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Forked from dmasad/ZI_Jungle.ipynb
Created December 17, 2016 19:31
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Code for Zero Intelligence Traders in the Jungle. http://davidmasad.com/blog/zi-traders-in-the-jungle/
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{
"metadata": {
"name": ""
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"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"%matplotlib inline"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from __future__ import division\n",
"\n",
"from ZI_Model import Market\n",
"\n",
"# For plotting and analysis:\n",
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas\n",
"\n",
"matplotlib.rcParams['figure.figsize'] = [12, 6]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"market_size = 50 # Number of buyer, seller agents\n",
"max_value = 100 # Maximum agent valuation\n",
"max_strength = 100 # Maximum agent strength\n",
"max_iterations = 10000 # Iterations to equilibrium\n",
"\n",
"market = Market(max_value, max_strength, True, market_size, market_size, max_iterations) "
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"market_data = market.find_supply_demand()\n",
"q_est = market_data[\"q\"] # Estimated equilibrium quantity\n",
"p_est = market_data[\"p\"] # Estimated equilibrium price \n",
"\n",
"# Supply and demand curves; quantity implicit in the index.\n",
"supply = market_data[\"supply\"] \n",
"demand = market_data[\"demand\"]\n",
"\n",
"q_line = range(1, market_size+1)\n",
"\n",
"print \"Estimated quantity:\", q_est\n",
"print \"Estimated price:\", p_est\n",
"\n",
"fig = plt.figure(figsize=(6,6))\n",
"ax = fig.add_subplot(111)\n",
"plot = ax.step(q_line, supply, q_line, demand)\n",
"vline = ax.axvline(x=q_est, ymin=0, ymax=p_est/max_value, color='k', linestyle='--')\n",
"hline = ax.axhline(y=p_est, xmin=0, xmax=q_est/market_size, color='k', linestyle='--')\n",
"label = ax.set_xlabel(\"Quantity\", fontsize=14)\n",
"label = ax.set_ylabel(\"Price\", fontsize=14)\n",
"legend = ax.legend(plot, [\"Supply\", \"Demand\"], loc=(1.03, 0))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Estimated quantity: 26\n",
"Estimated price: 57.009779338\n"
]
},
{
"metadata": {},
"output_type": "display_data",
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AJkSCBgDAhEjQAACY0P8BSApRW4mAsrYAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x1094aa310>"
]
}
],
"prompt_number": 6
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Traditional (non-coercive) model"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Traditional ZI Traders model\n",
"\n",
"market.coercion = False\n",
"all_prices = []\n",
"all_quantities = []\n",
"all_surplus = []\n",
"\n",
"for i in range(100):\n",
" market.reset_market()\n",
" market.run_market()\n",
" prices = [sale[2] for sale in market.transactions]\n",
" all_quantities.append(len(prices))\n",
" all_prices += prices\n",
" all_surplus += market.get_surplus()\n",
" \n",
"fig = plt.figure(figsize=(14,6))\n",
"ax1 = fig.add_subplot(121)\n",
"hist = ax1.hist(all_prices, bins=range(0,105,5))\n",
"xlab = ax1.set_xlabel(\"Price\", fontsize=14)\n",
"ylab = ax1.set_ylabel(\"Count\", fontsize=14)\n",
"\n",
"ax2 = fig.add_subplot(122)\n",
"hist = ax2.hist(all_quantities, bins=range(20,40,1))\n",
"xlab = ax2.set_xlabel(\"Quantity traded per run\", fontsize=14)\n",
"ylab = ax2.set_ylabel(\"Count\", fontsize=14)\n",
"\n",
"print \"Mean price:\", np.mean(all_prices)\n",
"print \"Mean quantity:\", np.mean(all_quantities)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Mean price: 55.461523649\n",
"Mean quantity: 27.19\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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La6Vjxw67qR4AAOAKwQgAPKpSDf0Lbnk5W4sBAPA0r558AQAAAACsiGAEAAAA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\nAACwvf8HvmYZPUE5HDgAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x109600c50>"
]
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig = plt.figure(figsize=(14,6))\n",
"ax1 = fig.add_subplot(121)\n",
"hist = ax1.hist(all_prices, bins=range(0,105,5))\n",
"xlab = ax1.set_xlabel(\"Price\", fontsize=14)\n",
"ylab = ax1.set_ylabel(\"Count\", fontsize=14)\n",
"\n",
"ax2 = fig.add_subplot(122)\n",
"hist = ax2.hist(all_surplus, bins=range(0, 105, 5))\n",
"xlab = ax2.set_xlabel(\"Surplus\", fontsize=14)\n",
"ylab = ax2.set_ylabel(\"Count\", fontsize=14)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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YQHAEbZGGdevW6eGHH+7x+hEjRozQ9773PT355JO9/sMAAPiDOgUACAafDdK+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\nvY4HhAJT7IBe+LiQnD59Wtu3b9dzzz2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"text": [
"<matplotlib.figure.Figure at 0x109e3ad10>"
]
}
],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print \"Total surplus:\", sum(all_surplus)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Total surplus: 118627.346531\n"
]
}
],
"prompt_number": 10
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Coercive model"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"market.coercion = True\n",
"all_prices = []\n",
"all_quantities = []\n",
"all_surplus = []\n",
"\n",
"for i in range(100):\n",
" market.reset_market()\n",
" market.run_market()\n",
" prices = [sale[2] for sale in market.transactions]\n",
" all_quantities.append(len(prices))\n",
" all_prices += prices\n",
" all_surplus += market.get_surplus()\n",
" \n",
"fig = plt.figure(figsize=(14,6))\n",
"ax1 = fig.add_subplot(121)\n",
"hist = ax1.hist(all_prices, bins=range(0,105,5))\n",
"xlab = ax1.set_xlabel(\"Price\", fontsize=14)\n",
"ylab = ax1.set_ylabel(\"Count\", fontsize=14)\n",
"\n",
"ax2 = fig.add_subplot(122)\n",
"hist = ax2.hist(all_surplus, bins=range(-100, 105, 5))\n",
"xlab = ax2.set_xlabel(\"Surplus\", fontsize=14)\n",
"ylab = ax2.set_ylabel(\"Count\", fontsize=14)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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Fe7FHZreD/3HFhABcQAkAANpXr46FLaDj3HZNEuArrNZQ5wxVXVms1lBv/ygA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\n3rx5GjZsmHMWLklKTU1VXl6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"text": [
"<matplotlib.figure.Figure at 0x109e01090>"
]
}
],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig = plt.figure(figsize=(7,6))\n",
"ax1 = fig.add_subplot(111)\n",
"hist = ax1.hist(all_prices, bins=range(0,105,5))\n",
"xlab = ax1.set_xlabel(\"Price\", fontsize=14)\n",
"ylab = ax1.set_ylabel(\"Count\", fontsize=14)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x109e48e90>"
]
}
],
"prompt_number": 19
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig = plt.figure(figsize=(7,6))\n",
"ax2 = fig.add_subplot(111)\n",
"hist = ax2.hist(all_surplus, bins=range(-100, 105, 5))\n",
"xlab = ax2.set_xlabel(\"Surplus\", fontsize=14)\n",
"ylab = ax2.set_ylabel(\"Count\", fontsize=14)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x109e35d10>"
]
}
],
"prompt_number": 20
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"all_quantities = np.array(all_quantities)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"all_quantities[all_quantities<50]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 18,
"text": [
"array([49])"
]
}
],
"prompt_number": 18
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"np.mean(all_prices)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 16,
"text": [
"54.031060930989533"
]
}
],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print \"Total surplus:\", sum(all_surplus)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Total surplus: 5006.92947474\n"
]
}
],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"5006.92947474 / 118627.346531"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 13,
"text": [
"0.04220721124729513"
]
}
],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"all_surplus = np.array(all_surplus)\n",
"np.sum(all_surplus[all_surplus>0])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 14,
"text": [
"157676.06928695814"
]
}
],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"157676.06928695814/118627.346531"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 15,
"text": [
"1.329171341160816"
]
}
],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"len(all_surplus[all_surplus>0])/len(all_surplus)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 21,
"text": [
"0.6265253050610122"
]
}
],
"prompt_number": 21
}
],
"metadata": {}
}
]
}
'''
Jungle Equilibrium with Zero-Intelligence Traders
==================================================
Based on the Gode & Sunder (1993) paper on ZI traders, and Piccione and
Rubinstein (2007) on Jungle Equilibrium.
'''
from __future__ import division # Ensure that int/int isn't coerced to int
import random
class Seller(object):
'''
A seller agent.
Attributes:
max_cost: The maximum cost the seller may have.
cost: The seller's actual cost, drawn from a uniform distribution
s.t. 0 <= cost < max_cost
units: The number of units the seller has to sell; initialized at 1
sale_price: The price the agent sold at; set to None before a sale.
surplus: The difference between the sale price and the cost
max_strength: The maximum coercive strength that an agent may have
strength: The seller's actual strength, drawn from a uniform dist.
s.t. 0 <= strength <= max_strength
'''
def __init__(self, max_cost, max_strength):
'''
Instantiate a new Seller Agent.
Args:
max_cost: The maximum allowed cost the agent may have
'''
self.max_cost = max_cost
self.cost = random.random() * max_cost
self.strength = random.random() * max_strength
self.units = 1
self.sale_price = None # Hold the price the unit is finally sold at
self.surplus = None
def ask_price(self):
'''
Form the asking price in a negotiation.
Returns:
An asking price for the unit for sale, drawn from a uniform
distribution s.t.:
cost <= asking_price < max_cost
'''
profit = random.random() * (self.max_cost - self.cost)
return self.cost + profit
def make_trade(self, sale_price):
'''
Close the sale: set remaining units to 0, and record the sale price.
Args:
sale_price: The price the unit was sold at.
'''
self.units = 0
self.sale_price = sale_price
self.surplus = self.sale_price - self.cost
def reset(self):
'''
Return the seller agent to its initial condition.
'''
self.units = 1
self.sale_price = None
self.surplus = None
class Buyer:
'''
A Buyer agent.
Attributes:
max_value: The maximum value the buyer may have.
value: The buyer's actual cost, drawn from a uniform distribution
s.t. 0 <= value < max_value
units: The number of units the buyer has; initialized at 0.
sale_price: The price the agent bought at; set to None before a sale.
surplus: The difference between the valuation and sale price.
max_strength: The maximum coercive strength that an agent may have
strength: The seller's actual strength, drawn from a uniform dist.
s.t. 0 <= strength <= max_strength
'''
def __init__(self, max_value, max_strength):
'''
Instantiate a new Buyer Agent.
Args:
max_value: The maximum allowed value to an agent.
'''
self.max_value = max_value
self.value = random.random() * max_value
self.strength = random.random() * max_strength
self.units = 0
self.sale_price = None # Hold the price the unit is finally bought at.
self.surplus = None
def bid_price(self):
'''
Form a bid price in a negotiation.
Returns:
A bid price drawn from a uniform distribution s.t.:
0 <= bid_price < value
'''
return random.random() * self.value
def make_trade(self, buy_price):
'''
Close the sale: set units to 1, and record the sale price.
Args:
buy_price: The price the unit was bought at.
'''
self.units = 1
self.sale_price = buy_price
self.surplus = self.value - self.sale_price
def reset(self):
'''
Returns the buyer to its initial state.
'''
self.units = 0
self.sale_price = None
self.surplus = None
class Market:
'''
A simulated 'jungle' market between a set of Buyer and Seller agents.
A given Market instance holds a population of agents set at initializations,
from which multiple trading processes may be realized via different random
pairings of agents.
Attributes:
max_value: The maximum value both buyers and sellers will place on
their goods.
num_buyers: The number of buyer agents in the market.
num_sellers: The number of seller agents in the market.
max_rounds_wo_trade: The number of pairings NOT resulting in a trade
before the market is considered in equilibrium
(i.e. no more trades are possible).
transactions: A list of tuples recording successful transactions, of
the form: (seller_id, buyer_id, transaction_price)
'''
def __init__(self, max_value, max_strength, coercion,
num_buyers, num_sellers, max_rounds_wo_trade):
'''
Initialize a new market.
Args:
max_value: The maximum value both buyers and sellers will place on
their goods.
max_strength: The maximum coercive strength that both buyers and
sellers may have.
num_buyers: The number of buyer agents in the market.
num_sellers: The number of seller agents in the market.
max_rounds_wo_trade: The number of pairings NOT resulting in a trade
before the market is considered in equilibrium
(i.e. no more trades are possible).
'''
# Set market constants:
self.max_value = max_value
self.max_strength = max_strength
self.coercion = coercion
self.num_buyers = num_buyers
self.num_sellers = num_sellers
self.max_rounds_wo_trade = max_rounds_wo_trade
# Create agents:
self.buyers = [Buyer(self.max_value, self.max_strength)
for i in range(self.num_buyers)]
self.sellers = [Seller(self.max_value, self.max_strength)
for i in range(self.num_sellers)]
# Initiate market counters:
self.rounds_wo_trade = 0 # Counter of rounds where no trade occured.
self.transactions = []
def reset_market(self):
'''
Resets the market back to its starting state.
Does not change the agents' valuations.
'''
self.rounds_wo_trade = 0
self.transactions = []
for agent in self.buyers + self.sellers:
agent.reset()
def find_supply_demand(self):
'''
Find the (non-coercive) supply/demand curves, and their intersection.
Returns:
A dictionary containing the summary of the market, as follows:
{
"supply": An ordered asceding list of seller costs,
"demand": An ordered descending list of buyer values,
"p": The estimated mean price, where the supply and demand
curves meet,
"q": The estimated number of trades, where the supply and demand
curves meet.
}
If the supply and demand curves do not cross, p and q are set to
None.
'''
# The supply and demand curves:
supply = [seller.cost for seller in self.sellers]
demand = [buyer.value for buyer in self.buyers]
supply.sort()
demand.sort(reverse=True)
# Estimate their intersection
market_size = min(self.num_buyers, self.num_sellers)
for i in range(market_size):
if supply[i] == demand[i]:
est_trades = i + 1 # From index to count
est_price = supply[i]
break
if demand[i] <= supply[i] and demand[i-1] > supply[i-1]:
est_trades = i+1
# Compute the middle of the range in overlap:
max_price = min(supply[i], demand[i-1])
min_price = max(supply[i-1], demand[i])
est_price = (min_price + max_price)/2
break
else: # If no intersection found, leave the values undefined.
est_trades = None
est_price = None
# Build the return dictionary:
market_summary = {
"supply": supply,
"demand": demand,
"p": est_price,
"q": est_trades
}
return market_summary
def try_trade(self, coercion=False):
'''
A single tick of the market.
Match up a random buyer and seller and see if a trade occurs.
Args:
coercion: (default=False) Whether trades occurs by coercion or
mutual benefit
Returns
None if no trade occurs
Otherwise, a transaction tuple
(seller_id, buyer_id, transaction_price)
'''
# Choose a seller and a buyer
seller_id = random.randint(0, self.num_sellers-1)
buyer_id = random.randint(0, self.num_buyers-1)
buyer = self.buyers[buyer_id]
seller = self.sellers[seller_id]
# If the buyer or seller are out of the market, no trade:
if buyer.units == 1 or seller.units == 0:
return None
if coercion:
# The stronger agent determines the trade price:
if seller.strength > buyer.strength:
transaction_price = seller.ask_price()
else:
transaction_price = buyer.bid_price()
else:
# Without coercion, the trade occurs only if mutually beneficial:
ask_price = seller.ask_price()
bid_price = buyer.bid_price()
if ask_price > bid_price: return None
transaction_price = ask_price + random.random() * (bid_price - ask_price)
# The coerced trade goes forward:
buyer.make_trade(transaction_price)
seller.make_trade(transaction_price)
return (seller_id, buyer_id, transaction_price)
def run_market(self):
'''
Run the market to equilibrium and populate the transactions list.
'''
while self.rounds_wo_trade < self.max_rounds_wo_trade:
transaction = self.try_trade(self.coercion)
if transaction is None:
self.rounds_wo_trade += 1
else:
self.transactions.append(transaction)
self.rounds_wo_trade = 0
def get_surplus(self):
'''
Return a list with the buyer and seller surpluses after trading.
'''
surpluses = []
for agent in self.buyers + self.sellers:
if agent.surplus is not None:
surpluses.append(agent.surplus)
return surpluses
if __name__ == "__main__":
market = Market(30, 100, True, 50, 50, 1000)
market.run_market()
market = Market(30, 100, False, 50, 50, 1000)
market.run_market()
@1994anne
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hey , have you resolved the installing tensorflow on gpu issue?

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