def main():
simulation_parameters = {
'name': 'direct_optimization',
'random_seed': None,
'rounds': 500,
'trade_logging': 'off',
'num_firms': 250,
'alpha': 0.3,
'gamma': 0.8,
'price_stickiness': 0.0,
'network_weight_stickiness': 0.0,
'wage_stickiness': 0.0,
'dividends_percent': 0.0,
'percentage_beneficiaries': 0.25,
'percentage_injection': 0.25,
'time_of_intervention': 250
}
s = Simulation(name=simulation_parameters['name'])
s.declare_service('labor_endowment', 1, 'labor')
network = create_network(simulation_parameters['num_firms'])
network = [
network.neighbors(neighbor)
for neighbor in range(simulation_parameters['num_firms'])
]
firms = s.build_agents(Firm,
'firm',
parameters=simulation_parameters,
agent_parameters=network)
household = s.build_agents(Household,
'household',
1,
parameters=simulation_parameters)
centralbank = s.build_agents(CentralBank,
'centralbank',
1,
parameters=simulation_parameters)
for rnd in range(simulation_parameters['rounds']):
s.advance_round(rnd)
(firms + household).send_demand()
(firms + household).selling()
(firms + household).buying()
firms.production()
firms.dividends()
household.consuming()
firms.change_weights()
firms.stats()
centralbank.intervention()
household.agg_log(possessions=['money'],
variables=['utility', 'rationing'])
firms.agg_log(possessions=['money'],
variables=[
'produced', 'profit', 'price', 'dead', 'inventory',
'rationing'
])
s.graphs()
def main(simulation_parameters):
s = Simulation(name=simulation_parameters['name'])
s.declare_service('labor_endowment', 1, 'labor')
network = create_network(simulation_parameters['num_firms'])
network = [
network.neighbors(neighbor)
for neighbor in range(simulation_parameters['num_firms'])
]
firms = s.build_agents(Firm,
'firm',
parameters=simulation_parameters,
agent_parameters=network)
household = s.build_agents(Household,
'household',
1,
parameters=simulation_parameters)
centralbank = s.build_agents(CentralBank,
'centralbank',
1,
parameters=simulation_parameters)
for rnd in range(simulation_parameters['rounds']):
s.advance_round(rnd)
(firms + household).send_demand()
(firms + household).selling()
(firms + household).buying()
firms.production()
firms.dividends()
household.consuming()
firms.change_weights()
firms.stats()
centralbank.intervention()
household.agg_log(possessions=['money'],
variables=['utility', 'rationing'])
firms.agg_log(possessions=['money'],
variables=[
'produced', 'profit', 'price', 'dead', 'inventory',
'rationing'
])
s.finalize()
def main(simulation_parameters):
sam = Sam(
'climate_square.sam.csv',
inputs=[
'col', 'ele', 'gas', 'o_g', 'oil', 'eis', 'trn', 'roe', 'lab',
'cap'
],
outputs=['col', 'ele', 'gas', 'o_g', 'oil', 'eis', 'trn', 'roe'],
output_tax='tax',
consumption=['col', 'ele', 'gas', 'o_g', 'oil', 'eis', 'trn', 'roe'],
consumers=['hoh'])
""" reads the social accounting matrix and returns coefficients of a cobb-douglas model """
carbon_prod = defaultdict(float)
carbon_prod.update({
'col': 2112 * 1e-4,
'oil': 2439.4 * 1e-4,
'gas': 1244.3 * 1e-4
})
""" this is the co2 output per sector at the base year """
print(sam.output_tax_shares())
simulation_parameters.update({
'name':
'cce',
'random_seed':
None,
'num_household':
1,
'num_firms':
1,
'endowment_FFcap':
sam.endowment('cap'),
'endowment_FFlab':
sam.endowment('lab'),
'final_goods':
sam.consumption,
'capital_types': ['cap', 'lab'],
'dividends_percent':
0.0,
'production_functions':
sam.production_functions(),
'consumption_functions':
sam.utility_function(),
'output_tax_shares':
sam.output_tax_shares(),
'money':
2691.2641884030372,
'inputs':
sam.inputs,
'outputs':
sam.outputs,
'balance_of_payment':
sam.balance_of_payment('nx', 'inv'),
'sam':
sam,
'carbon_prod':
carbon_prod,
'wage_stickiness':
0.5,
'price_stickiness':
0.5,
'network_weight_stickiness':
0.5
})
simulation = Simulation(trade_logging='group', processes=1)
simulation.declare_service('endowment_FFcap', 1, 'cap')
simulation.declare_service('endowment_FFlab', 1, 'lab')
""" every round for every endowment_FFcap the owner gets one good of lab
similar for cap"""
firms = {
good:
simulation.build_agents(Firm,
number=simulation_parameters['num_firms'],
group_name=good,
parameters=simulation_parameters)
for good in sam.outputs
}
household = simulation.build_agents(Household,
'household',
simulation_parameters['num_household'],
parameters=simulation_parameters)
netexport = simulation.build_agents(NetExport,
'netexport',
1,
parameters=simulation_parameters)
government = simulation.build_agents(Government,
'government',
1,
parameters=simulation_parameters)
firms_and_household = sum(firms.values()) + household
all_firms = sum(firms.values())
try:
for r in range(simulation_parameters['rounds']):
simulation.advance_round(r)
all_firms.taxes_intervention()
firms_and_household.send_demand()
firms_and_household.selling()
firms_and_household.buying()
household.money_to_nx()
all_firms.production()
all_firms.carbon_taxes()
all_firms.sales_tax()
government.taxes_to_household()
all_firms.international_trade()
all_firms.invest()
netexport.invest()
household.sales_accounting()
all_firms.dividends()
all_firms.change_weights()
all_firms.stats()
household.agg_log(variables=['welfare'])
(firms['col'] + firms['gas'] +
firms['oil']).agg_log(variables=['price', 'produced', 'co2'])
(firms['ele'] + firms['o_g'] + firms['eis'] + firms['trn'] +
firms['roe']).agg_log(variables=['price', 'produced'])
household.consuming()
except Exception as e:
print(e)
simulation.finalize()
# raise # put raise for full traceback but no graphs in case of error
iotable.to_iotable(simulation.path,
[99, simulation_parameters['rounds'] - 1])
mean_price = iotable.average_price(simulation.path, 99)
print('mean price', mean_price)
# simulation.graphs()
return mean_price
