def simulate(c_switch):
data = []
in_house = 1
third_party = in_house * 0.80
switching = in_house * c_switch
weights = np.linspace(0.1, 1, 100)
for weight in weights:
g = Graph()
g.from_dict({
1: {'payoff': 0, 'after': []},
2: {'payoff': 0, 'after': [{'node_id': 1, 'cost': in_house}]},
3: {'payoff': 0, 'after': [{'node_id': 2, 'cost': in_house}]},
4: {'payoff': 0, 'after': [{'node_id': 1, 'cost': third_party}]},
5: {'payoff': 0, 'after': [{'node_id': 4, 'cost': third_party}]},
6: {'payoff': 0, 'after': [{'node_id': 4, 'cost': switching, 'weight': weight}]},
7: {'payoff': 0, 'after': [{'node_id': 6, 'cost': in_house}, {'node_id': 5, 'cost': 2 * switching, 'weight': weight}]},
8: {'payoff': 0, 'after': [{'node_id': 3, 'cost': in_house}]},
9: {'payoff': 0, 'after': [{'node_id': 5, 'cost': third_party}]},
10: {'payoff': 0, 'after': [{'node_id': 7, 'cost': in_house}, {'node_id': 5, 'cost': 3 * switching, 'weight': weight}]},
11: {'payoff': 0, 'after': [{'node_id': 8, 'cost': 0}]},
12: {'payoff': 0, 'after': [{'node_id': 9, 'cost': 0}]},
13: {'payoff': 0, 'after': [{'node_id': 10, 'cost': 0}]},
})
options = g.get_options(iters=1000)
data.append([weight / (1.0 + weight), options[2] - options[4]])
return data
def build_graph(node_id=None):
"""
returns a list of petersburg graph objects with a certain object perturbed
"""
# weights
w1, w2, w3, w4, w5, w6 = 10, 10, 10, 10, 10, 10
w7 = random.randint(1, 10)
w8 = random.randint(1, 10)
w9 = random.randint(1, 10)
w10 = random.randint(1, 10)
w11 =random.randint(1, 10)
# costs
c1, c2, c3, c4, c5, c6 = 0, 0, 0, 0, 0, 0
c7 = random.randint(1, 10)
c8 = random.randint(1, 10)
c9 = random.randint(1, 10)
c10 = random.randint(1, 10)
c11 = random.randint(1, 10)
template = {
1: {'payoff': 0, 'after': []},
2: {'payoff': 0, 'after': [{'node_id': 1, 'cost': c1, 'weight': w1}]},
3: {'payoff': 0, 'after': [{'node_id': 1, 'cost': c2, 'weight': w2}]},
4: {'payoff': 0, 'after': [{'node_id': 3, 'cost': c3, 'weight': w3}]},
5: {'payoff': 0, 'after': [{'node_id': 3, 'cost': c4, 'weight': w4}]},
6: {'payoff': 0, 'after': [{'node_id': 3, 'cost': c5, 'weight': w5}]},
7: {'payoff': 0, 'after': [{'node_id': 4, 'cost': c6, 'weight': w6}]},
8: {'payoff': 0, 'after': [{'node_id': 4, 'cost': c7, 'weight': w7}]},
9: {'payoff': 0, 'after': [{'node_id': 5, 'cost': c8, 'weight': w8}]},
10: {'payoff': 0, 'after': [{'node_id': 5, 'cost': c9, 'weight': w9}]},
11: {'payoff': 0, 'after': [{'node_id': 6, 'cost': c10, 'weight': w10}]},
12: {'payoff': 0, 'after': [{'node_id': 6, 'cost': c11, 'weight': w11}]},
}
if node_id is None:
return [(None, Graph().from_dict(template))]
out = []
default = template[node_id]['after'][0]['weight']
for x in np.linspace(0.1, default + 1, 25):
template[node_id]['after'][0]['weight'] = x
g = Graph()
g.from_dict(template)
out.append((x, g))
return out
from petersburg import Graph
import json
__author__ = 'willmcginnis'
if __name__ == '__main__':
data = []
c_switch = 2
in_house = 1
third_party = in_house * 0.5
switching = in_house * c_switch
weight = 0.67
g = Graph()
g.from_dict({
1: {'payoff': 0, 'after': []},
2: {'payoff': 0, 'after': [{'node_id': 1, 'cost': in_house}]},
3: {'payoff': 0, 'after': [{'node_id': 2, 'cost': in_house}]},
4: {'payoff': 0, 'after': [{'node_id': 1, 'cost': third_party}]},
5: {'payoff': 0, 'after': [{'node_id': 4, 'cost': third_party}]},
6: {'payoff': 0, 'after': [{'node_id': 4, 'cost': switching, 'weight': weight}]},
7: {'payoff': 0, 'after': [{'node_id': 6, 'cost': in_house}, {'node_id': 5, 'cost': 2 * switching, 'weight': weight}]},
8: {'payoff': 0, 'after': [{'node_id': 3, 'cost': in_house}]},
9: {'payoff': 0, 'after': [{'node_id': 5, 'cost': third_party}]},
10: {'payoff': 0, 'after': [{'node_id': 7, 'cost': in_house}, {'node_id': 5, 'cost': 3 * switching, 'weight': weight}]},
11: {'payoff': 0, 'after': [{'node_id': 8, 'cost': 0}]},
12: {'payoff': 0, 'after': [{'node_id': 9, 'cost': 0}]},
13: {'payoff': 0, 'after': [{'node_id': 10, 'cost': 0}]},
})
print(g.to_dict_of_dicts())
"""
A variation of the st petersburg game where the bank has a limited bankroll, and therefore cannot payout an infinite
amount of money on any given hand.
"""
from petersburg import Graph
__author__ = 'willmcginnis'
if __name__ == '__main__':
for bankroll in [100, 10e6, 10e9, 79200000000]:
g = Graph()
entrance_fee = 0
gd = {1: {'payoff': 0, 'after': []}, 2: {'payoff': 0, 'after': [{'node_id': 1, 'cost': entrance_fee}]}}
nn = 3
idx = 0
payoff = 2 ** (idx + 1)
while payoff <= bankroll:
node_id = 2 * (idx + 1)
gd[nn] = {'payoff': payoff, 'after': [{'node_id': node_id, 'cost': 0, 'weight': 1}]}
nn += 1
gd[nn] = {'payoff': 0, 'after': [{'node_id': node_id, 'cost': 0, 'weight': 1}]}
nn += 1
idx += 1
payoff = 2 ** (idx + 1)
g.from_dict(gd)
outcomes = []
from petersburg import Graph
__author__ = 'willmcginnis'
if __name__ == '__main__':
g = Graph()
# st petersburg paradox w/ $10 entrance fee, a 1000 dollar bankroll and only 100 possible flips
entrance_fee = 10
gd = {1: {'payoff': 0, 'after': []}, 2: {'payoff': 0, 'after': [{'node_id': 1, 'cost': entrance_fee}]}}
nn = 3
for idx in range(10):
node_id = 2 * (idx + 1)
payoff = 2 ** (idx + 1)
gd[nn] = {'payoff': payoff, 'after': [{'node_id': node_id, 'cost': 0, 'weight': 1}]}
nn += 1
gd[nn] = {'payoff': 0, 'after': [{'node_id': node_id, 'cost': 0, 'weight': 1}]}
nn += 1
g.from_dict(gd)
outcomes = []
for _ in range(1000):
outcome = g.get_outcome(iters=1000, ruin=True, starting_bank=1000)
outcomes.append(outcome)
print(float(sum(outcomes))/len(outcomes))
print(min(outcomes))
print(max(outcomes))
from petersburg import Graph
__author__ = 'willmcginnis'
if __name__ == '__main__':
g = Graph()
# decide or research then decide (w/o switching)
g.from_dict({
1: {'payoff': 0, 'after': []},
2: {'payoff': 0, 'after': [{'node_id': 1, 'cost': 10, 'weight': 2}]},
3: {'payoff': 0, 'after': [{'node_id': 1, 'cost': 10}]},
4: {'payoff': 0, 'after': [{'node_id': 1, 'cost': 10, 'weight': 1.5}]},
5: {'payoff': 0, 'after': [{'node_id': 2, 'cost': 5}, {'node_id': 3, 'cost': 10}]},
6: {'payoff': 0, 'after': [{'node_id': 2, 'cost': 5}, {'node_id': 4, 'cost': 10}]},
7: {'payoff': 25, 'after': [{'node_id': 5, 'cost': 0}]},
8: {'payoff': 15, 'after': [{'node_id': 5, 'cost': 0}]},
9: {'payoff': 25, 'after': [{'node_id': 6, 'cost': 0}]},
10: {'payoff': 15, 'after': [{'node_id': 6, 'cost': 0}]},
})
print(g.adjacency_matrix())
#
# outcomes = []
# for _ in range(100000):
# outcomes.append(g.get_outcome())
#
# print('\n\nSimulated Output With Random Start')
# print(float(sum(outcomes))/len(outcomes))
#
# print('\n\nSimulated Profit of Each Starting Move')
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.style
matplotlib.style.use('ggplot')
import pandas as pd
from petersburg import Graph
__author__ = 'willmcginnis'
if __name__ == '__main__':
g = Graph()
# necktie paradox
g.from_dict({
1: {'payoff': 0, 'after': []},
2: {'payoff': 0, 'after': [{'node_id': 1}]},
3: {'payoff': 0, 'after': [{'node_id': 1}]},
4: {'payoff': 100, 'after': [{'node_id': 2, 'cost': 0}, {'node_id': 3, 'cost': 0}]},
5: {'payoff': 50, 'after': [{'node_id': 3, 'cost': 0}, {'node_id': 2, 'cost': 0}]},
})
data_2 = []
data_3 = []
for iter in [5, 10, 50, 100, 500, 1000, 5000, 10000, 50000, 1000000]:
outcomes = g.get_options(iters=iter, extended_stats=True)
data_2.append([
outcomes[2]['count'],
outcomes[2]['mean'],
outcomes[2]['min'],
outcomes[2]['max']
])
import pandas as pd
from petersburg import Graph
import matplotlib.pyplot as plt
import matplotlib.style
matplotlib.style.use('ggplot')
__author__ = 'willmcginnis'
if __name__ == '__main__':
g = Graph()
# necktie paradox
g.from_dict({
1: {'payoff': 0, 'after': []},
2: {'payoff': 0, 'after': [{'node_id': 1, 'cost': 40}]},
3: {'payoff': 0, 'after': [{'node_id': 1, 'cost': 50}]},
4: {'payoff': 50, 'after': [{'node_id': 2, 'cost': 0}]},
5: {'payoff': 40, 'after': [{'node_id': 3, 'cost': 0}]},
})
data = []
for iter in [5, 10, 50, 100, 500, 1000, 5000, 10000, 50000, 1000000]:
outcomes = []
for _ in range(iter):
outcomes.append(g.get_outcome())
data.append([iter,
float(sum(outcomes))/len(outcomes),
min(outcomes),
max(outcomes)
])
