parameters['N'] = 50 #number of consumers
#creating a dictionary that contains the parameters of the bernoulli distribution that models the endowment process
e_dist = {}
e_dist['lo'] = 0.5 #lower value in endowment distribution
e_dist['hi'] = 1 #higher value in endowment distribution
e_dist['lo_prob'] = 0.5 #prob lower earning state in endowment bernoulli distribution
#initialize interest rate variable
rate = .05
#create and initialize a list of N consumers, each with a distinct, randomly chosen beta. Beta is drawn from an uniform distribution between .9 and 1.
population = [agents.consumer(random.uniform(parameters['beta_lo'], parameters['beta_hi'])) for i in range(parameters['N'])]
#construct and initialize a single bank to represent the financial system in this economy.
bank = agents.bank(rate)
#main loop. each iteration is one time period. How long is a 'period' in this model?
bond_limit = []
borrow_matrix = np.empty((parameters['N'], parameters['T']))
borrow_adj_matrix = np.empty((parameters['N'], parameters['T']))
consump_matrix = np.empty((parameters['N'], parameters['T']))
utility_matrix = np.empty((parameters['N'], parameters['T']))
for period in range(parameters['T']):
print(period)
for person in population:
idx = population.index(person)
person.borrowing(parameters, e_dist, period, bank.rate)
def sen_analysis(parameters):
# This is the main program that should be executed to run the agent-based model.
import matplotlib.pyplot as plt
import numpy as np
import random
import agents
#breaking apart parameters
e_dist = {}
e_dist['lo'] = parameters['e_lo']
e_dist['hi'] = parameters['e_hi']
e_dist['lo_prob'] = parameters['e_lo_prob']
rate = parameters['rate']
#setting a seed for random number generator
random.seed(1)
#create and initialize a list of N consumers, each with a distinct, randomly chosen beta. Beta is drawn from an uniform distribution between .9 and 1.
population = [agents.consumer(random.uniform(parameters['beta_lo'], parameters['beta_hi'])) for i in np.arange(parameters['N'])]
#construct and initialize a single bank to represent the financial system in this economy.
bank = agents.bank(rate)
#main loop. each iteration is one time period. How long is a 'period' in this model?
bond_limit = []
borrow_matrix = np.empty((parameters['N'], parameters['T']))
borrow_adj_matrix = np.empty((parameters['N'], parameters['T']))
consump_matrix = np.empty((parameters['N'], parameters['T']))
utility_matrix = np.empty((parameters['N'], parameters['T']))
for period in np.arange(parameters['T']):
if (period % 100) == 0:
print(period)
for person in population:
idx = population.index(person)
person.borrowing(parameters, e_dist, period, bank.rate)
#collecting borrowing/saving decision of each consumer in each period BEFORE adjusted by the bank.
borrow_matrix[idx, period] = person.bond #collecting borrowing constrained by borrowing limit. does borrowing limit actually bind? mostly no.
#a list collecting the borrowing limits across time. this list is graphed below.
bond_limit.append(person.bond_limit)
#bank then adjusts borrowing/saving amounts so that market clears,
bank.clearing_market(population)
#need to calculate consumption and utility BEFORE adjusting price since TODAY's interest rate factors into both calculations, but AFTER bank adjusts borrowing.
for person in population:
idx = population.index(person)
person.calculating_utility(parameters, bank.rate)
consump_matrix[idx, period] = person.consumption
utility_matrix[idx, period] = person.utility
borrow_adj_matrix[idx, period] = person.bond #borrowing adjusted to equate supply and demand
#bank then adjusts the interest rate based upon balance of supply and bank. This adjusted interest rate will be used next period by consumers to make borrowing/saving decision.
bank.adjusting_price()
return bank.supply_rec, bank.demand_rec, bank.market_balance_rec, bank.rate_rec, np.sum(consump_matrix, axis = 0), np.sum(utility_matrix, axis = 0)