Firm and production

class abce.agents.Firm

Bases: abce.agents.firmmultitechnologies.FirmMultiTechnologies

The firm class allows you to declare a production function for a firm. abce.Firm.set_leontief(), abce.Firm.set_production_function() abce.Firm.set_cobb_douglas(), abce.Firm.set_production_function_fast() (FirmMultiTechnologies, allows you to declare several) With abce.Firm.produce() and abce.Firm.produce_use_everything() you can produce using the according production function. You have several auxiliary functions for example to predict the production. When you multiply abce.Firm.predict_produce() with the price vector you get the profitability of the production.

If you want to create a firm with more than one production technology, you, should use the abce.FirmMultiTechnologies class.

net_value(produced_goods, used_goods, price_vector)

Calculates the net_value of a goods_vector given a price_vector

goods_vectors are vector, where the input goods are negative and the output goods are positive. When we multiply every good with its according price we can calculate the net_value of the corresponding production. goods_vectors are produced by predict_produce(.)

Args:

produced_goods:

a dictionary with goods and quantities

used_goods: e.G. {‘car’: 1, ‘metal’: -1200, ‘tire’: -4, ‘plastic’: -21} price_vector: a dictionary with goods and prices (see example)

Example:

prices = {'car': 50000, 'tire': 100, 'metal': 10, 'plastic':  0.5}
value_one_car = net_value(predict_produce(car_production_function, one_car), prices)
value_two_cars = net_value(predict_produce(car_production_function, two_cars), prices)
if value_one_car > value_two_cars:
   produce(car_production_function, one_car)
else:
   produce(car_production_function, two_cars)

predict_net_value(input_goods, price_vector)

Predicts the net value of a production, given a price vector

Args:

production_function:

a production function

input_goods:

the goods to be used in the simulated production

price_vector:

vector of prices for input and output goods

Example:

input_goods = {'wheels': 4, 'chassi': 1}
price_vector = {'wheels': 10, 'chassi': 100, 'car':1000}
self.predict_net_value(self.consumption_good_production_function, input_goods, price_vector)

>>> 860

predict_produce_input(input_goods)

Returns a vector with input of goods

Predicts the use of input goods, for a production.

Args:

production_function:

A production_function produced with create_production_function, create_cobb_douglas or create_leontief

input_goods: {‘input_good1’: amount1, ‘input_good2’: amount2 …}:

dictionary containing the amount of input good used for the production.

Example:

print(A.predict_produce_input(car_production_function, two_cars))

>>> {'wheels': 4, 'chassi': 1}

predict_produce_output(input_goods)

Predicts the output of a certain input vector and for a given

production function

Predicts the production of produce(production_function, input_goods)

Args:

production_function:
    A production_function produced with
    create_production_function, create_cobb_douglas or create_leontief
input_goods {'input_good1': amount1, 'input_good2': amount2 ...}:
    dictionary containing the amount of input good used for the production.

Returns:

A dictionary of the predicted output

Example:

print(A.predict_produce_output(car_production_function, two_cars))
>>> {'car': 2}

produce(input_goods)

Produces output goods given the specified amount of inputs.

Transforms the Agent’s goods specified in input goods according to a given production_function to output goods. Automatically changes the agent’s belonging. Raises an exception, when the agent does not have sufficient resources.

Args:

{‘input_good1’: amount1, ‘input_good2’: amount2 …}:

dictionary containing the amount of input good used for the production.

Raises:

NotEnoughGoods:

This is raised when the goods are insufficient.

Example:

self.set_cobb_douglas_production_function('car' ..)
car = {'tire': 4, 'metal': 2000, 'plastic':  40}
try:
    self.produce(car)
except NotEnoughGoods:
    print('today no cars')

produce_use_everything()

Produces output goods from all input goods.

Example:

self.produce_use_everything()

set_ces(output, gamma, multiplier=1, shares=None)

creates a CES production function

A production function is a production process that produces the given input goods according to the CES formula to the output good:

\(Q = F \cdot \left[\sum_{i=1}^n a_{i}X_{i}^{\gamma}\ \right]^{\frac{1}{\gamma}}\)

Production_functions are than used by in produce, predict_vector_produce and predict_output_produce.

Args:

‘output’:

Name of the output good

gamma:

elasticity of substitution \(= s =\frac{1}{1-\gamma}\)

multiplier:

CES multiplier \(F\)

shares:

\(a_{i}\) = Share parameter of input i, \(\sum_{i=1}^n a_{i} = 1\) when share_parameters is not specified all inputs are weighted equally and the number of inputs is flexible. Share parameters are an array with good names as keys and the shares as values.

Example:

def init(self):
    self.set_ces('stuff', gamma=0.5, multiplier=1, shares={'labor': 0.25, 'stone':0.25, 'wood':0.5})

...


def producing(self):
    self.produce({'stone' : 20, 'labor' : 1, 'wood': 12})

set_cobb_douglas(output, multiplier, exponents)

sets the firm to use a Cobb-Douglas production function.

A production function is a production process that produces the given input goods according to the formula to the output good.

Args:

‘output’: Name of the output good multiplier: Cobb-Douglas multiplier {‘input1’: exponent1, ‘input2’: exponent2 …}: dictionary containing good names ‘input’ and corresponding exponents

Example:

self.set_cobb_douglas('plastic', 0.000001, {'oil' : 10, 'labor' : 1})
self.produce({'oil' : 20, 'labor' : 1})

set_leontief(output, utilization_quantities, multiplier=1)

sets the firm to use a Leontief production function.

A production function is a production process that produces the given input given according to the formula to the output good.

Warning, when you produce with a Leontief production_function all goods you put in the produce(…) function are used up. Regardless whether it is an efficient or wasteful bundle

Args:

‘output’: Name of the output good {‘input1’: utilization_quantity1, ‘input2’: utilization_quantity2 …}: dictionary containing good names ‘input’ and corresponding exponents multiplier: multiplier isinteger=’int’ or isinteger=’‘: When ‘int’ produces only integer amounts of the good. When ‘’, produces floating amounts.

Example:

self.create_leontief('car', {'tire' : 4, 'metal' : 1000, 'plastic' : 20}, 1)
two_cars = {'tire': 8, 'metal': 2000, 'plastic':  40}
self.produce(two_cars)

set_production_function(formula, output, use)

Creates the firm’s production functions from a formula.

A production function is a production process that produces a given output good from several input goods. Once you have set a production function you can use abce.Firm.produce() to produce.

If you want to produce more than one output good try abce.Firm.set_production_function_many_goods()

Args:

formula:

this is a method, that takes the possession dictionary as an input and returns a float.

output:

the name of the good ‘string’

use:

a dictionary of how much percent of each good is used up in the process

Example:

def init(self):
    ...
    def production_function(goods):
        return goods['a'] ** 0.25 * goods['b'] ** 0.5 * goods['c'] ** 0.25

    use = {'a': 1, 'b': 0.1, 'c': 0}

    self.set_production_function(production_function, output='cookies' use=use)

def production(self):
    self.produce({'a' : 1, 'b' : 2}

set_production_function_many_goods(formula, use)

creates a production function that produces many goods

A production function is a production process that produces the several output goods according to the formula to the output goods and uses up some or all of the input goods. Production_functions are than used by produce, predict_vector_produce and predict_output_produce.

create_production_function_fast is faster but more complicated

Args:

formula:

this is a method, that takes a goods dictionary as an input and returns a dictionary with the newly created goods.

use:

a dictionary of how much percent of each good is used up in the process

Returns:

A production_function that can be used in produce etc.

Example:

def init(self):
    ...
    def production_function(goods)
        output = {'soft_rubber': goods['a'] ** 0.25 * goods['b'] ** 0.5 * goods['c'] ** 0.25,
                  'hard_rubber': goods['a'] ** 0.1 * goods['b'] ** 0.2 * goods['c'] ** 0.01,
                  'waste': goods['b'] / 2}
        return output

    use = {'a': 1, 'b': 0.1, 'c': 0}

    self.set_production_function_many_goods(production_function, use)

def production(self):
    self.produce({'a' : 1, 'b' : 2, 'c': 5})

sufficient_goods(input_goods)

checks whether the agent has all the goods in the vector input