{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Games\n", "### EC297\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Nash Equilibrium \n", "\n", "A set of strategies for all players that are all _best response_. \n", "\n", "The idea is that such set of strategies is stable as noboy has a reason to\n", "unilaterally change\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Do people play NE?\n", "\n", "1. Epistemic -> people reason their way to equilibrium.\n", "\n", "1. Evolution -> Groups of people mutually learn their way to\n", "equilibrium.\n", "\n", "*We are going to analyze these ideas in detail*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Methods\n", "\n", "Experimental Econ. test game theory in the lab using several distinct\n", "timing and matching protocols.\n", "\n", "- **One shot:** Ss play game once, get paid. \n", "\n", " * Probably, useful for reasoning.\n", "\n", "\n", "- **Random matching** _or strangers matching_: Ss play a number of rounds\n", "but each round are randomly and anonymously: matched with someone\n", "new.\n", "\n", " * Learning\n", "\n", "\n", "- **Repeated rematching** or _partners matching_: Ss are matched with the\n", "some counterparts for a number of rounds. \n", "\n", " * Reputations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# PD Game\n", "\n", "- Two players (row and column) meet and pick either C or D.\n", "\n", "- Thus, the action set is {C,D}\n", "\n", "\n", "\n", "| | C | D |\n", "|-----|:-------:|:-------:|\n", "| C |10,10| 5,20|\n", "| D |20,5| 8,8|\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# PD Game\n", "\n", "- The equilibrium is (D,D)!\n", "\n", "\n", "| | C | D |\n", "|-----|:-------:|:-------:|\n", "| C |10,10| 5,__20__|\n", "| D |__20__,5| __8__,__8__|\n", "\n", "- Note that they can improve their payoffs by playing (C,C).\n", "\n", "- The 20 is usually called *the temptation payoff*, and 10 is the\n", "reward payoff.\n", "\n", "**Experimental Results: **\n", "\n", " - Modest cooperation (C) in one shot games with low-temptation and high reward.\n", "\n", " - Modest initial cooperation and decay in strangers.\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Finitely Repeated PD\n", "\n", "- Think about backward induction (aka BI): NE should be the same.\n", "\n", "- But, data shows early cooperation and eventual defection.\n", "\n", "- Why?\n", "\n", " * **Reputation** Suppose that there is small prob. that your\n", " counterparty cooperates until defection, then it can be rational\n", " \n", " to _pretend_ to be this type of player in early rounds and defect later.\n", " * $\\epsilon$ **-equilibrium:** When the rewards of C are high, Ss are\n", " willing to accept minor deviations from BR, causing them to\n", " cooperate. Note that benefits of cooperation get higher, the\n", " larger the game is repeated!\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### $\\infty$ Repeated PD\n", "\n", "| | C | D |\n", "|-----|:-------:|:-------:|\n", "| C |2,2| 0,3|\n", "| D |3,0| 1,1|\n", "\n", "- We analyze these games with _trigger strategies_\n", "\n", "- Cooperation\n", "\n", "$$\n", "2 + 2\\delta + 2 \\delta^2 + \\dots = \\frac{2}{1-\\delta}\n", "$$\n", "\n", "- Defect\n", "\n", "$$\n", "3 + \\delta + \\delta^2 + \\dots = 3+\\frac{\\delta}{1-\\delta}\n", "$$\n", "\n", "- Thus, cooperate if\n", "\n", "$$\n", "\\frac{2}{1-\\delta} \\geq 3+\\frac{\\delta}{1-\\delta} \\quad \\text{or}\n", "\\quad \\delta\\geq 1/2\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### $\\infty$ Repeated PD\n", "\n", "- The (standard) theory predicts cooperation if the discount factor is big enough.\n", "\n", "- But, how can we run these type of games?\n", "\n", " - Assume that I can kill you if one appears after rolling a dice. In\n", " this case, the prob. of survival is 5/6. (due to Roth and Murrigham, 78)\n", "\n", "- C doesn't emerge when discount rates are too low for it to emerge in standard theory (Dalbo and Frechette, 2011).\n", "\n", "- Higher $\\delta$, we observe higher C (Dalbo, 2005)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Matching pennies\n", "| | R | L |\n", "|-----|:-------:|:-------:|\n", "| U |10,5| 5,10|\n", "| D |5,10| 10,5|\n", "\n", "In this case, we cannot find a Pure Nash Equilibrium. Let's try to figure out if other type of equilibrium exists\n", "\n", "let's call $q$ the prob. of playing R and $p$ the probability of playing p. \n", "\n", "Therefore, the payoffs for playing U and D are\n", "\n", "$$\n", "\\pi^U = 10\\times q + 5\\times (1-q)\n", "$$\n", "\n", "$$\n", "\\pi^D = 5\\times q + 10\\times (1-q)\n", "$$\n", "\n", "Let's substract one from the other, \n", "\n", "$$\n", "\\Delta \\pi = \\pi^U-\\pi^D = 5q - 5(1-q) = 10q-5\n", "$$\n", "\n", "Now, \n", "\n", "If Player 2 plays $q=1$ or R, then $\\Delta \\pi>0$ or $\\pi^U>\\pi^D$. Thus, Player 1 picks $p=1$ or U \n", "\n", "If Player 2 plays $q=0$ or L, then $\\Delta \\pi<0$ or $\\pi^U<\\pi^D$. Thus, Player 1 picks $p=1$ or D \n", "\n", "Note that we can say that Player 1 is indifferent between U and D when $\\pi^D=\\pi^L$. This is true when $q=0$\n", "\n", "Perhaps, a graph it is more useful to represent the different responses: \n", "\n", "\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "### Code to draw a Best Response Function\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from __future__ import print_function\n", "from ipywidgets import interact, interactive, fixed, interact_manual\n", "import ipywidgets as widgets\n", "%matplotlib inline \n", "q = np.arange(0,1.01,.01) #The prob. of playing P(right)\n", "\n", "def plotbr(a,b,c,d,no):\n", " ## a,b,c,d, the payoff matrix\n", " piup = a*(q)+b*(1-q)\n", " pido = c*(q)+d*(1-q)\n", " pile = b*(q)+d*(1-q)\n", " piri = d*q + c*(1-q)\n", " dpi = piup-pido\n", " d2pi = pile-piri\n", " lp = np.copy(dpi)\n", " lq =np.copy(d2pi)\n", " lp[dpi>0]=1\n", " lp[dpi<0]=0\n", " lq[d2pi>0]=1\n", " lq[d2pi<0]=0\n", " if no>0:\n", " plt.title('Figure 1: The best response of Player 1')\n", " plt.plot(q,lp,linewidth=5)\n", " else:\n", " plt.title('Figure 2: The best response of Player 1 and 2')\n", " plt.plot(q,lp,lq,q,linewidth=5)\n", " plt.xlabel('P(right)')\n", " plt.ylabel('P(up)')\n", " plt.ylim(-0.1,1.1)\n", " plt.xlim(-0.1,1.1)\n", " plt.show()\n", " \n", "plotbr(10,5,5,10,1)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Figure 1 shows the best choice (aka **best response**) of players 1. Using similar ideas, we can do the graph for both players\n", "\n" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nFMzMrMFJwczMGv4XIP32nWcYIaAAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plotbr(10,5,5,10,-1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From Figure 2, we realize that both best responses intersect at 0.5. Both players are doing the best when they pick $(p,q)=(.5,5)$. \n", "In this case, they are mixing between both choices. We will say that this game have a unique mixed NE. " ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.core.display import HTML\n", "def css_styling():\n", " import os\n", " styles = open(os.path.expanduser(\"custom.css\"), \"r\").read()\n", " return HTML(styles)\n", "css_styling()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 1 }