2018-11-10_Axelrod/2018-11-10_Axelrod.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dinâmica social da tolerância, motivação e confiança"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Ciência política em Python!*\n",
    "\n",
    "Quais condutas são robustas/persistentes na sociedade? E quais tendem a desaparecer?\n",
    "O que é altruísmo? Isso existe?\n",
    "Quais comportamentos são benéficos para todos a médio/longo prazo?\n",
    "Como podemos criar tolerância, motivação e confiança naqueles que estão de alguma forma próximos a nós? Isso é possível, mesmo que os envolvidos sejam rivais?\n",
    "Do que a intolerência, a desmotivação e a desconfiança se alimentam?\n",
    "\n",
    "O objetivo da palestra é tentar responder a essas perguntas com um pouco de filosofia/lógica, e um pouco de teoria dos jogos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.options.display.max_columns = 8 # O padrão é 20\n",
    "pd.options.display.max_rows = 20   # O padrão é 60"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Nota*: A exibição das equações com MathJax utiliza o `cancel`.\n",
    "\n",
    "$\\require{cancel}$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Por que palestrar sobre isso?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tudo começou com uma discussão\n",
    "sobre o paradoxo da tolerância de Karl Popper (1945):\n",
    "\n",
    "> Menos conhecido é o paradoxo da tolerância:\n",
    "> tolerância ilimitada leva ao desaparecimento da tolerância.\n",
    "> Se estendermos tolerância ilimitada\n",
    "> até mesmo para aqueles que são intolerantes,\n",
    "> se não estivermos preparados para defender a sociedade tolerante\n",
    "> contra a investida dos intolerantes,\n",
    "> então os tolerantes serão destruídos,\n",
    "> e a tolerância junto destes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](paradoxo_tolerancia.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O que esse paradoxo diz é que todos devemos ser intolerantes\n",
    "àquilo que consideramos intolerância.\n",
    "Há casos óbvios:\n",
    "tolerância a *serial killers* e autoritários frios como Stálin\n",
    "levará a abusos destrutivos.\n",
    "\n",
    "Porém, o próprio julgamento do que é \"intolerância\"\n",
    "pode vir a ser um sinal de intolerância,\n",
    "tornando o argumento de Popper apenas um reforço\n",
    "para que intolerantes se sintam justificados\n",
    "em aplicar sua intolerância. **Cuidado**!!!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O mesmo argumento dado ao par *tolerância / intolerância*\n",
    "pode ser aplicado a outros pares, tais como:\n",
    "\n",
    "- *Motivação* / *desmotivação*\n",
    "- *Confiança/credibilidade* / *desconfiança*\n",
    "\n",
    "Uma atitude desmotivada/desmotivadora/destrutiva de uma pessoa\n",
    "pode minar com a motivação de outras pessoas.\n",
    "Permitir e motivar essa desmotivação é uma atitude auto-destrutiva\n",
    "para quem pretende motivar outras pessoas."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mas por que \"desaparecimento\" da tolerância?\n",
    "O que aconteceriam com aqueles que toleram tudo?\n",
    "Analogamente,\n",
    "o que aconteceriam com altruístas incondicionais em uma sociedade?\n",
    "\n",
    "Essa é a primeira motivação para estudarmos\n",
    "um pouco do que a teoria dos jogos\n",
    "(vista aqui como o estudo da tomada de decisão\n",
    " na presença de adversários),\n",
    "tem a nos dizer sobre robustez, estabilidade e outras características\n",
    "de comportamentos sociais\n",
    "no estudo da propagação da confiança."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Outra motivação é que, há muito tempo,\n",
    "o Brasil está em uma situação pouco amigável\n",
    "para o desenvolvimento de uma cultura de confiança\n",
    "<https://ourworldindata.org/trust>:\n",
    "\n",
    "![](ourworldindata_screenshot.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dilema do prisioneiro"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copiando do [Wikipedia](https://pt.wikipedia.org/wiki/Dilema_do_prisioneiro):\n",
    "\n",
    "> Dois suspeitos, A e B, são presos pela polícia.\n",
    "> A polícia tem provas insuficientes para os condenar,\n",
    "> mas, separando os prisioneiros, oferece a ambos o mesmo acordo:\n",
    "> se um dos prisioneiros, confessando, testemunhar contra o outro\n",
    "> e esse outro permanecer em silêncio,\n",
    "> o que confessou sai livre\n",
    "> enquanto o cúmplice silencioso cumpre 10 anos de sentença.\n",
    "> Se ambos ficarem em silêncio,\n",
    "> a polícia só pode condená-los a 6 meses de cadeia cada um.\n",
    "> Se ambos traírem o comparsa, cada um leva 5 anos de cadeia.\n",
    "> Cada prisioneiro faz a sua decisão\n",
    "> sem saber que decisão o outro vai tomar,\n",
    "> e nenhum tem certeza da decisão do outro.\n",
    "> A questão que o dilema propõe é: o que vai acontecer?\n",
    "> Como o prisioneiro vai reagir?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "\\text{A} \\quad\\quad \\left|\n",
    "\\begin{array}{rrcll}\n",
    "{}           & A\\,coopera                  &                                           & B\\,coopera \\\\\n",
    "A\\,trapaceia &                             & 6\\,\\text{meses} \\,\\Big|\\, 6\\,\\text{meses} &            & B\\,trapaceia \\\\\n",
    "{}           & 0 \\,\\Big|\\, 10\\,\\text{anos} &                                           & 10\\,\\text{anos} \\,\\Big|\\, 0 \\\\\n",
    "{}           &                             & 5\\,\\text{anos} \\,\\Big|\\, 5\\,\\text{anos}\n",
    "\\end{array}\n",
    "\\right| \\quad\\quad \\text{B}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para o jogador $A$ tomar uma decisão,\n",
    "ele se preocupa com a decisão de $B$:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Suponha que $B$ irá trapacear. Qual a decisão de $A$ que minimiza a própria pena?\n",
    "\n",
    "$$\n",
    "\\text{A} \\quad\\quad \\left|\n",
    "\\begin{array}{rrcll}\n",
    "{}           & A\\,coopera &                                                    & \\cancel{B\\,coopera} \\\\\n",
    "A\\,trapaceia &            & \\cancel{6\\,\\text{meses} \\,\\Big|\\, 6\\,\\text{meses}} & & B\\,trapaceia \\\\\n",
    "{}           & \\cancel{0 \\,\\Big|\\, 10\\,\\text{anos}} &                          & 10\\,\\text{anos} \\,\\Big|\\, 0 \\\\\n",
    "{}           &                                      & 5\\,\\text{anos} \\,\\Big|\\, 5\\,\\text{anos}\n",
    "\\end{array}\n",
    "\\right| \\quad\\quad \\text{B}\n",
    "$$\n",
    "\n",
    "Trapaceando, a pena é menor!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Suponha que $B$ irá cooperar. Qual a decisão de $A$ que minimiza a própria pena?\n",
    "\n",
    "$$\n",
    "\\text{A} \\quad\\quad \\left|\n",
    "\\begin{array}{rrcll}\n",
    "{}           & A\\,coopera                  &                                           & B\\,coopera \\\\\n",
    "A\\,trapaceia &                             & 6\\,\\text{meses} \\,\\Big|\\, 6\\,\\text{meses} & & \\cancel{B\\,trapaceia} \\\\\n",
    "{}           & 0 \\,\\Big|\\, 10\\,\\text{anos} &                                           & \\cancel{10\\,\\text{anos} \\,\\Big|\\, 0} \\\\\n",
    "{}           &                             & \\cancel{5\\,\\text{anos} \\,\\Big|\\, 5\\,\\text{anos}}\n",
    "\\end{array}\n",
    "\\right| \\quad\\quad \\text{B}\n",
    "$$\n",
    "\n",
    "Novamente, a pena é menor trapaceando!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dilema:\n",
    "\n",
    "- Trapacear levará a um resultado melhor, independente da ação do adversário;\n",
    "- Os resultados de ambos colaborando é melhor que o de ambos trapaceando."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Caso os 2 jogadores $A$ e $B$ nunca mais se encontrem,\n",
    "a solução desse jogo é sempre trapacear.\n",
    "Segundo Axelrod e Hamilton (1981),\n",
    "essa é a solução estável biológica:\n",
    "\n",
    "> [...] se as recompensas forem derivadas da aptidão,\n",
    "> e as interações entre pares de indivíduos\n",
    "> forem aleatórias e não se repetirem,\n",
    "> então qualquer população\n",
    "> com uma mistura de estratégias herdáveis\n",
    "> evoluirá para um estado no qual\n",
    "> todos os indivíduos são desertores.\n",
    "> Além disso, nenhuma estratégia mutante distinta\n",
    "> poderá sozinha fazer melhor que o restante\n",
    "> quando a população estiver utilizando tal estratégia\n",
    "> (sempre desertar).\n",
    "> Nesse quesito, a estratégia de deserção é estável."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dilema do prisioneiro iterado"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*O que acontece quando os jogadores jogam o mesmo jogo (dilema do prisioneiro) mais de uma vez?*\n",
    "\n",
    "Em 1981, o cientista político Robert Axelrod\n",
    "junto com o biólogo William Donald Hamilton\n",
    "publicaram um artigo chamado \n",
    "*The Evolution of Cooperation*\n",
    "(A evolução da cooperação),\n",
    "o qual possui um estudo desse jogo iterado,\n",
    "com a aplicação de diferentes estratégias,\n",
    "além de exemplos de contextualização biológica.\n",
    "Em 1984, Axelrod publicou um livro homônimo\n",
    "como continuação à pesquisa."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nicky Case criou um jogo baseado nessa pesquisa!!!\n",
    "\n",
    "URL: <https://ncase.me/trust/>.\n",
    "\n",
    "A continuação deste *notebook* depende do conteúdo do link acima.\n",
    "O texto da continuação deste *notebook* é fundamentalmente\n",
    "uma tentativa de simular e analisar, no Python,\n",
    "diferentes estratégias para o dilema do prisioneiro iterado.\n",
    "\n",
    "O código fonte do material do Nicky Case está disponível em\n",
    "<https://github.com/ncase/trust/>."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos estudar as diferentes estratégias de acordo com:\n",
    "\n",
    "- Robustez (persiste em um contexto no qual há diversas outras estratégias)\n",
    "- Estabilidade (quando estabelecida, resiste a novas estratégias pouco frequentes)\n",
    "- Viabilidade de início\n",
    "  (em ambientes predominantemente não-cooperativos, é possível ter posição?)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclusão do artigo de 1981 de Axelrod e Hamilton:\n",
    "\n",
    "> A ênfase de Darwin na vantagem individual\n",
    "> foi formalizada utilizando a teoria dos jogos.\n",
    "> Isto cria condições sobre as quais\n",
    "> a cooperação baseada em reciprocidade\n",
    "> pode evoluir."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Biblioteca `axelrod` no PyPI"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Há uma biblioteca em Python criada exatamente para analisar,\n",
    "com diferentes estratégias, o dilema do prisioneiro iterado!\n",
    "Ela pode ser obtida em <https://github.com/Axelrod-Python/Axelrod>\n",
    "ou no [PyPI](https://pypi.org/project/axelrod).\n",
    "A instalação pode ser feita com `pip install axelrod`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'4.4.0'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import axelrod as axl\n",
    "axl.seed(0) # Usaremos valores aleatórios!\n",
    "axl.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Documentação da biblioteca:\n",
    "<https://axelrod.readthedocs.io/en/stable/index.html>."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `axl.Game`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ao definir um jogo personalizado,\n",
    "os valores para usar na biblioteca são da seguinte forma\n",
    "(nomes definidos no artigo de 1981):\n",
    "\n",
    "- $R$ (*Reward*): Recompensa quando ambos cooperam\n",
    "- $P$ (*Punishment*): Penalidade quando ambos trapaçam\n",
    "- $S$ (*Sucker*): Sacaneado, perda por ingenuidade para quem cooperou com um trapaceiro\n",
    "- $T$ (*Temptation*): Tentação para o trapaceiro que enganou quem cooperou"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "|${}_{\\displaystyle A} \\backslash {}^{\\displaystyle B}$|$B$ coopera|$B$ trapaça|\n",
    "|-|-|-|\n",
    "|**$A$ coopera**|${}_{\\displaystyle R} \\backslash {}^{\\displaystyle R}$|${}_{\\displaystyle S} \\backslash {}^{\\displaystyle T}$|\n",
    "|**$A$ trapaça**|${}_{\\displaystyle T} \\backslash {}^{\\displaystyle S}$|${}_{\\displaystyle P} \\backslash {}^{\\displaystyle P}$|"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Temos um dilema do prisioneiro quando:\n",
    "\n",
    "- $T > R > P > S$, pois $T > R$ e $P > S$ são as inequações\n",
    "  que justificam a trapaça/deserção no jogo não iterado,\n",
    "  e $R > P$ é o que faz a cooperação mútua ser melhor\n",
    "  que a trapaça/deserção mútua; e\n",
    "- $R > \\dfrac{S + T}{2}$, do contrário uma defasada alternância\n",
    "  entre colaboração e deserção (o \"ciclo de vingança\" do *tit-for-tat*)\n",
    "  traria um resultado melhor que a constante cooperação mútua.\n",
    "\n",
    "No dilema do prisioneiro inicial deste *notebook*, em anos, temos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Axelrod game: (R,P,S,T) = (-0.5, -5, -10, 0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pdilema = axl.Game(t=0, r=-.5, p=-5, s=-10)\n",
    "pdilema"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos ver a tabela de pontuações/resultados desse jogo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{(C, C): (-0.5, -0.5), (D, D): (-5, -5), (C, D): (-10, 0), (D, C): (0, -10)}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pdilema.scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Os valores `C` e `D` correspondem ao *enum* `Action`,\n",
    "que possui os dois tipos de ações:\n",
    "\n",
    "* `axl.Action.C` (*Cooperating*): Colaborar\n",
    "* `axl.Action.D` (*Defection/Deceiving*): Desertar/Enganar/Trapacear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-10, 0)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pdilema.score((axl.Action.C, axl.Action.D))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O jogo padrão, quando não definido,\n",
    "é dado por $(R, P, S, T) = (3, 1, 0, 5)$,\n",
    "o jogo exemplificado no artigo de 1981."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Estratégias e jogadores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O pacote já vem com muitas estratégias prontas!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "227"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(axl.strategies)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Essa é uma lista de classes, cada uma definindo uma estratégia.\n",
    "Jogadores são instâncias dessas classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[axelrod.strategies.adaptive.Adaptive,\n",
       " axelrod.strategies.titfortat.AntiTitForTat,\n",
       " axelrod.strategies.axelrod_second.Borufsen,\n",
       " axelrod.strategies.cooperator.Cooperator]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "axl.strategies[:31:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Os nomes estão despejados no namespace principal\n",
    "(podemos usar `axl.Cooperator` ao invés de\n",
    " `axl.strategies.cooperator.Cooperator`).\n",
    "Quais são as estratégias dos personagens vistos\n",
    "no projeto do Nicky Case?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](peep_characters.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "|Código             |Player      |Jogador   |\n",
    "|------------------:|:----------:|:--------:|\n",
    "|`axl.TitForTat()`  |*Copycat*   |Copiador  |\n",
    "|`axl.Defector()`   |*Cheater*   |Trapaceiro|\n",
    "|`axl.Cooperator()` |*Cooperator*|Cooperador|\n",
    "|`axl.Grudger()`    |*Grudger*   |Rancoroso |\n",
    "|?                  |*Detective* |Detetive  |\n",
    "|`axl.TitFor2Tats()`|*Copykitten*|Copiador2 |\n",
    "|?                  |*Simpleton* |Simplista |\n",
    "|`axl.Random()`     |*Random*    |Aleatório |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para a maioria dos personagens, há uma classe.\n",
    "Para os outros, veremos depois como criar uma classe.\n",
    "Por hora, vamos aprender a usar essas classes prontas."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A documentação das estratégias encontra-se em\n",
    "<https://axelrod.readthedocs.io/en/stable/reference/all_strategies.html>."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Um jogo por vez!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "jogador_a = axl.TitForTat()\n",
    "jogador_b = axl.Defector()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Os jogadores possuem o método `play`,\n",
    "que realiza um jogo\n",
    "e armazena os resultados nos próprios jogadores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "jogador_a.play(jogador_b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'init_kwargs': OrderedDict(),\n",
       " 'history': [C],\n",
       " 'classifier': {'memory_depth': 1,\n",
       "  'stochastic': False,\n",
       "  'makes_use_of': set(),\n",
       "  'long_run_time': False,\n",
       "  'inspects_source': False,\n",
       "  'manipulates_source': False,\n",
       "  'manipulates_state': False},\n",
       " 'cooperations': 1,\n",
       " 'defections': 0,\n",
       " 'state_distribution': defaultdict(int, {(C, D): 1}),\n",
       " 'match_attributes': {'length': -1,\n",
       "  'game': Axelrod game: (R,P,S,T) = (3, 1, 0, 5),\n",
       "  'noise': 0}}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vars(jogador_a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'init_kwargs': OrderedDict(),\n",
       " 'history': [D],\n",
       " 'classifier': {'memory_depth': 0,\n",
       "  'stochastic': False,\n",
       "  'makes_use_of': set(),\n",
       "  'long_run_time': False,\n",
       "  'inspects_source': False,\n",
       "  'manipulates_source': False,\n",
       "  'manipulates_state': False},\n",
       " 'cooperations': 0,\n",
       " 'defections': 1,\n",
       " 'state_distribution': defaultdict(int, {(D, C): 1}),\n",
       " 'match_attributes': {'length': -1,\n",
       "  'game': Axelrod game: (R,P,S,T) = (3, 1, 0, 5),\n",
       "  'noise': 0}}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vars(jogador_b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Jogos subsequentes irão fazer o histórico crescer\n",
    "e atualizará o número de cooperações ou trapaças."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "jogador_b.play(jogador_a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(C, D), (D, D)]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(zip(jogador_a.history, jogador_b.history))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O número de vezes em que em que cada jogador cooperou e traiu\n",
    "está nos atributos `cooperations` e `defections`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'A': (1, 1), 'B': (0, 2)}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jogadores_dict = {\"A\": jogador_a, \"B\": jogador_b}\n",
    "{name: (j.cooperations, j.defections)\n",
    " for name, j in jogadores_dict.items()}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos limpar o estado do jogador com o método `reset`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 0, [])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jogador_a.reset()\n",
    "jogador_a.cooperations, jogador_a.defections, jogador_a.history"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Partida de jogos consecutivos entre $2$ jogadores, com `axl.Match`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Embora seja possível usar o método `play` dos jogadores,\n",
    "há uma forma mais prática de realizar\n",
    "uma sequência de acontecimentos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(C, D), (D, D), (D, C), (C, C), (C, D)]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "players_match = [axl.TitForTat(), axl.Random()]\n",
    "match = axl.Match(players=players_match, turns=5)\n",
    "match.play()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aqui estamos usando o jogo padrão\n",
    "$(R, P, S, T) = (3, 1, 0, 5)$.\n",
    "Para mudar, basta passar o parâmetro `game`\n",
    "na instanciação do `axl.Match`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, 5), (1, 1), (5, 0), (3, 3), (0, 5)]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "match.scores()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Jogador humano"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A classe `axl.Human` corresponde ao jogador humano,\n",
    "com dados entrados manualmente via *stdin*.\n",
    "Não funciona no Jupyter notebook.\n",
    "Exemplo de partida contra o *copycat*:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "import axelrod as axl\n",
    "players = [axl.Human(name=\"Fulano\"), axl.TitForTat()]\n",
    "match = axl.Match(players, turns=8)\n",
    "match.play()\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Estratégia personalizada"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Estratégias são classes que herdam de `axl.Player`\n",
    "e possuem:\n",
    "\n",
    "* Atributo `name`, string com o nome da estratégia para exibição\n",
    "* Atributo `classifier` com outros metadados (opcional)\n",
    "* Método `strategy(self, opponent)`:\n",
    "  método que deve realizar uma partida\n",
    "  contra um dado oponente\n",
    "  e deve devolver uma ação `axl.Action.C` ou `axl.Action.D`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A tabela não mapeou em estratégias do pacote `axelrod`\n",
    "todas as estratégias vistas na implementação do Nicky Case.\n",
    "Vamos criar classes para as estratégias que faltaram!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "C = axl.Action.C\n",
    "D = axl.Action.D"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Detetive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Detective(axl.Player):\n",
    "    name = \"Detective\"\n",
    "    classifier = {\n",
    "        \"memory_depth\": float(\"inf\"),\n",
    "        \"stochastic\": False,\n",
    "        \"makes_use_of\": set(),\n",
    "        \"long_run_time\": False,\n",
    "        \"inspects_source\": False,\n",
    "        \"manipulates_source\": False,\n",
    "        \"manipulates_state\": False,\n",
    "    }\n",
    "    def strategy(self, opponent):\n",
    "        hist_size = len(self.history)\n",
    "        if hist_size <= 3:\n",
    "            return [C, D, C, C][hist_size]\n",
    "        if D not in opponent.history[:4]:\n",
    "            return D\n",
    "        return opponent.history[-1] # TFT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exemplos do detetive contra o copiador e contra o cooperador:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(C, C), (C, D), (D, C), (C, C), (C, C), (C, C), (C, C), (C, C)]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "axl.Match(\n",
    "    players=[axl.TitForTat(), Detective()],\n",
    "    turns=8,\n",
    ").play()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(C, C), (C, D), (C, C), (C, C), (C, D), (C, D), (C, D), (C, D)]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "axl.Match(\n",
    "    players=[axl.Cooperator(), Detective()],\n",
    "    turns=8,\n",
    ").play()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Simplista"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Simpleton(axl.Player):\n",
    "    name = \"Simpleton\"\n",
    "    classifier = {\n",
    "        \"memory_depth\": 1,\n",
    "        \"stochastic\": False,\n",
    "        \"makes_use_of\": set(),\n",
    "        \"long_run_time\": False,\n",
    "        \"inspects_source\": False,\n",
    "        \"manipulates_source\": False,\n",
    "        \"manipulates_state\": False,\n",
    "    }\n",
    "    def strategy(self, opponent):\n",
    "        if not self.history:\n",
    "            return C\n",
    "        if opponent.history[-1] == C:\n",
    "            return self.history[-1]\n",
    "        if self.history[-1] == C:\n",
    "            return D\n",
    "        return C"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exemplos do simplista contra o trapaceiro\n",
    "e contra o \"alternador\" (`axl.Alternator`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(D, C), (D, D), (D, C), (D, D), (D, C), (D, D), (D, C), (D, D)]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "axl.Match(\n",
    "    players=[axl.Defector(), Simpleton()],\n",
    "    turns=8,\n",
    ").play()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`axl.Alternator` é a estratégia cíclica constante C D C D C D ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(C, C), (D, C), (C, D), (D, D), (C, C), (D, C), (C, D), (D, D)]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "axl.Match(\n",
    "    players=[axl.Alternator(), Simpleton()],\n",
    "    turns=8,\n",
    ").play()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Um único torneio / campeonato com `axl.Tournament`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Todos contra todos apenas uma vez,\n",
    "como o primeiro torneio da explicação do Nicky Case.\n",
    "Qual dos $5$ jogadores irá vencer esse torneio?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Playing matches: 100%|██████████| 15/15 [00:00<00:00, 614.72it/s]\n",
      "Analysing: 100%|██████████| 25/25 [00:00<00:00, 329.14it/s]\n"
     ]
    }
   ],
   "source": [
    "players_nc1 = [\n",
    "    axl.TitForTat(),\n",
    "    axl.Defector(),\n",
    "    axl.Cooperator(),\n",
    "    axl.Grudger(),\n",
    "    Detective(),\n",
    "]\n",
    "game_nc1 = axl.Game(r=2, p=0, s=-1, t=3)\n",
    "tournament = axl.Tournament(\n",
    "    players_nc1,\n",
    "    game=game_nc1,\n",
    "    turns=10,\n",
    "    repetitions=1,\n",
    ")\n",
    "tresults = tournament.play()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Torneio realizado.\n",
    "Agora podemos ver os resultados!\n",
    "Quem ganhou?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Tit For Tat', 'Grudger', 'Defector', 'Detective', 'Cooperator']"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tresults.ranked_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos ver a mesma informação\n",
    "com base nos índices do objeto original\n",
    "(lista `players_nc1`)\n",
    "ao invés de strings de nomes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 3, 1, 4, 2]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tresults.ranking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([Tit For Tat, Grudger, Defector, Detective, Cooperator],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(players_nc1)[tresults.ranking]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Tit For Tat', 'Grudger', 'Defector', 'Detective', 'Cooperator'],\n",
       "      dtype='<U11')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(tresults.players)[tresults.ranking]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pontuações finais:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[57], [45], [29], [46], [45]]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tresults.scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Pontuação média / amortizada"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Esses foram os resultados médios de $10$ jogos entre de cada par de jogadores\n",
    "(a linha do jogador representa o quanto ele \"ganhou\" ou \"perdeu\",\n",
    " a coluna do jogador representa o quanto ele fez o adversário \"ganhar\" ou \"perder\"):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[2.0, -0.1, 2.0, 2.0, 1.8],\n",
       " [0.3, 0.0, 3.0, 0.3, 0.9],\n",
       " [2.0, -1.0, 2.0, 2.0, -0.1],\n",
       " [2.0, -0.1, 2.0, 2.0, 0.7],\n",
       " [1.8, -0.3, 2.7, 0.3, 1.8]]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tresults.payoff_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tit For Tat</th>\n",
       "      <th>Defector</th>\n",
       "      <th>Cooperator</th>\n",
       "      <th>Grudger</th>\n",
       "      <th>Detective</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Tit For Tat</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Defector</th>\n",
       "      <td>0.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cooperator</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grudger</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Detective</th>\n",
       "      <td>1.8</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Tit For Tat  Defector  Cooperator  Grudger  Detective\n",
       "Tit For Tat          2.0      -0.1         2.0      2.0        1.8\n",
       "Defector             0.3       0.0         3.0      0.3        0.9\n",
       "Cooperator           2.0      -1.0         2.0      2.0       -0.1\n",
       "Grudger              2.0      -0.1         2.0      2.0        0.7\n",
       "Detective            1.8      -0.3         2.7      0.3        1.8"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "payoff_matrix_df_nc1 = pd.DataFrame(tresults.payoff_matrix,\n",
    "                                    index=tresults.players,\n",
    "                                    columns=tresults.players)\n",
    "sns.heatmap(payoff_matrix_df_nc1, annot=True).set(\n",
    "    xlabel=\"Payoff offered\",\n",
    "    ylabel=\"Payoff received\",\n",
    ")\n",
    "payoff_matrix_df_nc1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ao invés de usar o `seaborn`,\n",
    "pode-se usar o recurso nativo da biblioteca `axelrod`\n",
    "para fazer o *heat map*\n",
    "(a ordem das linhas/colunas é a do ranking, não a da entrada):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "axl.Plot(tresults).payoff()\n",
    "plt.gcf().set(figwidth=5, figheight=5);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A biblioteca `axelrod` possui outros recursos de plot,\n",
    "mas tais recursos não serão vistos neste *notebook*,\n",
    "com exceção do gráfico do processo de Moran."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Sumário estatístico"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Rank</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <td>Tit For Tat</td>\n",
       "      <td>Grudger</td>\n",
       "      <td>Defector</td>\n",
       "      <td>Detective</td>\n",
       "      <td>Cooperator</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Median_score</th>\n",
       "      <td>1.425</td>\n",
       "      <td>1.15</td>\n",
       "      <td>1.125</td>\n",
       "      <td>1.125</td>\n",
       "      <td>0.725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cooperation_rating</th>\n",
       "      <td>0.75</td>\n",
       "      <td>0.575</td>\n",
       "      <td>0</td>\n",
       "      <td>0.45</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wins</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Initial_C_rate</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_rate</th>\n",
       "      <td>0.7</td>\n",
       "      <td>0.525</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CD_rate</th>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DC_rate</th>\n",
       "      <td>0.025</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.225</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DD_rate</th>\n",
       "      <td>0.225</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.625</td>\n",
       "      <td>0.325</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_to_C_rate</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.595238</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CD_to_C_rate</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.611111</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DC_to_C_rate</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.722222</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DD_to_C_rate</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Rank                          0        1         2          3           4\n",
       "Name                Tit For Tat  Grudger  Defector  Detective  Cooperator\n",
       "Median_score              1.425     1.15     1.125      1.125       0.725\n",
       "Cooperation_rating         0.75    0.575         0       0.45           1\n",
       "Wins                          0        1         4          1           0\n",
       "Initial_C_rate                1        1         0          1           1\n",
       "CC_rate                     0.7    0.525         0        0.3       0.575\n",
       "CD_rate                    0.05     0.05         0       0.15       0.425\n",
       "DC_rate                   0.025     0.05     0.375      0.225           0\n",
       "DD_rate                   0.225    0.375     0.625      0.325           0\n",
       "CC_to_C_rate                  1        1         0   0.595238           1\n",
       "CD_to_C_rate                  0        0         0   0.611111           1\n",
       "DC_to_C_rate                  1        0         0   0.722222           0\n",
       "DD_to_C_rate                  0        0         0   0.166667           0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tsummary = tresults.summarise()\n",
    "pd.DataFrame(tsummary).set_index(\"Rank\").T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cooperação"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O número de cooperações de cada *match* também é guardado,\n",
    "caso a caso."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[10, 1, 10, 10, 9],\n",
       " [0, 0, 0, 0, 0],\n",
       " [10, 10, 10, 10, 10],\n",
       " [10, 1, 10, 10, 2],\n",
       " [9, 3, 3, 3, 9]]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tresults.cooperation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tit For Tat</th>\n",
       "      <th>Defector</th>\n",
       "      <th>Cooperator</th>\n",
       "      <th>Grudger</th>\n",
       "      <th>Detective</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Tit For Tat</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Defector</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cooperator</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grudger</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Detective</th>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Tit For Tat  Defector  Cooperator  Grudger  Detective\n",
       "Tit For Tat           10         1          10       10          9\n",
       "Defector               0         0           0        0          0\n",
       "Cooperator            10        10          10       10         10\n",
       "Grudger               10         1          10       10          2\n",
       "Detective              9         3           3        3          9"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cooperation_df_nc1 = pd.DataFrame(tresults.cooperation,\n",
    "                                  index=tresults.players,\n",
    "                                  columns=tresults.players)\n",
    "sns.heatmap(cooperation_df_nc1, annot=True).set(\n",
    "    xlabel=\"Cooperations received\",\n",
    "    ylabel=\"Cooperations emitted\",\n",
    ")\n",
    "cooperation_df_nc1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para referência, o número de partidas neste caso foi constante,\n",
    "igual a $10$ (número armazenado em `tournament.turns`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[[10.0, 10.0, 10.0, 10.0, 10.0],\n",
       "  [10.0, 10.0, 10.0, 10.0, 10.0],\n",
       "  [10.0, 10.0, 10.0, 10.0, 10.0],\n",
       "  [10.0, 10.0, 10.0, 10.0, 10.0],\n",
       "  [10.0, 10.0, 10.0, 10.0, 10.0]]]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tresults.match_lengths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tournament.turns # Poderia ser diferente, se usássemos o prob_end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tresults.num_players # Número de linhas e colunas das matrizes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A divisão do número de cooperações pelo número de partidas\n",
    "está disponível diretamente:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[1.0, 0.1, 1.0, 1.0, 0.9],\n",
       " [0.0, 0.0, 0.0, 0.0, 0.0],\n",
       " [1.0, 1.0, 1.0, 1.0, 1.0],\n",
       " [1.0, 0.1, 1.0, 1.0, 0.2],\n",
       " [0.9, 0.3, 0.3, 0.3, 0.9]]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tresults.normalised_cooperation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ignorando a diagonal, a média de cada linha\n",
    "nos fornece a taxa de cooperação:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.75 , 0.   , 1.   , 0.575, 0.45 ])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ncooperation_ar_nc1 = np.array(tresults.normalised_cooperation)\n",
    "np.fill_diagonal(ncooperation_ar_nc1, 0)\n",
    "ncooperation_ar_nc1.sum(axis=1) / (tresults.num_players - 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ou, diretamente:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.75, 0.0, 1.0, 0.575, 0.45]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tresults.cooperating_rating"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Esses valores fundamentam\n",
    "as [medidas de \"moralidade\" de Tyler Singer-Clark](\n",
    "https://axelrod.readthedocs.io/en/stable/tutorials/further_topics/morality_metrics.html\n",
    "),\n",
    "tais como a matriz de \"boa parceria\",\n",
    "nos quais o jogador da linha associada (jogador de referência)\n",
    "colabora pelo menos o mesmo número de vezes\n",
    "que seu diferente adversário.\n",
    "Especificamente, o elemento $b_{ij}$ da matriz resultante\n",
    "é $1$ quando $a_{ij} \\ge a_{ji}$ e $i \\ne j$,\n",
    "e $0$ caso contrário,\n",
    "sendo $a_{ij}$ o elemento da $i$-ésima linha\n",
    "e $j$-ésima coluna da matriz de cooperações."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[0, 1, 1, 1, 1],\n",
       " [0, 0, 0, 0, 0],\n",
       " [1, 1, 0, 1, 1],\n",
       " [1, 1, 1, 0, 0],\n",
       " [1, 1, 0, 1, 0]]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tresults.good_partner_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tit For Tat</th>\n",
       "      <th>Defector</th>\n",
       "      <th>Cooperator</th>\n",
       "      <th>Grudger</th>\n",
       "      <th>Detective</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Tit For Tat</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Defector</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cooperator</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grudger</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Detective</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Tit For Tat  Defector  Cooperator  Grudger  Detective\n",
       "Tit For Tat            0         1           1        1          1\n",
       "Defector               0         0           0        0          0\n",
       "Cooperator             1         1           0        1          1\n",
       "Grudger                1         1           1        0          0\n",
       "Detective              1         1           0        1          0"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "good_partner_df_nc1 = pd.DataFrame(tresults.good_partner_matrix,\n",
    "                                  index=tresults.players,\n",
    "                                  columns=tresults.players)\n",
    "sns.heatmap(good_partner_df_nc1).set(\n",
    "    xlabel=\"Adversary\",\n",
    "    ylabel=\"Reference player\",\n",
    ")\n",
    "good_partner_df_nc1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Processo de Moran"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Esse processo consiste em uma simulação da seleção natural,\n",
    "em que a pontuação em cada partida sinaliza a aptidão do jogador.\n",
    "A grosso modo, é um laço de $3$ passos/*rounds*/rodadas:\n",
    "\n",
    "* São realizadas partidas entre cada par de jogadores, guardando os resultados acumulados\n",
    "* Um jogador é escolhido para \"reproduzir\" (ter sua estratégia clonada),\n",
    "  com probabilidade proporcional à pontuação naquele *round*/partida\n",
    "* Um jogador é escolhido aleatoriamente para ser removido\n",
    "\n",
    "O parâmetro `mode=\"db\"` do `axl.MoranProcess` inverte os $2$ últimos passos,\n",
    "tornando o modelo mais próximo do exemplo do Nicky Case.\n",
    "\n",
    "Maiores informações em\n",
    "<https://axelrod.readthedocs.io/en/stable/tutorials/getting_started/moran.html>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_players = (\n",
    "    5 * [axl.TitForTat()] +\n",
    "    25 * [axl.Defector()] +\n",
    "    5 * [axl.Cooperator()] +\n",
    "    8 * [axl.Grudger()] +\n",
    "    10 * [Detective()] +\n",
    "    5 * [axl.TitFor2Tats()] +\n",
    "    5 * [Simpleton()] +\n",
    "    10 * [axl.Random()]\n",
    ")\n",
    "mp = axl.MoranProcess(\n",
    "    players=all_players,\n",
    "    noise=.05,\n",
    "    turns=10,\n",
    "    prob_end=.01,\n",
    "    game=game_nc1,\n",
    "    mode=\"db\",\n",
    ")\n",
    "max_rounds = 701\n",
    "list(zip(range(max_rounds), mp)); # Ou mp.play()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Nota*:\n",
    "Apenas use `populations = mp.play()`\n",
    "se souber que, em tempo finito,\n",
    "o processo sempre resultará em uma sociedade homogênea."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Há \"vencedor\" (única estratégia sobrevivente)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# String com o nome da estratégia, ou None se o resultado não for homogêneo\n",
    "mp.winning_strategy_name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Em cada rodada, haviam quantos indivíduos de cada estratégia?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cooperator</th>\n",
       "      <th>Defector</th>\n",
       "      <th>Detective</th>\n",
       "      <th>Grudger</th>\n",
       "      <th>Random: 0.5</th>\n",
       "      <th>Simpleton</th>\n",
       "      <th>Tit For 2 Tats</th>\n",
       "      <th>Tit For Tat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>24</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5</td>\n",
       "      <td>24</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5</td>\n",
       "      <td>26</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>692</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>693</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>694</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>695</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>696</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>697</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>698</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>699</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>700</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>701</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>702 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Cooperator  Defector  Detective  Grudger  Random: 0.5  Simpleton  \\\n",
       "0             5        25         10        8           10          5   \n",
       "1             5        24         10        8           10          5   \n",
       "2             5        23         10        8           10          6   \n",
       "3             5        23         10        7           10          6   \n",
       "4             5        23         10        7           10          6   \n",
       "5             5        23         11        7           10          5   \n",
       "6             5        24         11        7            9          5   \n",
       "7             5        25         11        7            8          5   \n",
       "8             5        26         11        7            7          5   \n",
       "9             5        25         12        7            7          5   \n",
       "..          ...       ...        ...      ...          ...        ...   \n",
       "692           0         0          8        0            0          0   \n",
       "693           0         0          8        0            0          0   \n",
       "694           0         0          8        0            0          0   \n",
       "695           0         0          8        0            0          0   \n",
       "696           0         0          9        0            0          0   \n",
       "697           0         0          9        0            0          0   \n",
       "698           0         0          9        0            0          0   \n",
       "699           0         0          9        0            0          0   \n",
       "700           0         0          9        0            0          0   \n",
       "701           0         0          9        0            0          0   \n",
       "\n",
       "     Tit For 2 Tats  Tit For Tat  \n",
       "0                 5            5  \n",
       "1                 5            6  \n",
       "2                 5            6  \n",
       "3                 6            6  \n",
       "4                 6            6  \n",
       "5                 6            6  \n",
       "6                 6            6  \n",
       "7                 6            6  \n",
       "8                 6            6  \n",
       "9                 6            6  \n",
       "..              ...          ...  \n",
       "692               0           65  \n",
       "693               0           65  \n",
       "694               0           65  \n",
       "695               0           65  \n",
       "696               0           64  \n",
       "697               0           64  \n",
       "698               0           64  \n",
       "699               0           64  \n",
       "700               0           64  \n",
       "701               0           64  \n",
       "\n",
       "[702 rows x 8 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(mp.populations).fillna(0).astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Há mais de uma forma imediata\n",
    "de representar graficamente o número de indivíduos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 10))\n",
    "mp.populations_plot(ax=ax)\n",
    "ax.set(xlim=[0, max_rounds], ylim=[0, len(all_players)])\n",
    "ax.legend(loc=\"center left\", bbox_to_anchor=(1, .5));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x1152 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 16))\n",
    "sns.heatmap(pd.DataFrame(mp.populations).fillna(0));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As pontuações de cada rodada também são armazenadas,\n",
    "embora não seja trivial interpretar esses dados\n",
    "(a estratégia do indivíduo da $i$-ésima coluna\n",
    "não é a mesma em todas as linhas/rodadas):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>...</th>\n",
       "      <th>68</th>\n",
       "      <th>69</th>\n",
       "      <th>70</th>\n",
       "      <th>71</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60.700000</td>\n",
       "      <td>72.458333</td>\n",
       "      <td>70.057143</td>\n",
       "      <td>64.788889</td>\n",
       "      <td>...</td>\n",
       "      <td>49.705556</td>\n",
       "      <td>45.261111</td>\n",
       "      <td>40.523810</td>\n",
       "      <td>49.092063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>69.800000</td>\n",
       "      <td>72.598413</td>\n",
       "      <td>63.550000</td>\n",
       "      <td>66.647222</td>\n",
       "      <td>...</td>\n",
       "      <td>50.225000</td>\n",
       "      <td>49.380952</td>\n",
       "      <td>54.504762</td>\n",
       "      <td>47.983333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>63.983333</td>\n",
       "      <td>71.582540</td>\n",
       "      <td>63.275000</td>\n",
       "      <td>62.511905</td>\n",
       "      <td>...</td>\n",
       "      <td>54.175000</td>\n",
       "      <td>51.400000</td>\n",
       "      <td>50.950000</td>\n",
       "      <td>50.629762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>73.291667</td>\n",
       "      <td>67.642857</td>\n",
       "      <td>71.864286</td>\n",
       "      <td>67.814286</td>\n",
       "      <td>...</td>\n",
       "      <td>44.973016</td>\n",
       "      <td>49.300000</td>\n",
       "      <td>50.733333</td>\n",
       "      <td>48.521429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>70.311111</td>\n",
       "      <td>64.514286</td>\n",
       "      <td>70.495238</td>\n",
       "      <td>72.350000</td>\n",
       "      <td>...</td>\n",
       "      <td>50.211111</td>\n",
       "      <td>52.952778</td>\n",
       "      <td>48.877778</td>\n",
       "      <td>49.283333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>68.469841</td>\n",
       "      <td>68.344444</td>\n",
       "      <td>69.172619</td>\n",
       "      <td>69.823810</td>\n",
       "      <td>...</td>\n",
       "      <td>59.466270</td>\n",
       "      <td>49.650794</td>\n",
       "      <td>54.610714</td>\n",
       "      <td>48.207143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>70.007143</td>\n",
       "      <td>65.975000</td>\n",
       "      <td>63.700000</td>\n",
       "      <td>67.157143</td>\n",
       "      <td>...</td>\n",
       "      <td>54.730556</td>\n",
       "      <td>43.083333</td>\n",
       "      <td>52.300000</td>\n",
       "      <td>48.192857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>64.414286</td>\n",
       "      <td>72.969841</td>\n",
       "      <td>66.983333</td>\n",
       "      <td>62.755556</td>\n",
       "      <td>...</td>\n",
       "      <td>51.433333</td>\n",
       "      <td>43.505556</td>\n",
       "      <td>59.500000</td>\n",
       "      <td>42.951984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>70.145238</td>\n",
       "      <td>67.439286</td>\n",
       "      <td>71.500000</td>\n",
       "      <td>67.875000</td>\n",
       "      <td>...</td>\n",
       "      <td>49.141667</td>\n",
       "      <td>47.669444</td>\n",
       "      <td>52.733333</td>\n",
       "      <td>51.388889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>68.500000</td>\n",
       "      <td>62.022222</td>\n",
       "      <td>66.866667</td>\n",
       "      <td>69.389286</td>\n",
       "      <td>...</td>\n",
       "      <td>51.104762</td>\n",
       "      <td>50.677778</td>\n",
       "      <td>55.214286</td>\n",
       "      <td>49.025000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>691</th>\n",
       "      <td>110.800000</td>\n",
       "      <td>114.304762</td>\n",
       "      <td>107.688095</td>\n",
       "      <td>106.623810</td>\n",
       "      <td>...</td>\n",
       "      <td>115.333333</td>\n",
       "      <td>117.752381</td>\n",
       "      <td>112.422222</td>\n",
       "      <td>110.855556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>692</th>\n",
       "      <td>112.435714</td>\n",
       "      <td>118.150000</td>\n",
       "      <td>102.156349</td>\n",
       "      <td>115.600000</td>\n",
       "      <td>...</td>\n",
       "      <td>115.133333</td>\n",
       "      <td>108.292063</td>\n",
       "      <td>111.994444</td>\n",
       "      <td>108.350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>693</th>\n",
       "      <td>112.483333</td>\n",
       "      <td>116.885714</td>\n",
       "      <td>103.355556</td>\n",
       "      <td>109.577778</td>\n",
       "      <td>...</td>\n",
       "      <td>117.692063</td>\n",
       "      <td>113.514286</td>\n",
       "      <td>111.488889</td>\n",
       "      <td>108.111111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>694</th>\n",
       "      <td>112.733333</td>\n",
       "      <td>109.755556</td>\n",
       "      <td>102.605952</td>\n",
       "      <td>111.100000</td>\n",
       "      <td>...</td>\n",
       "      <td>107.274603</td>\n",
       "      <td>110.866667</td>\n",
       "      <td>117.266667</td>\n",
       "      <td>115.073016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>695</th>\n",
       "      <td>110.966667</td>\n",
       "      <td>115.018254</td>\n",
       "      <td>109.650000</td>\n",
       "      <td>114.247619</td>\n",
       "      <td>...</td>\n",
       "      <td>116.957143</td>\n",
       "      <td>119.433333</td>\n",
       "      <td>108.600000</td>\n",
       "      <td>102.261111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>696</th>\n",
       "      <td>113.567857</td>\n",
       "      <td>112.365476</td>\n",
       "      <td>107.699206</td>\n",
       "      <td>114.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>112.191667</td>\n",
       "      <td>115.058333</td>\n",
       "      <td>115.475000</td>\n",
       "      <td>112.347619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>697</th>\n",
       "      <td>112.833333</td>\n",
       "      <td>111.466667</td>\n",
       "      <td>108.880952</td>\n",
       "      <td>113.933333</td>\n",
       "      <td>...</td>\n",
       "      <td>112.705556</td>\n",
       "      <td>118.280952</td>\n",
       "      <td>113.650000</td>\n",
       "      <td>105.963492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>698</th>\n",
       "      <td>112.402778</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>103.377778</td>\n",
       "      <td>103.650000</td>\n",
       "      <td>...</td>\n",
       "      <td>116.665079</td>\n",
       "      <td>116.300000</td>\n",
       "      <td>114.450000</td>\n",
       "      <td>107.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>699</th>\n",
       "      <td>111.600000</td>\n",
       "      <td>100.994444</td>\n",
       "      <td>103.739286</td>\n",
       "      <td>115.275000</td>\n",
       "      <td>...</td>\n",
       "      <td>115.666667</td>\n",
       "      <td>113.471429</td>\n",
       "      <td>113.900000</td>\n",
       "      <td>110.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>700</th>\n",
       "      <td>111.300000</td>\n",
       "      <td>114.050000</td>\n",
       "      <td>110.064286</td>\n",
       "      <td>110.991667</td>\n",
       "      <td>...</td>\n",
       "      <td>111.750000</td>\n",
       "      <td>118.794444</td>\n",
       "      <td>116.716667</td>\n",
       "      <td>111.685714</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>701 rows × 72 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             0           1           2           3      ...              68  \\\n",
       "0     60.700000   72.458333   70.057143   64.788889     ...       49.705556   \n",
       "1     69.800000   72.598413   63.550000   66.647222     ...       50.225000   \n",
       "2     63.983333   71.582540   63.275000   62.511905     ...       54.175000   \n",
       "3     73.291667   67.642857   71.864286   67.814286     ...       44.973016   \n",
       "4     70.311111   64.514286   70.495238   72.350000     ...       50.211111   \n",
       "5     68.469841   68.344444   69.172619   69.823810     ...       59.466270   \n",
       "6     70.007143   65.975000   63.700000   67.157143     ...       54.730556   \n",
       "7     64.414286   72.969841   66.983333   62.755556     ...       51.433333   \n",
       "8     70.145238   67.439286   71.500000   67.875000     ...       49.141667   \n",
       "9     68.500000   62.022222   66.866667   69.389286     ...       51.104762   \n",
       "..          ...         ...         ...         ...     ...             ...   \n",
       "691  110.800000  114.304762  107.688095  106.623810     ...      115.333333   \n",
       "692  112.435714  118.150000  102.156349  115.600000     ...      115.133333   \n",
       "693  112.483333  116.885714  103.355556  109.577778     ...      117.692063   \n",
       "694  112.733333  109.755556  102.605952  111.100000     ...      107.274603   \n",
       "695  110.966667  115.018254  109.650000  114.247619     ...      116.957143   \n",
       "696  113.567857  112.365476  107.699206  114.500000     ...      112.191667   \n",
       "697  112.833333  111.466667  108.880952  113.933333     ...      112.705556   \n",
       "698  112.402778  115.000000  103.377778  103.650000     ...      116.665079   \n",
       "699  111.600000  100.994444  103.739286  115.275000     ...      115.666667   \n",
       "700  111.300000  114.050000  110.064286  110.991667     ...      111.750000   \n",
       "\n",
       "             69          70          71  \n",
       "0     45.261111   40.523810   49.092063  \n",
       "1     49.380952   54.504762   47.983333  \n",
       "2     51.400000   50.950000   50.629762  \n",
       "3     49.300000   50.733333   48.521429  \n",
       "4     52.952778   48.877778   49.283333  \n",
       "5     49.650794   54.610714   48.207143  \n",
       "6     43.083333   52.300000   48.192857  \n",
       "7     43.505556   59.500000   42.951984  \n",
       "8     47.669444   52.733333   51.388889  \n",
       "9     50.677778   55.214286   49.025000  \n",
       "..          ...         ...         ...  \n",
       "691  117.752381  112.422222  110.855556  \n",
       "692  108.292063  111.994444  108.350000  \n",
       "693  113.514286  111.488889  108.111111  \n",
       "694  110.866667  117.266667  115.073016  \n",
       "695  119.433333  108.600000  102.261111  \n",
       "696  115.058333  115.475000  112.347619  \n",
       "697  118.280952  113.650000  105.963492  \n",
       "698  116.300000  114.450000  107.000000  \n",
       "699  113.471429  113.900000  110.700000  \n",
       "700  118.794444  116.716667  111.685714  \n",
       "\n",
       "[701 rows x 72 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(mp.score_history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusões"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seguem *screenshots* das conclusões expostas por Nicky Case\n",
    "(na versão traduzida por Bruno Lemos):"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](nc_conclusao1.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](nc_conclusao2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](nc_conclusao3.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**FIM!**"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}