NL4Py: Agent-based modeling in Python with parallelizable NetLogo workspaces

Chathika Gunaratne; Ivan Garibay

doi:10.1016/j.softx.2021.100801

NL4Py: Agent-based modeling in Python with parallelizable NetLogo workspaces

Chathika Gunaratne, Ivan Garibay

Learning Systems

Research output: Contribution to journal › Article › peer-review

12 Scopus citations

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.

Original language	English
Article number	100801
Journal	SoftwareX
Volume	16
DOIs	https://doi.org/10.1016/j.softx.2021.100801
State	Published - Dec 2021

Funding

This work was supported by Defense Advanced Research Projects Agency (DARPA), USA program HR001117S0018 ( FA8650-18-C-7823 ).

Keywords

Agent-based modeling
Complex adaptive systems
NetLogo
Parameter calibration
Python

Access to Document

10.1016/j.softx.2021.100801

Cite this

@article{17bf98a921d04b7486155653437a2e9f,

title = "NL4Py: Agent-based modeling in Python with parallelizable NetLogo workspaces",

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.",

keywords = "Agent-based modeling, Complex adaptive systems, NetLogo, Parameter calibration, Python",

author = "Chathika Gunaratne and Ivan Garibay",

note = "Publisher Copyright: {\textcopyright} 2021 The Authors",

year = "2021",

month = dec,

doi = "10.1016/j.softx.2021.100801",

language = "English",

volume = "16",

journal = "SoftwareX",

issn = "2352-7110",

publisher = "Elsevier",

}

TY - JOUR

T1 - NL4Py

T2 - Agent-based modeling in Python with parallelizable NetLogo workspaces

AU - Gunaratne, Chathika

AU - Garibay, Ivan

PY - 2021/12

Y1 - 2021/12

N2 - 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.

AB - 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.

KW - Agent-based modeling

KW - Complex adaptive systems

KW - NetLogo

KW - Parameter calibration

KW - Python

UR - http://www.scopus.com/inward/record.url?scp=85122757754&partnerID=8YFLogxK

U2 - 10.1016/j.softx.2021.100801

DO - 10.1016/j.softx.2021.100801

M3 - Article

AN - SCOPUS:85122757754

SN - 2352-7110

VL - 16

JO - SoftwareX

JF - SoftwareX

M1 - 100801

ER -

NL4Py: Agent-based modeling in Python with parallelizable NetLogo workspaces

Abstract

Funding

Keywords

Access to Document

Other files and links

Fingerprint

Cite this