def run_batch(batch_size,
US_strat,
Iran_strat,
default_strat="low",
coop_multiplier=1,
conflict_multiplier=1,
coop_gain=0.01,
war_cost=0.5,
war_steal=0.5,
power_scale=0.1):
data = []
for i in range(batch_size):
data.append(
run(end_at_tick=1000,
US_strat=US_strat,
Iran_strat=Iran_strat,
default_strat=default_strat,
coop_multiplier=coop_multiplier,
conflict_multiplier=conflict_multiplier,
coop_gain=coop_gain,
war_cost=war_cost,
war_steal=war_steal,
power_scale=power_scale).get_model_run(0, 1000)[0])
return data
def figure_6_data():
if not os.path.exists('Heatmaps'):
os.makedirs('Heatmaps')
data = np.array([]).reshape(0, 50)
for coop in np.arange(0.14, 0.71, 0.01):
result = run(cooperate_probability=coop).get_log_wealth_heatmap(
0, 0.05, 51)[:, 1:]
data = np.concatenate([data, result])
np.savetxt('Heatmaps/CooperateHeatmap.csv', data, fmt='%d', delimiter=',')
def figure_10_data():
if not os.path.exists('Dwell Time'):
os.makedirs('Dwell Time')
data = np.array([]).reshape([0, 2])
for _ in range(10):
data = np.concatenate([data, run(linear_power=False).get_dwell_time()])
np.savetxt('Dwell Time/Logarithmic.csv',
data,
fmt=['%d', '%f'],
delimiter=',')
def figure_8_and_9_data():
if not os.path.exists('Model Runs'):
os.makedirs('Model Runs')
data = run(end_at_tick=2001,
cooperate_probability=0.3,
linear_power=False,
power_exponent=1.75).get_model_run(1000, 2001)
np.savetxt('Model Runs/Log 0.3 coop.csv', data.T, fmt='%f', delimiter=',')
data = run(end_at_tick=2001,
cooperate_probability=0.5,
linear_power=False,
power_exponent=1.75).get_model_run(1000, 2001)
np.savetxt('Model Runs/Log default.csv', data.T, fmt='%f', delimiter=',')
data = run(end_at_tick=2001,
cooperate_probability=0.7,
linear_power=False,
power_exponent=1.75).get_model_run(1000, 2001)
np.savetxt('Model Runs/Log 0.7 coop.csv', data.T, fmt='%f', delimiter=',')
data = run(end_at_tick=2001,
cooperate_probability=0.5,
linear_power=False,
power_exponent=3.0).get_model_run(1000, 2001)
np.savetxt('Model Runs/Log 3.0 exponent.csv',
data.T,
fmt='%f',
delimiter=',')
data = run(end_at_tick=2001,
cooperate_probability=0.5,
linear_power=False,
power_exponent=1.0).get_model_run(1000, 2001)
np.savetxt('Model Runs/Log 1.0 exponent.csv',
data.T,
fmt='%f',
delimiter=',')
def figure_3_data():
if not os.path.exists('Model Runs'):
os.makedirs('Model Runs')
data = run(end_at_tick=2001,
cooperate_probability=0.3).get_model_run(1000, 2001)
np.savetxt('Model Runs/Linear 0.3 coop.csv',
data.T,
fmt='%f',
delimiter=',')
data = run(end_at_tick=2001,
cooperate_probability=0.5).get_model_run(1000, 2001)
np.savetxt('Model Runs/Linear default.csv',
data.T,
fmt='%f',
delimiter=',')
data = run(end_at_tick=2001,
cooperate_probability=0.7).get_model_run(1000, 2001)
np.savetxt('Model Runs/Linear 0.7 coop.csv',
data.T,
fmt='%f',
delimiter=',')
def figure_11_data():
if not os.path.exists('Dwell Time'):
os.makedirs('Dwell Time')
data = np.array([]).reshape(0, 3)
for exponent in [1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0]:
result = run(linear_power=False,
power_exponent=exponent).get_dwell_time()
exponent_values = np.array([[exponent]] * len(result))
result = np.concatenate([exponent_values, result], axis=1)
data = np.concatenate([data, result])
np.savetxt('Dwell Time/LogarithmicColours.csv',
data,
fmt=['%.2f', '%d', '%f'],
delimiter=',')
def figure_5_data():
if not os.path.exists('Dwell Time'):
os.makedirs('Dwell Time')
data = np.array([]).reshape(0, 3)
for coop in [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7]:
result = run(end_at_tick=100000,
cooperate_probability=coop).get_dwell_time()
coop_values = np.array([[coop]] * len(result))
result = np.concatenate([coop_values, result], axis=1)
data = np.concatenate([data, result])
np.savetxt('Dwell Time/LinearColours.csv',
data,
fmt=['%.1f', '%d', '%f'],
delimiter=',')
def figure_12_data():
if not os.path.exists('Heatmaps'):
os.makedirs('Heatmaps')
data = np.array([]).reshape(0, 700)
for exponent in np.arange(1.0, 3.01, 0.01):
print('{:.1%} done'.format((exponent - 1) / 2))
result = []
for _ in range(4):
result.append(
run(linear_power=False,
power_exponent=exponent).get_log_wealth_heatmap(
-5, 0.01, 700))
result = np.array(result).mean(axis=0)
data = np.concatenate([data, result])
np.savetxt('Heatmaps/ExponentHeatmap.csv', data, fmt='%g', delimiter=',')
def figure_4_data():
if not os.path.exists('Dwell Time'):
os.makedirs('Dwell Time')
data = run(end_at_tick=100000).get_dwell_time()
np.savetxt('Dwell Time/Linear.csv', data, fmt=['%d', '%f'], delimiter=',')