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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The model examines the dynamics of herd growth in African pastoral systems. We used it to examine the role of scale (herd size) stochasticity (in mortality, fertility, and offtake) on herd growth.
The aim of the model is to define when researcher’s assumptions of dependence or independence of cases in multiple case study research affect the results — hence, the understanding of these cases.
The code for the paper “Social norms and the dominance of Low-doers”
Evolution of Sex is a NetLogo model that illustrates the advantages and disadvantages of sexual and asexual reproductive strategies. It seeks to demonstrate the answer to the question “Why do we have sex?”
This model illustrates how the effective population size and the rate of change in mean skill level of a cultural trait are affected by the presence of natural selection and/or the cultural transmission mechanism by which it is passed.
It is NetLogo reconstruction of the original FORTRAN code of the classical M. Cohen, J. March, and J. Olsen “garbage can model” (GCM or CMO) of collective decision-making.
This Agent-Based model intends to explore the conditions for the emergence and change of land use patterns in Central Asian oases and similar contexts.
This models provides the infrastructure to model the activity of making. Individuals use resources they find in their environment plus those they buy, to design, construct and deconstruct items. It represents plans and complex objects explicitly.
Positive feedback can lead to “trapping” in local optima. Adding a simple negative feedback effect, based on ant behaviour, prevents this trapping
The model explores how two types of information - social (in the form of pheromone trails) and private (in the form of route memories) affect ant colony level foraging in a variable enviroment.
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