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Calibrating Agent-Based Models Workshop

Python License DOI Binder Ruff pre-commit security: bandit Build CodeQL Advanced

Documentation | API | Changelog | Releases | Docker | Binder

A workshop covering the calibration of agent-based models.

Table of contents

Introduction

This repository contains code examples and materials for an introductory session on the calibration of agent-based models (ABMs). The examples are designed to accompany the session and demonstrate, at a high level, what calibration means in the context of ABMs, why calibration is needed, and some of the different approaches that can be used to connect model parameters with observations or other empirical data.

Workshop

Workshop material for agent-based modelling may be found in the workshop directory.

This workshop material includes the following example models:

  1. Lotka-Volterra ordinary differential equations
  2. Wolf and Sheep Predation agent-based model

We will work though basic examples for optimisation and sensitivity analysis, alongside more complex calibration methods.

Usage with Docker

To run the examples and workshop material within a Docker container, execute the following:

wget https://raw.githubusercontent.com/JBris/calibrating-abms-workshop/refs/heads/main/docker-compose.yaml
docker compose up caliagent

# ctrl + C to exit

Usage with Binder

Click this link to launch the examples and workshop material within Binder.

Note that you may need to wait roughly 3 or more minutes for the workshop Docker image to be pulled when first using Binder. Please be patient.

Coordinators

Announcements

To view workshop announcements, please select this link.

Communication

Please refer to the following links:

Contributions and Support

Contributions are more than welcome. For general guidelines on how to contribute to this project, take a look at CONTRIBUTING.md.

For our community code of conduct, please also view CODE_OF_CONDUCT.md.

License

This workshop is published under the MIT License (see LICENSE).