{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Monte-Carlo Fictitious Play" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2021-02-15T09:49:41.377061Z", "start_time": "2021-02-15T09:49:36.640673Z" } }, "outputs": [], "source": [ "import poisson_approval as pa" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The goal of Monte-Carlo fictitious play is to perform fictitious play on several profiles drawn at random. First, define a random factory of profiles:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2021-02-15T09:49:41.389917Z", "start_time": "2021-02-15T09:49:41.380981Z" } }, "outputs": [], "source": [ "rand_profile= pa.RandProfileNoisyDiscreteUniform(\n", " types=[(ranking, 0.5) for ranking in pa.RANKINGS],\n", " noise=0.5\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here, we consider profiles of the class *ProfileNoisyDiscreteUniform*, and we specify that the possible types are all rankings, with a utility 0.5 for their middle candidate, and with a noise of 0.5: in other words, their utility for their middle candidate is uniformly drawn on the interval (0, 1)." ] }, { "cell_type": "markdown", "metadata": { "ExecuteTime": { "end_time": "2021-02-11T19:37:04.437624Z", "start_time": "2021-02-11T19:37:04.431087Z" } }, "source": [ "Launch Monte-Carlo fictitious play:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2021-02-15T09:50:31.148228Z", "start_time": "2021-02-15T09:49:41.394902Z" } }, "outputs": [], "source": [ "meta_results = pa.monte_carlo_fictitious_play(\n", " factory=rand_profile,\n", " n_samples=100,\n", " n_max_episodes=100,\n", " voting_rules=pa.VOTING_RULES,\n", " init='random_tau',\n", " monte_carlo_settings=[\n", " pa.MCS_BALLOT_STATISTICS,\n", " pa.MCS_CANDIDATE_WINNING_FREQUENCY,\n", " pa.MCS_CONVERGES,\n", " pa.MCS_DECREASING_SCORES,\n", " pa.MCS_N_EPISODES,\n", " pa.MCS_PROFILE,\n", " pa.MCS_TAU_INIT,\n", " pa.MCS_UTILITY_THRESHOLDS,\n", " pa.MCS_WELFARE_LOSSES\n", " ],\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "According to the options we entered, we use the factory *rand_profile* defined above, we draw *n_samples=100* profiles, fictitious play is performed with a maximum of *n_max_episodes=100* episodes, for all the voting rules of the package (Approval, Plurality, Anti-Plurality). For each profile, fictitious play is initialized with the option ``'random_tau'``, i.e. with a tau-vector drawn uniformly at random. The list *monte_carlo_settings* gives some additional options: each of them provides a \"bundle\" of statistics that will be computed during the process. For example, the option *MCS_CONVERGES* gives access to the two statistics ``'converges'`` and ``'mean_converges'``. Let us see their results for Approval." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2021-02-15T09:50:31.162190Z", "start_time": "2021-02-15T09:50:31.152217Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[False, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, False, True]\n" ] } ], "source": [ "print(meta_results[pa.APPROVAL]['converges'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For each profile drawn, the above list indicates whether fictitious play has converged or not." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2021-02-15T09:50:31.192147Z", "start_time": "2021-02-15T09:50:31.166179Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.96\n" ] } ], "source": [ "print(meta_results[pa.APPROVAL]['mean_converges'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The above number is the rate of convergence (over all profiles)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot the distribution (CDF) of scores for the winner, the challenger and the loser (here for Approval):" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2021-02-15T09:50:32.563377Z", "start_time": "2021-02-15T09:50:31.195140Z" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "pa.plot_distribution_scores(\n", " meta_results,\n", " voting_rule=pa.APPROVAL\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The gray areas represent 95% confidence intervals." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot the distribution (CDF) of the utility threshold (here for Approval):" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2021-02-15T09:50:34.386931Z", "start_time": "2021-02-15T09:50:32.567368Z" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "pa.plot_utility_thresholds(\n", " meta_results,\n", " voting_rule=pa.APPROVAL\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot the distribution (CDF) of the welfare loss (for all voting rules):" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2021-02-15T09:50:37.107692Z", "start_time": "2021-02-15T09:50:34.392910Z" } }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "pa.plot_welfare_losses(\n", " meta_results,\n", " criterion='utilitarian_welfare_losses'\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For more information, cf. the *Reference* section on Monte-Carlo fictitious play." ] } ], "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": "3.7.3" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": false, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 4 }