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IPython notebook for a simulation of a simple barter economy
{
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Simple barter makes everybody happier -- i.e., creates wealth"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import random\n",
"import copy\n",
"\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 50
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A Human is someone with an ordinal scale of preferences (like `['orange', 'banana', 'fish', 'coconut', 'strawberry']`) and some random quantity of each of these goods."
]
},
{
"cell_type": "code",
"collapsed": true,
"input": [
"first_order_goods = [\n",
" 'orange',\n",
" 'banana',\n",
" 'fish',\n",
" 'coconut',\n",
" 'strawberry',\n",
" 'tomato',\n",
" 'ice cream',\n",
" 'potato'\n",
"]\n",
"\n",
"class Human(object):\n",
" def __init__(self):\n",
" self.set_scale()\n",
" self.set_things()\n",
"\n",
" def set_scale(self):\n",
" scale = copy.deepcopy(first_order_goods)\n",
" random.shuffle(scale)\n",
" self.scale = scale\n",
"\n",
" def set_things(self):\n",
" self.things = {good: 0 for good in first_order_goods}\n",
" for i in range(len(self.things)):\n",
" good = random.choice(self.scale)\n",
" self.things[good] += 1\n",
"\n",
" def simple_satisfaction(self):\n",
" total = 0\n",
" for good, quantity in self.things.items():\n",
" value = len(self.scale) - self.scale.index(good)\n",
" total += quantity * value\n",
" return total\n",
"\n",
"def total_satisfaction():\n",
" return sum([h.simple_satisfaction() for h in population])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 51
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"population = [Human() for i in range(50)]\n",
"plt.bar(list(range(len(population))), [h.simple_satisfaction() for h in population])\n",
"print 'total satisfaction: %s' % total_satisfaction()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"total satisfaction: 1818\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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2bNhgampqTEVFhamtrTWvvvqq+f33300wGDRLly41nueZ4eHhpMdKMpI556EZtue6T3nu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\nlvt/gM0vi3aEu4MAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0xb61514c>"
]
}
],
"prompt_number": 52
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A transaction is *an encounter between two individuals*, one of them (**Jack**) offers one good he has for goods the other individual (**Rose**) has and he values more. If Rose values the good Jack offered more than anyone he wants, she will choose, from these, the one she likes less and trade with him.\n",
"\n",
"This process repeats itself for all goods Jack has, some of which will not be traded because Rose can't give anything Jack wants for them. After this, Jack and Rose move apart and the Titanic doesn't crash."
]
},
{
"cell_type": "code",
"collapsed": true,
"input": [
"def encounter():\n",
" jack, rose = random.sample(population, 2)\n",
" for jack_good in jack.things:\n",
" # jack can't trade what he doesn't have\n",
" if jack.things[jack_good] == 0:\n",
" continue\n",
"\n",
" better_goods_for_jack = jack.scale[:jack.scale.index(jack_good)]\n",
" rose_offers = {}\n",
" for good in better_goods_for_jack:\n",
" # rose can't trade what she doesn't have and\n",
" # rose won't trade what she values more for what she values less\n",
" if rose.things[good] > 0 and \\\n",
" rose.scale.index(good) > rose.scale.index(jack_good):\n",
" rose_offers[good] = rose.scale.index(good)\n",
"\n",
" if rose_offers:\n",
" # get the worse good in rose scale which can be traded\n",
" rose_good = min(rose_offers, key=rose_offers.get)\n",
"\n",
" # make the trade\n",
" jack.things[jack_good] -= 1\n",
" jack.things[rose_good] += 1\n",
" rose.things[jack_good] += 1\n",
" rose.things[rose_good] -= 1"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 53
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"The simulation is some quantity (150) of sequential encounters of individuals who act like Jack and Rose and trade between themselves. The state of everybody's satisfaction is before each of these encounters."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def simulate():\n",
" states = []\n",
" everybody = []\n",
" for i in range(150):\n",
" states.append(total_satisfaction())\n",
" everybody.append([h.simple_satisfaction() for h in population])\n",
" encounter()\n",
" return everybody, states"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 54
},
{
"cell_type": "code",
"collapsed": true,
"input": [
"humans, states = simulate()\n",
"\n",
"print 'satisfaction of each member of the population'\n",
"plt.plot(humans)\n",
"plt.show()\n",
"\n",
"print 'total satisfaction'\n",
"plt.plot(states)\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"satisfaction of each member of the population\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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ua0MFLO3VBrDFaOTZPvyYOZ3NGAzvhdW3ufktiorujeoRz38d/BdNXf7TUK88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"text": [
"<matplotlib.figure.Figure at 0xaf3d62c>"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"total satisfaction\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0xbd9c0ec>"
]
}
],
"prompt_number": 55
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the end, nobody produced a thing, but everybody is happier by only making voluntary transactions."
]
}
],
"metadata": {}
}
]
}
@jisantuc

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commented Feb 20, 2015

Have you considered extending this basic sim to include notions of search costs/transactions costs/asymmetric information (humans who know others' preferences vs. humans who don't, for example)? Adding intermediaries?

I might play around with this some.

@fiatjaf

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commented Mar 16, 2015

How did you find this old gist, @jisantuc? I have considered, and tried to write, a simulation in this same lines, but considering transactions, capital and production. Until now I couldn't finish the work, because it is really complicated, but someday I will.

What do you got? I wanna see it.

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