Walk through

In order to learn using ABCE we will now dissect and explain a simple ABCE model. Additional to this walk through you should also have a look on the examples in


""" 1. Build a Simulation
    2. Build one Household and one Firm follow_agent
    3. For every labor_endowment an agent has he gets one trade or usable labor
    per round. If it is not used at the end of the round it disappears.
    4. Firms' and Households' possessions are monitored to the points marked in

from abce import Simulation, gui
from firm import Firm
from household import Household

def main():
    simulation = Simulation()

    simulation.declare_round_endowment(resource='labor_endowment', units=1, product='labor')

    firms = simulation.build_agents(Firm, 'firm', 1)
    households = simulation.build_agents(Household, 'household', 1)

    for r in range(100):
        (households + firms).panel_log(possessions=['money', 'GOOD'])


if __name__ == '__main__':

It is of utter most importance to end with either simulation.graphs() or simulation.finalize()

A simulation with GUI

In start.py the simulation, thus the parameters, objects, agents and time line are set up. Further it is declared, what is observed and written to the database.

from abce import Simulation, gui
from firm import Firm
from household import Household

Here the Agent class Firm is imported from the file firm.py. Likewise the Household class. Further the Simulation base class and the graphical user interface (gui) are imported

Parameters are specified as a python dictionary

parameters = {'name': '2x2',
              'random_seed': None,
              'rounds': 10,
              'slider': 100.0,
              'Checkbox': True,
              'Textbox': 'type here',
              'integer_slider': 100,
              'limited_slider': (20, 25, 50)}

def main(parameters):
    . . .

if __name__ == '__main__':

The main function is generating and executing the simulation. When the main function is preceded with @gui(simulation_parameters) The graphical user interface is started in your browser the simulation_parameters are used as default values. If no browser window open you have to go manually to the address “”. The graphical user interface starts the simulation.

During development its often more practical run the simulation without graphical user interface (GUI). In order to switch of the GUI comment out the #@gui(simulation_parameters). In order show graphs at the end of the simulation add simulation.graphs() after simulation.run, as it is done in start.py above.

To set up a new model, you create a class instance a that will comprise your model

simulation = Simulation(name="ABCE")


The order of actions: The order of actions within a round

Every agents-based model is characterized by the order of which the actions are executed. In ABCE, there are rounds, every round is composed of sub-rounds, in which a group or several groups of agents act in parallel. In the code below you see a typical sub-round. Therefore after declaring the Simulation the order of actions, agents and objects are added.

for round in range(1000):
    (households + firms).panel_log(...)

This establishes the order of the simulation. Make sure you do not overwrite internal abilities/properties of the agents. Such as ‘sell’, ‘buy’ or ‘consume’.

A more complex example could be:

for week in range(52):
    for day in ['mo', 'tu', 'we', 'th', 'fr']:
    simulation.advance_round((week, day))
    if day = 'mo':
    (households + firms).panel()
    for i in range(10):
    if week == 26:

Interactions happen between sub-rounds. An agent, sends a message in one round. The receiving agent, receives the message the following sub-round. A trade is finished in three rounds: (1) an agent sends an offer the good is blocked, so it can not be sold twice (2) the other agent accepts or rejects it. (3) If accepted, the good is automatically delivered at the beginning of the sub-round. If the trade was rejected: the blocked good is automatically unblocked.

Special goods and services

Now we will establish properties of special goods. A normal good can just be created or produced by an agent; it can also be destroyed, transformed or consumed by an agent. Some goods ‘replenish’ every round. And some goods ‘perish’ every round. These properties have to be declared:

This example declares ‘corn’ perishable and every round the agent gets 100 units of of ‘corn’ for every unit of field he possesses. If the corn is not consumed, it automatically disappears at the end of the round.

simulation.declare_round_endowment('field', 100, 'corn')


declare_round_endowment, establishes that at the beginning of every round, every agent that possesses x units of a resource, gets x*units units of the product. Every owner of x fields gets 100*x units of corn. Every owner of labor_endowment gets one unit of labor for each unit of labor_endowment he owns. An agent has to create the field or labor_endowment by self.create('field', 5), for labor_endowment respectively.


declare_perishable, establishes that every unit of the specified good that is not used by the end of the round ceases to exist.

Declaring a good as replenishing and perishable is ABCE’s way of treating services. In this example every household has some units of labor that can be used in the particular period. abce.Simulation.declare_service() is a synthetic way of declaring a good as a service.

One important remark, for a logically consistent macro-model it is best to not create any goods during the simulation, but only in abce.Agent.init(). During the simulation the only new goods should be created by abce.Simulation.declare_round_endowment(). In this way the economy is physically closed.

firms.panel_log(possessions=['good1', 'good2') # a list of firm possessions to track here

households.agg_log('household', possessions=['good1', 'good2'],
                    variables=['utility']) #  a list of household variables to track here

The possessions good1 and good2 are tracked, the agent’s variable self.utility is tracked. There are several ways in ABCE to log data. Note that the variable names a strings.

Alternative to this you can also log within the agents by simply using self.log(‘text’, variable) (abce.Database.log()) Or self.log(‘text’, {‘var1’: var1, ‘var2’: var2}). Using one log command with a dictionary is faster than using several seperate log commands.

Having established special goods and logging, we create the agents:

simulation.build_agents(Firm, 'firm', number=simulation_parameters['number_of_firms'], parameters=simulation_parameters)
simulation.build_agents(Household, 'household', number=10, parameters=simulation_parameters)
  • Firm is the class of the agent, that you have imported
  • ‘firm’ is the group_name of the agent
  • number is the number of agents that are created
  • parameters is a dictionary of parameters that the agent receives in the init function (which is discussed later)
simulation.build_agents(Plant, 'plant',
                        agent_parameters=[{'type':'coal' 'watt': 20000},
                                          {'type':'electric' 'watt': 99}
                                          {'type':'water' 'watt': 100234}])

This builds three Plant agents. The first plant gets the first dictionary as a agent_parameter {‘type’:’coal’ ‘watt’: 20000}. The second agent, gets the second dictionary and so on.

The agents

The Household agent

import abce

class Household(abce.Agent, abce.Household, abce.Trade):
    def init(self, simulation_parameters, agent_parameters):
        """ 1. labor_endowment, which produces, because of simulation.declare_resource(...)
        in start.py one unit of labor per month
        2. Sets the utility function to utility = consumption of good "GOOD"
        self.create('labor_endowment', 1)
        self.set_cobb_douglas_utility_function({"GOOD": 1})
        self.current_utility = 0

    def sell_labor(self):
        """ offers one unit of labor to firm 0, for the price of 1 "money" """
        self.sell('firm', 0,

    def buy_goods(self):
        """ receives the offers and accepts them one by one """
        oo = self.get_offers("GOOD")
        for offer in oo:

    def consumption(self):
        """ consumes_everything and logs the aggregate utility. current_utility
        self.current_utility = self.consume_everything()
        self.log('HH', self.current_utility)

The Firm agent

import abce

class Firm(abce.Agent, abce.Firm, abce.Trade):
    def init(self, simulation_parameters, agent_parameters):
        """ 1. Gets an initial amount of money
        2. create a cobb_douglas function: GOOD = 1 * labor ** 1.
        self.create('money', 1)
        self.set_cobb_douglas("GOOD", 1, {"labor": 1})

    def buy_labor(self):
        """ receives all labor offers and accepts them one by one """
        oo = self.get_offers("labor")
        for offer in oo:

    def production(self):
        """ uses all labor that is available and produces
        according to the set cobb_douglas function """

    def sell_goods(self):
        """ offers one unit of labor to firm 0, for the price of 1 "money" """
        self.sell('household', 0,

Agents are modeled in a separate file. In the template directory, you will find two agents: firm.py and household.py.

At the beginning of each agent you will find

An agent has to import the abce module and the abce.NotEnoughGoods exception

import abce
from abce import NotEnoughGoods

This imports the module abce in order to use the base classes Household and Firm. And the NotEnoughGoods exception that allows us the handle situation in which the agent has insufficient resources.

An agent is a class and must at least inherit abce.Agent. It automatically inherits abce.Trade - abce.Messenger and abce.Logger

class Agent(abce.Agent):

To create an agent that has can create a consumption function and consume

class Household(abce.Agent, abce.Household):

To create an agent that can produce:

class Firm(abce.Agent, abce.Firm)

You see our Household agent inherits from abce.Agent, which is compulsory and abce.Household. Household on the other hand are a set of methods that are unique for Household agents. The Firm class accordingly

The init method

When an agent is created it’s init function is called and the simulation parameters as well as the agent_parameters are given to him

DO NOT OVERWRITE THE __init__ method. Instead use ABCE’s init method, which is called when the agents are created

def init(self, parameters, agent_parameters):
    self.create('labor_endowment', 1)
    self.set_cobb_douglas_utility_function({"MLK": 0.300, "BRD": 0.700})
    self.type = agent_parameters['type']
    self.watt = agent_parameters['watt']
    self.number_of_firms = parameters['number_of_firms']

The init method is the method that is called when the agents are created (by the abce.Simulation.build_agents()). When the agents were build, a parameter dictionary and a list of agent parameters were given. These can now be accessed in init via the parameters and agents_parameters variable. Each agent gets only one element of the agents_parameters list.

With self.create the agent creates the good ‘labor_endowment’. Any good can be created. Generally speaking. In order to have a physically consistent economy goods should only be created in the init method. The good money is used in transactions.

This agent class inherited abce.Household.set_cobb_douglas_utility_function() from abce.Household. With abce.Household.set_cobb_douglas_utility_function() you can create a cobb-douglas function. Other functional forms are also available.

In order to let the agent remember a parameter it has to be saved in the self domain of the agent.

The action methods and a consuming Household

All the other methods of the agent are executed when the corresponding sub-round is called from the action_list in the Simulation in start.py.

For example when in the action list (‘household’, ‘consumption’) is called the consumption method is executed of each household agent is executed. It is important not to overwrite abce’s methods with the agents methods. For example if one would call the consumption(self) method below consume(self), abce’s consume function would not work anymore.

class Household(abce.Agent, abce.Household):
    def init(self, simulation_parameters, agent_parameters):
        self.create('labor_endowment', 1)
        self.set_cobb_douglas_utility_function({"GOOD": 1})
        self.current_utility = 0

    . . .

    def consumption(self):
        """ consumes_everything and logs the aggregate utility. current_utility
        self.current_utility = self.consume_everything()
        self.log('HH', self.current_utility)

In the above example we see how a (degenerate) utility function is declared and how the agent consumes. The dictionary assigns an exponent for each good, for example a consumption function that has .5 for both exponents would be {‘good1’: 0.5, ‘good2’: 0.5}.

In the method consumption, which has to be called form the action_list in the Simulation, everything is consumed an the utility from the consumption is calculated and logged. The utility is logged and can be retrieved see retrieval of the simulation results

Firms and Production functions

Firms do two things they produce (transform) and trade. The following code shows you how to declare a technology and produce bread from labor and yeast.

class Agent(abce.Agent, abce.Firm):
    def init(self):
       set_cobb_douglas('bread', 1.890, {"yeast": 0.333, "labor": 0.667})

    def production(self):

More details in abce.Firm. abce.FirmMultiTechnologies offers a more advanced interface for firms with layered production functions.


ABCE clears trade automatically. That means, that goods are automatically exchanged, double selling of a good is avoided by subtracting a good from the possessions when it is offered for sale. The modeler has only to decide when the agent offers a trade and sets the criteria to accept the trade

# Agent 1
def selling(self):
    offer = self.sell(buyer, 2, 'BRD', price=1, quantity=2.5)
    self.checkorders.append(offer)  # optional
# Agent 2
def buying(self):
    offers = self.get_offers('cookies')
    for offer in offers:
        if offer.price < 0.5
            except NotEnoughGoods:
                self.accept(offer, self.possession('money') / offer.price)
# Agent 1
def check_trade(self):

Agent 1 sends a selling offer to Agent 2, which is the agent with the id 2 from the buyer group (buyer_2) Agent 2 receives all offers, he accepts all offers with a price smaller that 0.5. If he has insufficient funds to accept an offer an NotEnoughGoods exception is thrown. If a NotEnoughGoods exception is thrown the except block self.accept(offer, self.possession('money') / offer.price) is executed, which leads to a partial accept. Only as many goods as the agent can afford are accepted. If a polled offer is not accepted its automatically rejected. It can also be explicitly rejected with self.reject(offer) (abce.Trade.reject()).

You can find a detailed explanation how trade works in abce.Trade.

Data production

There are three different ways of observing your agents:

Trade Logging

when you specify Simulation(..., trade_logging='individual') all trades are recorded and a SAM or IO matrix is created. This matrices are currently not display in the GUI, but accessible as csv files in the simulation.path directory

Manual in agent logging

An agent can log a variable, abce.Agent.possession(), abce.Agent.possessions() and most other methods such as abce.Firm.produce() with abce.Database.log():

self.log('possessions', self.possessions())
self.log('custom', {'price_setting': 5: 'production_value': 12})
prod = self.production_use_everything()
self.log('current_production', prod)

Retrieving the logged data

If the GUI is switched off there must be a abce.Simulation.graphs() after abce.Simulation.run() . Otherwise no graphs are displayed. If no browser window open you have to go manually to the address “

The results are stored in a subfolder of the ./results/ folder. simulation.path gives you the path to that folder.

The tables are stored as ‘.csv’ files which can be opened with excel.

[1]round % 2 == 0 means the remainder of round divided by 2 is zero.