Teoria_de_Juegos.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Teoria de Juegos.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyOKR+gyGRSEJxwfvUkh3DsC",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/LCaravaggio/AnalisisEconomico/blob/main/Teoria_de_Juegos.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%capture\n",
        "!pip install nashpy\n",
        "!pip install axelrod"
      ],
      "metadata": {
        "id": "fnL87w1bKMAj"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "3MrEwJGeKC6z"
      },
      "outputs": [],
      "source": [
        "import nashpy as nash\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Documentación de la librería: https://nashpy.readthedocs.io/en/stable/how-to/use-stochastic-fictitious-play.html"
      ],
      "metadata": {
        "id": "WwErHABFiRi5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Carrera publicitaria"
      ],
      "metadata": {
        "id": "inCyfTyyRcA3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![image.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "aLzkBAYshrQF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Matríz de pagos de la carrera publicitaria\n",
        "A = np.array([[500,250],[200,300]]) # Empresa 1 (filas)\n",
        "B = np.array([[400,750],[0,200]]) # Empresa 2 (columnas)\n",
        "game1 = nash.Game(A,B)\n",
        "game1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EOIQj2ePLKuM",
        "outputId": "615428a3-86e2-4273-9761-f6f75b824c5e"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Bi matrix game with payoff matrices:\n",
              "\n",
              "Row player:\n",
              "[[500 250]\n",
              " [200 300]]\n",
              "\n",
              "Column player:\n",
              "[[400 750]\n",
              " [  0 200]]"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "equilibria = game1.support_enumeration()\n",
        "for eq in equilibria:\n",
        "    print(eq)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PFFk1c__LPcW",
        "outputId": "63c33496-ea40-47b0-ffb1-832664ead532"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(array([0., 1.]), array([0., 1.]))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Hawk-Dove"
      ],
      "metadata": {
        "id": "Zh_Gqy09M2zv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Este es un juego de interacción con animales, donde una sola especie, tal vez un perro salvaje, exhibe 2 tipos diferentes de comportamiento. Hawk (Halcón) es un comportamiento agresivo, mientras que Dove (paloma) es un comportamiento más pasivo. Digamos que 2 perros están compitiendo por un trozo de carne. Por lo tanto, el halcón es una estrategia de lucha en la que si ambos luchan no obtienen nada y la paloma comparte más y cada uno obtiene una recompensa en este caso. Mientras que si uno pelea y el otro no, el que pelea (el halcón) se queda con todo y el otro nada.\n",
        "\n",
        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)"
      ],
      "metadata": {
        "id": "0mTyFum_M04A"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "A = np.array([[-4,8],[0,4]]) # A is the row player\n",
        "B = np.array([[-4,0],[8,4]]) # B is the column player\n",
        "game = nash.Game(A,B)\n",
        "\n",
        "equilibria = game.support_enumeration()\n",
        "for eq in equilibria:\n",
        "    print(eq)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oTILnZE9M0Jo",
        "outputId": "9fe36607-0759-4fb3-e2cc-2db83f67f37b"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(array([1., 0.]), array([0., 1.]))\n",
            "(array([0., 1.]), array([1., 0.]))\n",
            "(array([0.5, 0.5]), array([0.5, 0.5]))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Simulación de estrategias"
      ],
      "metadata": {
        "id": "ranjTVxlO0a8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import axelrod as axl"
      ],
      "metadata": {
        "id": "S3eKp3C1O-Dv"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Documentación de la librería: https://axelrod.readthedocs.io/en/stable/index.html"
      ],
      "metadata": {
        "id": "O_UEwjpJQj_X"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Mutual cooperation: (C,C)\n",
        "\n",
        "Defection: (C,D) or (D,C)\n",
        "\n",
        "Mutual defection: (D,D)"
      ],
      "metadata": {
        "id": "VNQaEpwlmqQc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "players = (axl.Defector(), axl.Defector())   # Ambos jugadores usan la estrategia de delatar\n",
        "match1 =   axl.Match(players, turns =10)     # Se juega 10 turnos seguidos\n",
        "match1.play()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cWeuvKFFOt7v",
        "outputId": "4d33ed88-d458-4eb0-9b1d-f926cba8b386"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(D, D),\n",
              " (D, D),\n",
              " (D, D),\n",
              " (D, D),\n",
              " (D, D),\n",
              " (D, D),\n",
              " (D, D),\n",
              " (D, D),\n",
              " (D, D),\n",
              " (D, D)]"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Estrategias posibles: https://axelrod.readthedocs.io/en/fix-documentation/reference/all_strategies.html"
      ],
      "metadata": {
        "id": "ifYfbaCxnHhK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "players = (axl.TitForTat(), axl.Random())  # El jugador 1 juega titfortat y el  jugador 2 juega al azar\n",
        "match2 =   axl.Match(players, turns =20)   # 20 veces\n",
        "match2.play()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F3AYOhVbQDkV",
        "outputId": "491194f0-e11e-4146-889d-1cbee86c400f"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(C, D),\n",
              " (D, C),\n",
              " (C, C),\n",
              " (C, D),\n",
              " (D, C),\n",
              " (C, D),\n",
              " (D, D),\n",
              " (D, C),\n",
              " (C, C),\n",
              " (C, C),\n",
              " (C, D),\n",
              " (D, D),\n",
              " (D, D),\n",
              " (D, C),\n",
              " (C, D),\n",
              " (D, D),\n",
              " (D, D),\n",
              " (D, D),\n",
              " (D, C),\n",
              " (C, C)]"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "match2.game"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jjPjEW2NQzol",
        "outputId": "0e68df01-2d36-4aa6-96f9-98fae0ec5eb4"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Axelrod game: (R,P,S,T) = (3, 1, 0, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "R: the Reward payoff (default value in the library: 3)\n",
        "\n",
        "P: the Punishment payoff (default value in the library: 1)\n",
        "\n",
        "S: the Sucker payoff (default value in the library: 0)\n",
        "\n",
        "T: the Temptation payoff (default value in the library: 5)"
      ],
      "metadata": {
        "id": "xVTrYZf0mN7a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "match2.scores()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rRAWsmQlQ3S_",
        "outputId": "69a7223b-fd41-4ca6-d26f-99fbaf1ccd8f"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(0, 5),\n",
              " (1, 1),\n",
              " (5, 0),\n",
              " (3, 3),\n",
              " (0, 5),\n",
              " (5, 0),\n",
              " (3, 3),\n",
              " (3, 3),\n",
              " (3, 3),\n",
              " (3, 3),\n",
              " (3, 3),\n",
              " (0, 5),\n",
              " (5, 0),\n",
              " (3, 3),\n",
              " (3, 3),\n",
              " (3, 3),\n",
              " (0, 5),\n",
              " (5, 0),\n",
              " (0, 5),\n",
              " (1, 1)]"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(match2.sparklines())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eC9BT-L0QicV",
        "outputId": "318bc4b3-4f7a-41c9-85db-038f5d87bfc6"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "█ ██ █  ███   █    █\n",
            " ██ █  ███   █    ██\n"
          ]
        }
      ]
    }
  ]
}