The simulation in start.py

The best way to start creating a simulation is by copying the start.py file and other files from ‘abce/template’ in https://github.com/AB-CE/examples.

To see how to create a simulation read ipython_tutorial.

This is a minimal template for a start.py:

from agent import Agent
from abce import *


simulation = Simulation(name='ABCE')
agents = simulation.build_agents(Agent, 'agent', 2)
for time in range(100):
    simulation.advance_round(time)
    agents.one()
    agents.two()
    agents.three()
simulation.graphs()

Note two things are important: there must be either a

graphs() or a finalize() at the end otherwise the simulation blocks at the end. Further, every round needs to be announced using simulation.advance_round(time). Where time is any representation of time.

class abce.Simulation(name='abce', random_seed=None, trade_logging='off', processes=1, check_unchecked_msgs=False)[source]

Bases: object

This class in which the simulation is run. Actions and agents have to be added. databases and resource declarations can be added. Then runs the simulation.

Args:
name:
name of the simulation
random_seed (optional):
a random seed that controls the random number of the simulation
trade_logging:
Whether trades are logged,trade_logging can be ‘group’ (fast) or ‘individual’ (slow) or ‘off’
processes (optional):
The number of processes that run in parallel. Each process hosts a share of the agents. Default is all your logical processor cores times two, using hyper-threading when available. For easy debugging set processes to one and the simulation is executed without parallelization. Sometimes it is advisable to decrease the number of processes to the number of logical or even physical processor cores on your computer. ‘None’ for all processor cores times 2. For easy debugging set processes to 1, this way only one agent runs at a time and only one error message is displayed
check_unchecked_msgs:
check every round that all messages have been received with get_massages or get_offers.

Example:

simulation = Simulation(name='ABCE',
                        trade_logging='individual',
                        processes=None)

Example for a simulation:

num_firms = 5
num_households = 2000

w = Simulation(name='ABCE',
               trade_logging='individual',
               processes=None)

w.declare_round_endowment(resource='labor_endowment',
                          productivity=1,
                          product='labor')

w.panel('firm', command='after_sales_before_consumption')

firms = w.build_agents(Firm, 'firm', num_firms)
households = w.build_agents(Household, 'household', num_households)

all = firms + households

for r in range(100):
    self.advance_round(r)
    households.recieve_connections()
    households.offer_capital()
    firms.buy_capital()
    firms.production()
    if r == 250:
        centralbank.intervention()
    households.buy_product()
    all.after_sales_before_consumption()
    households.consume()

w.finalize()
w.graphs()
advance_round(time)[source]
build_agents(AgentClass, group_name, number=None, agent_parameters=None, **parameters)[source]

This method creates agents.

Args:

AgentClass:
is the name of the AgentClass that you imported
group_name:
the name of the group, as it will be used in the action list and transactions. Should generally be lowercase of the AgentClass.
number:
number of agents to be created.
agent_parameters:
a list of dictionaries, where each agent gets one dictionary. The number of agents is the length of the list
any other parameters:
are directly passed to the agent

Example:

firms = simulation.build_agents(Firm, 'firm',
    number=simulation_parameters['num_firms'])
banks = simulation.build_agents(Bank, 'bank',
                                agent_parameters=[{'name': 'UBS'},
                                {'name': 'amex'},{'name': 'chase'}
                                **simulation_parameters,
                                loanable=True)

centralbanks = simulation.build_agents(CentralBank, 'centralbank',
                                       number=1,
                                       rounds=num_rounds)
create_agent(AgentClass, group_name, simulation_parameters=None, agent_parameters=None)[source]
create_agents(AgentClass, group_name, simulation_parameters=None, agent_parameters=None, number=1)[source]

Creates an additional agent in an existing group during the simulation. If agents have been deleted, their id’s are reduced.

Args:

AgentClass:
the class of agent to create. (can be the same class as the creating agent)
‘group_name’:
the name of the group the agent should belong to. This is the group name string e.G. 'firm', not the group variable e.G. firms in firms = simulation.build_agents(...)
simulation_parameters:
a dictionary of parameters
agent_parameters:
List of a dictionary of parameters
number:
if no agent_parameters list is given the number of agents to be created can be specified
Returns:
id of new agent.

Example:

self.create_agent(BeerFirm, 'beerfirm',
                  parameters=self.parameters,
                  agent_parameters={'creation': self.time})
declare_expiring(good, duration)[source]

This type of good lasts for several rounds, but eventually expires. For example computers would last for several years and than become obsolete.

Args:

good:
the good, which expires
duration:
the duration before the good expires
declare_perishable(good)[source]

This good only lasts one round and then disappears. For example labor, if the labor is not used today today’s labor is lost. In combination with resource this is useful to model labor or capital.

In the example below a worker has an endowment of labor and capital. Every round he can sell his labor service and rent his capital. If he does not the labor service for this round and the rent is lost.

Args:

good:
   the good that perishes

Example::

    w.declare_perishable(good='LAB')
    w.declare_perishable(good='CAP')
declare_round_endowment(resource, units, product)[source]

At the beginning of very round the agent gets ‘units’ units of good ‘product’ for every ‘resource’ he possesses.

Round endowments are group specific, that means that when somebody except the specified group holds them they do not produce.

Args:

resource:
    The good that you have to hold to get the other

units:
    the multiplier to get the produced good

product:
    the good that is produced if you hold the first good

groups:
    a list of agent groups, which gain the second good,
    if they hold the first one

Example:

A farmer gets a ton of harvest for every acre:

w.declare_round_endowment(resource='land',
                          units=1000,
                          product='wheat')
declare_service(human_or_other_resource, units, service)[source]

When the agent holds the human_or_other_resource, he gets ‘units’ of service every round the service can be used only with in this round.

Args:

human_or_other_resource:
    the good that needs to be in possessions to create the other
    good 'self.create('adult', 2)'
units:
    how many units of the service is available
service:
    the service that is created
groups:
    a list of agent groups that can create the service

Example:

For example if a household has two adult family members, it gets
16 hours of work

w.declare_service('adult', 8, 'work')
delete_agent(*ang)[source]
delete_agents(group, ids)[source]

This deletes a group of agents. The model has to make sure that other agents are notified of the death of agents in order to stop them from corresponding with this agent. Note that if you create new agents after deleting agents the ID’s of the deleted agents are reused.

Args:
group:
group of the agent
ids:
a list of ids of the agents to be deleted in that group
finalize()[source]

simulation.finalize() must be run after each simulation. It will write all data to disk

Example:

simulation = Simulation(...)
...
for r in range(100):
    simulation.advance_round(r)
    agents.do_something()
    ...

simulation.finalize()
graphs()[source]

after the simulation is run, graphs() shows graphs of all data collected in the simulation. Shows the same output as the @gui decorator shows.

Example:

simulation = Simulation(...)
for r in range(100):
    simulation.advance_round(r)
    agents.do_something()
    ...

simulation.graphs()
path = None

the path variable contains the path to the simulation outcomes it can be used to generate your own graphs as all resulting csv files are there.

time = None

Returns the current time set with simulation.advance_round(time)