Code for Zero Intelligence Traders in the Jungle. http://davidmasad.com/blog/zi-traders-in-the-jungle/

ZI_Jungle.ipynb

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   "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",
       "png": 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HdMcddxgdWsiQoIEwoBc3GqKuBBwocdudw+HQihUrNHjwYJWXlys/P1+PPPKI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\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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M0I033qjGxkZJ6jHhqqoqFRcXKyYmRikpKUpLS1NNTU2w0gMAAACA84TkHKSDBw9q//79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\n4sKdDqCFw/gAAAAASUVORK5CYII=\n",
       "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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\nAAAAAMDk/wEz9Jo+Vm7aqAAAAABJRU5ErkJggg==\n",
       "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": {}
  }
 ]
}

ZI_Model.py

'''
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()

hey , have you resolved the installing tensorflow on gpu issue?