\n",
    "\n",
    "ax = axs[2]\n",
    "ax.hist(final_results(results_noi_r), range=(0,1), bins=10, label='no inhibition, rationality')\n",
    "ax.legend()\n",
    "\n",
    "ax = axs[3]\n",
    "ax.hist(final_results(results_i_r), range=(0,1), bins=10, label='inhibition, rationality')\n",
    "ax.legend()\n",
    "\n",
    "ax.set(xlabel='communicative accuracy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Change the population update method to ‘chain’, and re-run the simulation. What happens? Why? Increase the number of interactions by a factor of 100, and reduce the number of generations by a factor of 10. What happens now?\n",
    "4. Experiment with the ‘closed’ method as well. What difference does the update method make to the way the simulation works?\n",
    "5. In previous worksheets you have had the opportunity to write and play with code which models genetic transmission, spatial organisation, and so on. How would you fit these things in to this iterated learning model? Why might that be an interesting thing to do?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example answer for assessment 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def produce(speaker, meaning):\n",
    "    signal = wta(speaker[meaning])\n",
    "    if communication:\n",
    "        speaker_meaning = wta(reception_weights(speaker, signal))\n",
    "        if meaning != speaker_meaning:\n",
    "            if super_rational:\n",
    "                good_signals = []\n",
    "                for s in range(len(speaker[0])):\n",
    "                    speaker_meaning = wta(reception_weights(speaker, s))\n",
    "                    if meaning == speaker_meaning:\n",
    "                        good_signals.append(s)\n",
    "                    if good_signals != []:\n",
    "                        signal = random.choice(good_signals)\n",
    "            else:\n",
    "                signal = random.randrange(len(speaker[0]))\n",
    "    return signal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "inhibition = False\n",
    "communication = True\n",
    "super_rational = False\n",
    "results1=[]\n",
    "for i in range(100):\n",
    "    result = simulation(500,1000,10)[1]\n",
    "    results1.append(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "super_rational = True\n",
    "results2=[]\n",
    "for i in range(100):\n",
    "    result = simulation(500,1000,10)[1]\n",
    "    results2.append(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def average_results(results):\n",
    "    average=[]\n",
    "    for i in range(len(results[0])):\n",
    "        total = 0\n",
    "        for r in results:\n",
    "            total += r[i]\n",
    "        total /= len(results)\n",
    "        average.append(total)\n",
    "    return average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
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   "source": [
    "for r in results1:\n",
    "    plt.plot(range(0, 501, 10),r, color='orange', alpha =0.1)\n",
    "\n",
    "plt.plot(range(0, 501, 10),average_results(results1), color='red', label='original')\n",
    "    \n",
    "for r in results2:\n",
    "    plt.plot(range(0, 501, 10),r, color='green', alpha =0.1)\n",
    "\n",
    "plt.plot(range(0, 501, 10),average_results(results2), color='blue', label='super rational')\n",
    "    \n",
    "plt.legend()\n",
    "plt.xlabel('generations')\n",
    "plt.ylabel('communicative accuracy')\n",
    "plt.grid()\n",
    "plt.savefig('super_rational.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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