Skip to main content
Pre-release

This release is a pre-release and may not be stable for production use.

ABCE the Agent-Based Computational Economy platform that makes modeling easier
//////////////////////////////////////////////////////////////////////////////

ABCE is a Python based modeling platform for economic simulations.
ABCE comes with standard functions to simulations of trade, production
and consumption. The modeler can concentrate on implementing
the logic and decisions of an agents; ABCE takes care of all exchange
of goods and production and consumption.

.. figure:: http://abce.readthedocs.io/en/0.9b/_images/cheesegrater.png
:target: http://35.176.189.179/ABCE/
:scale: 20 %
:align: right

In ABCE goods have the physical properties of
goods in reality in the sense that if agent A gives a good to agent B, then
- unlike information - agent B receives the good and agent B does not have
the good anymore.
The ownership and transformations (production or consumption) of goods are
automatically handled by the platform.

ABCE models are programmed in standard Python, stock functions of agents
can be inherited from archetype classes (Firm or Household). The only
not-so-standard Python is that agents are executed in parallel by the
Simulation class (in start.py).

ABCE allows the modeler to program agents as ordinary Python class-objects,
but run the simulation on a multi-core/processor computer. It takes no
effort or intervention from the modeler to run the simulation on a
multi-core system.
The speed advantages of using ABCE with multi-processes enabled.
ABCE are typically only observed for 10000 agents and more. Below, it
might be slower than pure python implementation. ABCE supports pypy3,
which is approximately 10 times faster than CPython.

ABCE is a scheduler and a set of agent classes.
According to the schedule the simulation class calls - each sub-round - agents
to execute some actions. Each agent executes these actions
using some of the build-in functions, such as trade, production and
consumption of ABCE. The agents can use the full set of commands of the
Python general purpose language.

The audience of ABCE are economists that want to model agent-based
models of trade and production.

ABCE does support an accounting framework
for financial simulations. `ESL can be downloaded here <https://github.com/AB-CE/ABCESL>`_.

ABCE runs on macOS, Windows, and Linux. ABCE runs 10x faster on pypy!

.. image:: https://zenodo.org/badge/4157636.svg
:target: https://zenodo.org/badge/latestdoi/4157636

.. image:: https://travis-ci.org/AB-CE/abce.svg?branch=master
:alt: ABCE build status on Travis CI
:target: https://travis-ci.org/AB-CE/abce

.. image:: https://ci.appveyor.com/api/projects/status/c2w73u9im2b87reb?svg=true
:alt: ABCE build status on Appveyor CI
:target: https://ci.appveyor.com/project/AB-CE/abce

.. image:: https://img.shields.io/pypi/v/abce.svg
:alt: Pypi version
:target: https://pypi.python.org/pypi/abce

.. image:: https://readthedocs.org/projects/abce/badge/?version=master
:alt: readthedocs
:target: https://abce.readthedocs.io


Install with::

pip3 install abce

The documentation is here:

http://abce.readthedocs.io/

An example is here:

`Insurance Market <http://35.176.189.179/ABCE/>`_

A code example is here:

`Jupyter Tutorial <https://github.com/AB-CE/examples/tree/master/examples/jupyter_tutorial>`_

More code examples are here:

https://github.com/AB-CE/examples

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

abcEconomics-0.9.5b3.tar.gz (499.5 kB view details)

Uploaded Source

File details

Details for the file abcEconomics-0.9.5b3.tar.gz.

File metadata

  • Download URL: abcEconomics-0.9.5b3.tar.gz
  • Upload date:
  • Size: 499.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No

File hashes

Hashes for abcEconomics-0.9.5b3.tar.gz
Algorithm Hash digest
SHA256 2d76ebd01806ec590e751e1e626855a089586f7e772c98247f3561184b7607a3
MD5 7e7a31c8a26cdf915679383d7015cacf
BLAKE2b-256 86781bb88baf3f21f180a71482ed2cf7fc70b97db609a696b43dee29247fac84

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page