conviction/ABM - discounted integral v...

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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import random\n",
"\n",
"from scipy.stats import gamma\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Discounted integral 'Voting'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Simple model proposed for implementing and testing a voting scheme\n",
"\n",
"- Assume a dynamic supply of governance tokens accessed by a bonding ETH (linear bonding curve)\n",
"- Assume this tokens also represent a stake in a revenue generating process\n",
"- The revenue generating process is has one parameter which is \"governed\" \n",
"- The revenue generated is random and there is a true \"best parameter\" unknown to the voters which may change\n",
"- The goal of the 'voting' system is for the selected parameter to trend toward the \"best parameter\" (even if it changes)\n",
"- In this set up, voting is completely passive, \"votes\" are automatically determined by each agents belief state and counted according to their balance of the 'Tokens' that representing their voting capacity\n",
"- An agent has the right to change their belief or preference at any time but the effect of their prior beliefs or prefences continues to influence the system, decaying in time according to the forgetfulness parameter\n",
"- These tokens also represent their stake in the pool of Ether being generated by the revenue process\n",
"\n",
"This is a sensor fusion problem -- coordination problem. The environment, the pool of agents, the process, the actions and the system updates have been made mind-numbingly noisy in order to show the effect of the di"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"#Define the Revenue generating process\n",
"def revenue(true_best_param,current_voted_param ):\n",
" #use an concave function with unique maximum as true_best_param = current_voted_param\n",
" base_scale = 1\n",
" scale = base_scale*np.exp(-(true_best_param-current_voted_param)**2)\n",
" shape = .5\n",
" return gamma(scale, shape).rvs()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1a0e136550>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#test the revenue random variable as a function of how right the param is\n",
"N = 1000\n",
"bins = 10\n",
"d11=np.zeros(N)\n",
"d12=np.zeros(N)\n",
"d21=np.zeros(N)\n",
"d110=np.zeros(N)\n",
"d101=np.zeros(N)\n",
"for i in range(N):\n",
" d11[i] = revenue(1,1)\n",
" d12[i] = revenue(1,2)\n",
" d21[i] = revenue(2,1)\n",
" d110[i] = revenue(1,10)\n",
" d101[i] = revenue(10,1)\n",
"\n",
"plt.hist(d11, bins, alpha=0.5, label='d11')\n",
"plt.hist(d12, bins, alpha=0.5, label='d12')\n",
"plt.hist(d21, bins, alpha=0.5, label='d21')\n",
"plt.hist(d110, bins, alpha=0.5, label='d110')\n",
"plt.hist(d101, bins, alpha=0.5, label='d101')\n",
"\n",
"plt.legend(loc='upper right')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"#Lets assuming that all tokens in existence is given by T\n",
"\n",
"def bond_mint(eth, Eth, Tokens):\n",
" return eth*Tokens/Eth\n",
"\n",
"def burn_withdraw(tokens, Eth, Tokens):\n",
" return tokens*Eth/Tokens\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"#define a set of agents\n",
"agents = {}\n",
"\n",
"#in this passive voting model agents maintain a belief or a preference as part of their state with respect\n",
"#to the contract the 'token' came from\n",
"#their belief or preference may be updated by them at will\n",
"def add_agent(eth, tbparam, Eth, Tokens):\n",
" agents[str(len(agents))] = {\"eth\":eth,\n",
" \"tokens\":0,\n",
" \"param_belief\": 2*tbparam*np.random.uniform(),\n",
" \"value_belief\":2*Tokens/Eth*np.random.uniform()}\n",
" \n",
"\n",
"def agent_acts(a, Eth, Tokens, r, p):\n",
" #update value belief\n",
" agents[a][\"value_belief\"] = 2*Tokens/Eth*np.random.uniform()\n",
" \n",
" #update tokens held -- buy or sell -- pretty naive with random belief and random amount\n",
" if agents[a][\"value_belief\"]<Tokens/Eth:\n",
" urv =np.random.uniform() \n",
" burn = agents[a][\"tokens\"]*urv\n",
" eth_out = burn_withdraw(burn, Eth, Tokens)\n",
" \n",
" agents[a][\"tokens\"] = agents[a][\"tokens\"]-burn\n",
" agents[a][\"eth\"] = agents[a][\"eth\"] + eth_out\n",
" Tokens = Tokens - burn\n",
" Eth = Eth - eth_out\n",
" \n",
" else:\n",
" \n",
" urv =np.random.uniform() \n",
" bond =agents[a][\"eth\"]*urv\n",
" mint = bond_mint(bond, Eth, Tokens)\n",
" \n",
" agents[a][\"tokens\"] = agents[a][\"tokens\"]+mint\n",
" agents[a][\"eth\"] = agents[a][\"eth\"] -bond\n",
" Tokens = Tokens + mint\n",
" Eth = Eth + bond\n",
" \n",
" #update believe by taking a tiny step along slope of observations\n",
" #only assumption is that the agent roughly estimates the direction of better returns from observed revenue events\n",
" agents[a][\"param_belief\"] = np.max([0,agents[a][\"param_belief\"] + np.random.uniform()*np.polyfit(p, r, 1)[0]])\n",
"\n",
" #print(agents[a][\"value_belief\"])\n",
" return Eth, Tokens, agents[a]\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"K= 50000\n",
"alpha=.95 #forgetfulness\n",
"true_best_param = 5*np.ones(K) \n",
"#throw a step change in there to mix things up\n",
"#true_best_param[:int(K/2)] =.5*true_best_param[:int(K/2)]\n",
"\n",
"\n",
"E=np.zeros(K)\n",
"T=np.zeros(K)\n",
"\n",
"A=np.zeros(K)\n",
"\n",
"Eth =1\n",
"Tokens =100\n",
"E[0] = Eth\n",
"T[0] = Tokens\n",
"A[0] = 1\n",
"\n",
"agents = {}\n",
"add_agent(gamma(5,1).rvs(), true_best_param[0], Eth, Tokens)\n",
"\n",
"votes=np.zeros(K)\n",
"count=np.zeros(K)\n",
"param=np.zeros(K)\n",
"rev=np.zeros(K)\n",
"\n",
"n = np.zeros(K)\n",
"n[0]=1\n",
"\n",
"for k in range(1,K):\n",
" Eth = E[k-1]\n",
" Tokens = T[k-1]\n",
" #resolve governance -- what is the belief of the param\n",
" votes[k] = (np.sum([agents[a][\"tokens\"]*agents[a][\"param_belief\"] for a in list(agents.keys())])+ alpha*votes[k-1])\n",
" count[k] = (np.sum([agents[a][\"tokens\"] for a in list(agents.keys())])+ alpha*count[k-1])\n",
" if count[k]>0:\n",
" param[k] = votes[k]/count[k]\n",
" else:\n",
" param[k] = 1\n",
" rev[k] = revenue(true_best_param[k],param[k])\n",
" Eth = Eth+rev[k]\n",
" \n",
" #new agents join\n",
" if np.random.uniform()< np.log10(K/k):\n",
" new = int(2*np.random.uniform())\n",
" for i in range(new):\n",
" add_agent(gamma(5,1).rvs(), true_best_param[k], Eth, Tokens)\n",
" n[k]=len(agents)\n",
" \n",
" #pick some to update on the order of log of agents\n",
" active = random.sample(list(agents.keys()), int(np.log2(len(agents))))\n",
" for a in active:\n",
" Eth, Tokens, val = agent_acts(a, Eth, Tokens, rev[:k+1], param[:k+1])\n",
" agents[a] = val\n",
" \n",
" A[k] = len(active)\n",
" E[k] = Eth\n",
" T[k] = Tokens\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"49999"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"k"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x1a16fbb748>]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1a13c03eb8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(range(K),n)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(array([2.7333e+04, 1.6130e+04, 4.6390e+03, 1.3580e+03, 3.7600e+02,\n",
" 1.1900e+02, 3.2000e+01, 9.0000e+00, 3.0000e+00, 1.0000e+00]),\n",
" array([ 0. , 1.2107594 , 2.4215188 , 3.63227819, 4.84303759,\n",
" 6.05379699, 7.26455639, 8.47531578, 9.68607518, 10.89683458,\n",
" 12.10759398]),\n",
" <a list of 10 Patch objects>)"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1a13be3898>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.hist(rev)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(array([0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
" 0.00000000e+00, 0.00000000e+00, 1.66618863e-04, 1.11095012e-02,\n",
" 5.99890619e-01, 6.28179818e+00, 2.94059390e+00, 1.65366177e-01,\n",
" 2.15563329e-04, 2.27537984e-04, 7.02105404e-05, 2.80842162e-04,\n",
" 7.02105404e-05, 0.00000000e+00, 2.10631621e-04]),\n",
" array([-1.00000000e+00, -9.00000000e-01, -8.00000000e-01, -7.00000000e-01,\n",
" -6.00000000e-01, -5.00000000e-01, -4.00000000e-01, -3.00000000e-01,\n",
" -2.00000000e-01, -1.00000000e-01, -2.22044605e-16, 1.00000000e-01,\n",
" 2.00000000e-01, 3.00000000e-01, 4.00000000e-01, 5.00000000e-01,\n",
" 6.00000000e-01, 7.00000000e-01, 8.00000000e-01, 9.00000000e-01]),\n",
" <a list of 19 Patch objects>)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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D4gaAZChuAEiG4gaAZChuAEiG4gaAZChuAEiG4gaAZP4f/xkn19L6QDcAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1083741d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.hist((true_best_param-param)/true_best_param, bins=(np.arange(-1,1,.1)), density=True, alpha=.5)\n",
"plt.hist((true_best_param-param)/true_best_param, bins=(np.arange(-1,1,.1)), density=True, weights = rev, alpha=.25)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5,0,'Number of Agents')"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1a1af7d128>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.semilogy(n, np.abs(true_best_param - param)/true_best_param, n, .1*np.ones(K,))\n",
"plt.title(\"Logscale absolute percent error\")\n",
"#plt.labels([\"error\", \"10% reference\"])\n",
"plt.xlabel(\"Number of Agents\")"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5,0,'Number of Agents')"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1a1e061c50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.loglog(n, np.abs(true_best_param - param)/true_best_param, n, .1*np.ones(K,))\n",
"plt.title(\"Logscale absolute percent error\")\n",
"#plt.labels([\"error\", \"10% reference\"])\n",
"plt.xlabel(\"Number of Agents\")"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5,0,'Block')"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1a1e007550>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.loglog(range(K), np.abs(true_best_param - param)/true_best_param, range(K), .1*np.ones(K,))\n",
"plt.title(\"Logscale absolute percent error\")\n",
"#plt.labels([\"error\", \"10% reference\"])\n",
"plt.xlabel(\"Block\")"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x1a1a283860>,\n",
" <matplotlib.lines.Line2D at 0x1a1a2837b8>]"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1a1a2d2f28>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(range(K), true_best_param, range(K), param )"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x1a0e16c7b8>]"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1a17f053c8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(range(K), E/T)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x1a1c1ecd68>,\n",
" <matplotlib.lines.Line2D at 0x1a1c313550>]"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1a1b824588>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.loglog(range(K), E, range(K), T)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x1a1953e710>]"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1a17a1fc88>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(E, T)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x1a19263748>"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1a17c748d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(param,rev)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[4, 6, 0, 4]"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1a181e2630>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.kdeplot(param,rev)\n",
"plt.axis([4,6,0,4])"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.JointGrid at 0x1a18f80908>"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1a18f80828>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.jointplot(param, rev, kind=\"kde\", size=7, space=0, xlim=[4,6],ylim=[0,5])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"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": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}