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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A general model of human mate choice in which agents are localized in space, interact with close neighbors, and tend to range either near or far. At the individual level, our model uses two oft-used but incompletely understood decision rules: one based on preferences for similar partners, the other for maximally attractive partners.
This is the R code of the mathematical model that includes the decision making formulations for artificial agents. This code corresponds to equations 1-70 given in the paper “A Mathematical Model of The Beer Game”.
This is a model of coherency based belief within a dynamic network of individuals. Described in an invited talk on workshop on Coherence, Berlin, 9th July 2016.
The model simulates agents in a spatial environment competing for a common resource that grows on patches. The resource is converted to energy, which is needed for performing actions and for surviving.
Comparing 7 alternative models of human behavior and assess their performance on a high resolution dataset based on individual behavior performance in laboratory experiments.
The model includes different formulations how agents make decisions in irrigation games and this is compared with empirical data. The aim is to test different theoretical models, especially explaining effect of communication.
In this Repast model the ‘Consumat’ cognitive framework is applied to an ABM of the Dutch car market. Different policy scenarios can be selected or created to examine their effect on the diffusion of EVs.
The model simulates interaction between internal physiological factors (e.g. energy balance) and external social factors (e.g. competition level) underlying feeding and social interaction behaviour of commercially group-housed pigs.
At the heart of a study of Social-Ecological Systems, this model is built by coupling together two independently developed models of social and ecological phenomena. The social component of the model is an abstract model of interactions of a governing agent and several user agents, where the governing agent aims to promote a particular behavior among the user agents. The ecological model is a spatial model of spread of the Mountain Pine Beetle in the forests of British Columbia, Canada. The coupled model allowed us to simulate various hypothetical management scenarios in a context of forest insect infestations. The social and ecological components of this model are developed in two different environments. In order to establish the connection between those components, this model is equipped with a ‘FlipFlop’ - a structure of storage directories and communication protocols which allows each of the models to process its inputs, send an output message to the other, and/or wait for an input message from the other, when necessary. To see the publications associated with the social and ecological components of this coupled model please see the References section.
This ABM aims to introduce a new individual decision-making model, BNE into the ABM of pedestrian evacuation to properly model individual behaviours and motions in emergency situations. Three types of behavioural models has been developed, which are Shortest Route (SR) model, Random Follow (RF) model, and BNE model, to better reproduce evacuation dynamics in a tunnel space. A series of simulation experiments were conducted to evaluate the simulating performance of the proposed ABM.
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