def equi(self):
#random.seed(datetime.datetime.now())
sys = SOE(g = self.g, a = self.a, t = self.t, c_k = self.c_k, c_l = self.c_l,\
a_k = self.a_k, a_l = self.a_l, PE = self.PE)
a = Agent(g = self.g, a = self.a, t = self.t, c_k = self.c_k, c_l = self.c_l,\
a_k = self.a_k, a_l = self.a_l, PE = self.PE)
x = fsolve(sys.KL_PE, ((random.uniform(1, 5), ) * 2), xtol=1.49012e-10)
while x[0] <= 1e-2 or x[1] <= 1e-2:
x = fsolve(sys.KL_PE, ((random.uniform(1, 5), ) * 2),
xtol=1.49012e-10)
y = np.array([])
for b in range(2):
y = np.append(y, x[b]**2)
ans = [0, 0, 0]
ans[1] = y[0]
ans[2] = y[1]
ans[0] = a.Prod(ans)
for i in range(100):
random.seed(datetime.datetime.now())
x = fsolve(sys.KL_PE, ((random.uniform(1, 5), ) * 2),
xtol=1.49012e-10)
while x[0] < 1e-2 or x[1] < 1e-2:
random.seed(datetime.datetime.now())
x = fsolve(sys.KL_PE, ((random.uniform(1, 5), ) * 2),
xtol=1.49012e-10)
y = np.array([])
for b in range(2):
y = np.append(y, x[b]**2)
comp = [0, 0, 0]
comp[1] = y[0]
comp[2] = y[1]
comp[0] = a.Prod(comp)
print comp
if a.Util(ans) < a.Util(comp):
ans = comp
print ans
print a.Util(ans)
def Comp(self):
random.seed(datetime.datetime.now())
sys = SOE(g = self.g, a = self.a, t = self.t, c_k = self.c_k, c_l = self.c_l,\
a_k = self.a_k, a_l = self.a_l, PE = self.PE)
a = Agent(g = self.g, a = self.a, t = self.t, c_k = self.c_k, c_l = self.c_l,\
a_k = self.a_k, a_l = self.a_l, PE = self.PE)
#fin the solution for the social planner
x = fsolve(sys.KL, ((random.uniform(1, 5), ) * 2),
xtol=1.49012e-10,
maxfev=10000)
while x[0] <= 1e-2 or x[1] <= 1e-2:
x = fsolve(sys.KL, ((random.uniform(1, 5), ) * 2),
xtol=1.49012e-10,
maxfev=10000)
y = np.array([])
for b in range(2):
y = np.append(y, x[b]**2)
ans = [0, 0, 0]
ans[1] = y[0]
ans[2] = y[1]
ans[0] = a.Prod(ans)
for i in range(1000):
random.seed(datetime.datetime.now())
x = fsolve(sys.KL, ((random.uniform(1, 5), ) * 2),
xtol=1.49012e-10,
maxfev=10000)
while x[0] <= 1e-2 or x[1] <= 1e-2:
random.seed(datetime.datetime.now())
x = fsolve(sys.KL, ((random.uniform(1, 5), ) * 2),
xtol=1.49012e-10,
maxfev=10000)
y = np.array([])
for b in range(2):
y = np.append(y, x[b]**2)
comp = [0, 0, 0]
comp[1] = y[0]
comp[2] = y[1]
comp[0] = a.Prod(comp)
#print a.Util(comp)
if a.Util(ans) < a.Util(comp):
ans = comp
ans1 = ans
u1 = a.Util(ans1)
x = fsolve(sys.KL_PE, ((random.uniform(1, 5), ) * 2), xtol=1.49012e-10)
while x[0] <= 1e-2 or x[1] <= 1e-2:
x = fsolve(sys.KL_PE, ((random.uniform(1, 5), ) * 2),
xtol=1.49012e-10)
y = np.array([])
for b in range(2):
y = np.append(y, x[b]**2)
ans = [0, 0, 0]
ans[1] = y[0]
ans[2] = y[1]
ans[0] = a.Prod(ans)
for i in range(1000):
random.seed(datetime.datetime.now())
x = fsolve(sys.KL_PE, ((random.uniform(1, 5), ) * 2),
xtol=1.49012e-10)
while x[0] < 1e-2 or x[1] < 1e-2:
random.seed(datetime.datetime.now())
x = fsolve(sys.KL_PE, ((random.uniform(1, 5), ) * 2),
xtol=1.49012e-10)
y = np.array([])
for b in range(2):
y = np.append(y, x[b]**2)
comp = [0, 0, 0]
comp[1] = y[0]
comp[2] = y[1]
comp[0] = a.Prod(comp)
#print a.Util(comp)
if a.Util(ans) < a.Util(comp):
ans = comp
ans2 = ans
u2 = a.Util(ans2)
d = u2 - u1
k1 = ans1[1]
k2 = ans2[1]
kd = k2 - k1
print "Individual utility level: %6.4f" % u1
print "Optimal utility level: %6.4f" % u2
print "There is a %6.4f difference" % d
print "Capital is underinvested by %6.4f" % kd
