Our mission is to help computational modelers at all levels engage in the establishment and adoption of community standards and good practices for developing and sharing computational models. Model authors can freely publish their model source code in the Computational Model Library alongside narrative documentation, open science metadata, and other emerging open science norms that facilitate software citation, reproducibility, interoperability, and reuse. Model authors can also request peer review of their computational models to receive a DOI.
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 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 additional 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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Using data from the British Social Attitude Survey, we develop an agent-based model to study the effect of social influence on the spread of meat-eating behaviour in the British population.
This is a model of root disease spread between trees in the landscape. The disease spreads via two transmission processes: (a) root contact/root graft transmission between adjacent trees and (b) insect vectors that carry spores between trees. Full details can be found in the “Info” tab in the model and in the readme file in the GitHub repository.
BorealFireSIM is a cellular automaton based model that serves to identify future fire patterns in the boreal forest of Quebec, Canada. The model simulates yearly fire seasons and adjusts decadal climate variables based on two future carbon pathways (RCP45 (low emissions) and RCP85 (business as usual)). The BorealFireSIM model simulates future fire patterns up to the year 2100.
A dynmaic microsimulation model to project the UK population over time
RefugeePathSIM is an agent-based model to simulate the movement behavior of refugees in order to identify pathways of forced migration under crisis. The model generates migrants and lets them leave conflict areas for a destination that they choose based on their characteristics and desires. RefugeePathSIM has been developed and applied in a study of the Syrian war, using monthly data in years 2011-2015.
Aqua.MORE (Agent-based MOdelling of REsources in Socio-Hydrological Systems) is an agent based modelling (ABM) approach to simulate the resource flow and social interaction in a coupled natural and social system of water supply and demand. The model is able to simulate the two-way feedback as socio-economic agents influence the natural resource flow and the availability of this resource influences the agents in their behaviour.
Genetic algorithms try to solve a computational problem following some principles of organic evolution. This model has educational purposes; it can give us an answer to the simple arithmetic problem on how to find the highest natural number composed by a given number of digits. We approach the task using a genetic algorithm, where the candidate solutions to the problem are represented by agents, that in logo programming environment are usually known as “turtles”.
An agent-based model simulating West Nile Virus dynamics in a one host (American robin)-one vector (Culex spp. mosquito) system. ODD improved and code cleaned.
To investigate the potential of using Social Psychology Theory in ABMs of natural resource use and show proof of concept, we present an exemplary agent-based modelling framework that explicitly represents multiple and hierarchical agent self-concepts
The code shared here accompanies the paper at https://doi.org/10.1371/journal.pone.0208451. It simulates the effects of various economic trade scenarios on the phenomenon of the ‘disappearing middle’ in the Scottish beef and dairy farming industries. The ‘disappearing middle’ is a situation in which there is a simultaneous observed decline in medium-sized enterprises and rise in the number of small and large-scale enterprises.
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