About the CoMSES Model Library more info
Our mission is to help computational modelers develop, document, and share their computational models in accordance with community standards and good open science and software engineering practices. Model authors can publish their model source code in the Computational Model Library with narrative documentation as well as metadata that supports open science and emerging norms that facilitate software citation, computational reproducibility / frictionless reuse, and interoperability. Model authors can also request private peer review of their computational models. Models that pass peer review receive a DOI once published.
All users of models published in the library must cite model authors when they use and benefit from their code.
Please check out our model publishing tutorial and feel free to contact us if you have any questions or concerns about publishing your model(s) in the Computational Model Library.
We also maintain a curated database of over 7500 publications of agent-based and individual based models with detailed metadata on availability of code and bibliometric information on the landscape of ABM/IBM publications that we welcome you to explore.
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Ring Around the Kula: The Influence of Ceremonial Exchange on Network Formation
Andrea Tovinen | Published Tuesday, December 16, 2008 | Last modified Saturday, April 27, 2013The purpose of the model is to examine the strength of network connections in a ceremonial exchange network in a non-hierarchical society.
SugarscapeCW
Christopher Watts | Published Saturday, August 01, 2015 | Last modified Wednesday, April 12, 2023A replication in Netlogo 5.2 of the classic model, Sugarscape (Epstein & Axtell, 1996).
Peer reviewed Dawkins Weasel
Kristin Crouse | Published Thursday, February 08, 2018 | Last modified Tuesday, February 04, 2020Dawkins’ Weasel is a NetLogo model that illustrates the principle of evolution by natural selection. It is inspired by a thought experiment presented by Richard Dawkins in his book The Blind Watchmaker (1996).
Virus Spread on Broad-degree-distribution Network.
Gianluca Manzo | Published Friday, July 24, 2020The model simulates the spread of a virus through a synthetic network with a degree distribution calibrated on close-range contact data. The model is used to study the macroscopic consequences of cross-individual variability in close-range contact frequencies and to assess whether this variability can be exploited for effective intervention targeting high-contact nodes.
SEDIBASES
Sebastian Rasch | Published Monday, October 20, 2014The Sediba socio-ecolgoical rangeland model is an biomass growth model coupled with a social model of pastoralist behaviour in a commmon pool resource setting. The social subsystem is an empircal ABM.
Agent-based Simulation of Time Management
Hang Xiong | Published Thursday, March 24, 2016 | Last modified Friday, March 25, 2016This model simulates how the strategy one manages time affect the well-being that he/she can obtain.
Perspectives on the Information-Based Economy
Vladimir Gazda Jana Zausinova Marcel Volosin | Published Monday, October 24, 2022This is the agent-based model of information market evolution. It simulates the influences of the transition from material to electronic carriers of information, which is modelled by the falling price of variable production factor. It demonstrates that due to zero marginal production costs, the competition increases, the market becomes unstable, and experience various phases of evolution leading to market monopolization.
Simulating Knowledge Dynamics in Innovation Networks
Nigel Gilbert | Published Wednesday, March 03, 2010 | Last modified Saturday, April 27, 20133.8 with Unis
Sensitivity of the Deffuant model to measurement error
Dino Carpentras | Published Monday, June 07, 2021This code can be used to analyze the sensitivity of the Deffuant model to different measurement errors. Specifically to:
- Intrinsic stochastic error
- Binning of the measurement scale
- Random measurement noise
- Psychometric distortions
…
Introductory SIR Model
Kit Martin Amber Cesare Matthew Johnson | Published Tuesday, September 28, 2021This is a basic Susceptible, Infected, Recovered (SIR) model. This model explores the spread of disease in a space. In particular, it explores how changing assumptions about the number of susceptible people, starting number of infected people, as well as the disease’s infection probability, and average duration of infection. The model shows that the interactions of agents can drastically affect the results of the model.
We used it in our course on COVID-19: https://www.csats.psu.edu/science-of-covid19
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