From dbd00c85818d9f90e16f67909788585bb387d8aa Mon Sep 17 00:00:00 2001 From: Markus Date: Fri, 28 Dec 2018 01:04:23 -0200 Subject: [PATCH] first pass at verifier's dilemma should refactor to be cleaner and have more comments, like simple_tracker --- demos/verifiers_dilemma.ipynb | 823 ++++++++++++++++++++++++++++++++++ 1 file changed, 823 insertions(+) create mode 100644 demos/verifiers_dilemma.ipynb diff --git a/demos/verifiers_dilemma.ipynb b/demos/verifiers_dilemma.ipynb new file mode 100644 index 0000000..1bc4dfd --- /dev/null +++ b/demos/verifiers_dilemma.ipynb @@ -0,0 +1,823 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Configuration" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from datetime import timedelta\n", + "\n", + "from SimCAD import configs\n", + "from SimCAD.configuration import Configuration\n", + "from SimCAD.configuration.utils import exo_update_per_ts, proc_trigger, bound_norm_random, \\\n", + " ep_time_step\n", + "\n", + "seed = {\n", + "}\n", + "\n", + "# Genesis States\n", + "genesis_states = {\n", + " 'Verifiers_On': True,\n", + " 'Cheaters_On': False,\n", + " 'Total_Volume': 100,\n", + " 'Honest_Volume': 100,\n", + " 'Cheats_Volume': 0,\n", + " 'Cheats_Caught_Volume': 0,\n", + " 'Verifiers_Cost': 0,\n", + " 'Verifiers_Reward': 0,\n", + " 'Cheaters_Cost': 0,\n", + " 'Cheater_Reward': 0,\n", + " 'timestamp': '2018-01-01 00:00:00'\n", + "}\n", + "\n", + "# Verifier's cost per transaction verified\n", + "alfa = 0.001\n", + "def verifier_cost(s):\n", + " return alfa * (s['Total_Volume'])\n", + "\n", + "# Verifier's reward per cheat caught\n", + "beta = 10 \n", + "def verifier_reward(s):\n", + " return beta * s['Cheats_Volume']\n", + "\n", + "def verifier_expected_reward(s):\n", + " '''\n", + " We assume the existence of an off-chain signaling mechanism \n", + " by which potential verifiers become aware of some of cheating volume.\n", + " '''\n", + " off_chain_cheating_signal = 1\n", + " off_chain_expected_cheating = off_chain_cheating_signal * s['Cheats_Volume']\n", + " on_chain_expected_cheating = s['Cheats_Caught_Volume']\n", + " return beta * max([off_chain_expected_cheating, on_chain_expected_cheating])\n", + "\n", + "# Cheater's reward per transaction sent\n", + "gamma = 1\n", + "def cheater_reward(s):\n", + " return gamma * (s['Cheats_Volume'])\n", + "\n", + "# Cheater's cost per cheat caught\n", + "delta = 10\n", + "def cheater_cost(s):\n", + " return delta * s['Cheats_Caught_Volume']\n", + "\n", + "\n", + "\n", + "# Behaviors\n", + "def verifier(step, sL, s):\n", + " act = False\n", + " if (verifier_expected_reward(s) > verifier_cost(s)):\n", + " act = True\n", + " return {'verifier': act}\n", + "\n", + "def cheater(step, sL, s):\n", + " act = not(s['Verifiers_On'])\n", + " return {'cheater': act}\n", + "\n", + "# Mechanisms\n", + "def commit_resources_to_verifying(step, sL, s, _input):\n", + " y = 'Verifiers_On'\n", + " x = _input['verifier']\n", + " return (y, x)\n", + "\n", + "def commit_resources_to_cheating(step, sL, s, _input):\n", + " y = 'Cheaters_On'\n", + " x = _input['cheater']\n", + " return (y, x)\n", + "\n", + "mechanisms = {\n", + " 'commit': {\n", + " 'behaviors': {\n", + " 'verifier': verifier,\n", + " 'cheater': cheater\n", + " },\n", + " 'states': { \n", + " 'Verifiers_On': commit_resources_to_verifying,\n", + " 'Cheaters_On': commit_resources_to_cheating \n", + " }\n", + " }\n", + "}\n", + "\n", + "# Environmental Processes\n", + "epsilon = 1\n", + "def volume_ep(step, sL, s, _input):\n", + " y = 'Total_Volume'\n", + " x = epsilon*s['Total_Volume']\n", + " return (y, x)\n", + "\n", + "zeta=0.2\n", + "def cheat_volume_ep(step, sL, s, _input):\n", + " y = 'Cheats_Volume'\n", + " if (s['Cheaters_On']):\n", + " x = zeta*(s['Total_Volume'])\n", + " else:\n", + " x = 0\n", + " return (y, x)\n", + "\n", + "def honest_volume_ep(step, sL, s, _input):\n", + " y = 'Honest_Volume'\n", + " if (s['Cheaters_On']):\n", + " x = (1-zeta)*s['Total_Volume']\n", + " else:\n", + " x = s['Total_Volume']\n", + " return (y, x)\n", + "\n", + "def cheats_caught_ep(step, sL, s, _input):\n", + " y = 'Cheats_Caught_Volume'\n", + " if (s['Verifiers_On']):\n", + " x = s['Cheats_Volume']\n", + " else:\n", + " x = 0\n", + " return (y, x)\n", + "\n", + "def verifier_cost_ep(step, sL, s, _input):\n", + " y = 'Verifiers_Cost'\n", + " if (s['Verifiers_On']):\n", + " x = verifier_cost(s)\n", + " else:\n", + " x = 0\n", + " return (y, x)\n", + "\n", + "def verifier_reward_ep(step, sL, s, _input):\n", + " y = 'Verifiers_Reward'\n", + " if (s['Verifiers_On']):\n", + " x = verifier_reward(s)\n", + " else:\n", + " x = 0\n", + " return (y, x)\n", + "\n", + "def cheater_cost_ep(step, sL, s, _input):\n", + " y = 'Cheaters_Cost'\n", + " if (s['Verifiers_On']):\n", + " x = cheater_cost(s)\n", + " else:\n", + " x = 0\n", + " return (y, x)\n", + "\n", + "def cheater_reward_ep(step, sL, s, _input):\n", + " y = 'Cheater_Reward'\n", + " if (s['Cheaters_On']):\n", + " x = cheater_reward(s)\n", + " else:\n", + " x = 0\n", + " return (y, x)\n", + "\n", + "ts_format = '%Y-%m-%d %H:%M:%S'\n", + "t_delta = timedelta(days=0, minutes=0, seconds=1)\n", + "def time_model(step, sL, s, _input):\n", + " y = 'timestamp'\n", + " x = ep_time_step(s, dt_str=s['timestamp'], fromat_str=ts_format, _timedelta=t_delta)\n", + " return (y, x)\n", + "\n", + "\n", + "\n", + "# remove `exo_update_per_ts` to update every ts\n", + "exogenous_states = exo_update_per_ts(\n", + " {\n", + " 'Total_Volume': volume_ep,\n", + " 'Honest_Volume': honest_volume_ep,\n", + " 'Cheats_Volume': cheat_volume_ep,\n", + " 'Cheats_Caught_Volume': cheats_caught_ep,\n", + " 'Verifiers_Cost': verifier_cost_ep,\n", + " 'Verifiers_Reward': verifier_reward_ep,\n", + " 'Cheaters_Cost': cheater_cost_ep,\n", + " 'Cheater_Reward': cheater_reward_ep,\n", + " 'timestamp': time_model\n", + " }\n", + ")\n", + "\n", + "env_processes = {\n", + "}\n", + "\n", + "\n", + "\n", + "sim_config = {\n", + " 'N': 1,\n", + " 'T': range(100)\n", + "}\n", + "\n", + "configs.append(\n", + " Configuration(\n", + " sim_config=sim_config,\n", + " state_dict=genesis_states,\n", + " seed=seed,\n", + " exogenous_states=exogenous_states,\n", + " env_processes=env_processes,\n", + " mechanisms=mechanisms\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Run the engine" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "single_proc: []\n" + ] + } + ], + "source": [ + "from SimCAD.engine import ExecutionMode, ExecutionContext, Executor\n", + "# from demos import simple_tracker_config\n", + "from SimCAD import configs\n", + "exec_mode = ExecutionMode()\n", + "\n", + "single_config = [configs[0]]\n", + "single_proc_ctx = ExecutionContext(exec_mode.single_proc)\n", + "run = Executor(single_proc_ctx, single_config)\n", + "run_raw_result = run.main()[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Analyze the results" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "import pandas as pd\n", + "from tabulate import tabulate\n", + "result = pd.DataFrame(run_raw_result)\n", + "result.plot('timestamp', \n", + " ['Total_Volume',\n", + " 'Honest_Volume',\n", + " 'Cheats_Volume',\n", + " 'Cheats_Caught_Volume'])" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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Cheater_RewardCheaters_CostCheaters_OnCheats_Caught_VolumeCheats_VolumeHonest_VolumeTotal_VolumeVerifiers_CostVerifiers_OnVerifiers_Rewardmech_stepruntime_steptimestamp
00.00.0False0.00.0100.01000.0True0.00102018-01-01 00:00:00
10.00.000.00.0100.01000.100.01112018-01-01 00:00:01
20.00.010.00.0100.01000.000.01122018-01-01 00:00:02
30.00.010.020.080.01000.000.01132018-01-01 00:00:03
420.00.010.020.080.01000.010.01142018-01-01 00:00:04
520.00.0020.020.080.01000.11200.01152018-01-01 00:00:05
60.0200.0020.00.0100.01000.11200.01162018-01-01 00:00:06
70.0200.000.00.0100.01000.110.01172018-01-01 00:00:07
80.00.000.00.0100.01000.100.01182018-01-01 00:00:08
90.00.010.00.0100.01000.000.01192018-01-01 00:00:09
100.00.010.020.080.01000.000.011102018-01-01 00:00:10
1120.00.010.020.080.01000.010.011112018-01-01 00:00:11
1220.00.0020.020.080.01000.11200.011122018-01-01 00:00:12
130.0200.0020.00.0100.01000.11200.011132018-01-01 00:00:13
140.0200.000.00.0100.01000.110.011142018-01-01 00:00:14
150.00.000.00.0100.01000.100.011152018-01-01 00:00:15
160.00.010.00.0100.01000.000.011162018-01-01 00:00:16
170.00.010.020.080.01000.000.011172018-01-01 00:00:17
1820.00.010.020.080.01000.010.011182018-01-01 00:00:18
1920.00.0020.020.080.01000.11200.011192018-01-01 00:00:19
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