NL4Py: Agent-Based Modeling in Python with Parallelizable NetLogo Workspaces

TL;DR

NL4Py is a Python package that enables efficient, parallel execution of NetLogo agent-based models, integrating with Python's ecosystem for scalable and accessible simulation experiments.

Contribution

It introduces a Python interface for NetLogo that supports parallel simulation runs, enhancing speed and scalability for complex agent-based modeling.

Findings

Enables parallel execution of NetLogo models in Python

Improves simulation speed and scalability

Facilitates integration with Python's analytics libraries

Abstract

External control of agent-based models is vital for complex adaptive systems research. Often these experiments require vast numbers of simulation runs and are computationally expensive. NetLogo is the language of choice for most agent-based modelers but lacks direct API access through Python. NL4Py is a Python package for the parallel execution of NetLogo simulations via Python, designed for speed, scalability, and simplicity of use. NL4Py provides access to the large number of open-source machine learning and analytics libraries of Python and enables convenient and efficient parallelization of NetLogo simulations with minimal coding expertise by domain scientists.