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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PopComp
Andre Costopoulos | Published Thursday, December 10, 2020PopComp by Andre Costopoulos 2020
andre.costopoulos@ualberta.ca
Licence: DWYWWI (Do whatever you want with it)
I use Netlogo to build a simple environmental change and population expansion and diffusion model. Patches have a carrying capacity and can host two kinds of populations (APop and BPop). Each time step, the carrying capacity of each patch has a given probability of increasing or decreasing up to a maximum proportion.
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ManPest
François Rebaudo | Published Tuesday, February 25, 2014 | Last modified Wednesday, August 27, 2014The purpose of the model is to explore the impacts of global change on the ability of a community of farmers to adapt their practices to an agricultural pest.
Due to teacher requests to represent changes in atomic radius, we developed a visualization of the first 36 elements in Netlogo
Group assortment with preference rankings
Fredrik Jansson | Published Thursday, July 14, 2016 | Last modified Monday, April 09, 2018This model uses preference rankings w.r.t. ethnic group compositions (e.g. at companies) and assigns ethnic agents to groups based on their rankings.
HMODEL: an exploratory simulation of surface archaeological formation
Benjamin Davies Simon Holdaway Patricia Fanning | Published Thursday, November 30, 2017This model is used to simulate the influence of spatially and temporally variable sedimentary processes on the distribution of dated archaeological features in a surface context.
Urban Dynamics
Hideyuki Nagai | Published Monday, November 11, 2019This is an urban dynamics ABM of abstraction of a city and residents’ activities there.
It allows you to evaluate the effects of urban policies, such as an introduction of an open facility for residents with pedestrian-friendly accommodations, promotion of bicycle use, and control of private automobile use in an urban central area, in controlling urban sprawl.
The Effect of Different Governmental Pandemic Control Measures on the Spread of a Virus Disease
Chiara Letter | Published Wednesday, January 26, 2022In this model, the spread of a virus disease in a network consisting of school pupils, employed, and umemployed people is simulated. The special feature in this model is the distinction between different types of links: family-, friends-, school-, or work-links. In this way, different governmental measures can be implemented in order to decelerate or stop the transmission.
This is a social trust model for investigating the social relationships and social networks in the real world and in social media.
Life Cycle Cost of Military Manpower Model
Jonathan Ozik Todd Combs | Published Monday, January 05, 2015We demonstrate how Repast Simphony statecharts can efficiently encapsulate the deep classification hierarchy of the U.S. Air Force for manpower life cycle costing.
Managing ecological disturbances: Learning and the structure of social-ecological networks
Jacopo A. Baggio Vicken Hillis | Published Friday, March 03, 2017 | Last modified Thursday, August 02, 2018The aim of this model is to explore and understand the factors driving adoption of treatment strategies for ecological disturbances, considering payoff signals, learning strategies and social-ecological network structure
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