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
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']
])
data_3.append([
outcomes[3]['count'],
outcomes[3]['mean'],
outcomes[3]['min'],
outcomes[3]['max']
])
df = pd.DataFrame(data_2, columns=['iters', 'outcome', 'min', 'max'])
