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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FIBE represents a simple fishery model. Fish that reproduce and fisher with different fishing styles that fish as their main source of income. The aim of the model is to reflect the different fishing behaviours as described and observed in the (Swedish) Baltic Sea fishery and explore the consequences of different approximations of human/fisher behaviour in under different environmental and managerial scenarios.
The overarching aim is to advance the incorporation and understanding of human behaviour (diversity) in fisheries research and management. In particular focusing on insights from social (fishery) science of fisher behaviour.
A logging agent builds roads based on the location of high-value hotspots, and cuts trees based on road access. A forest monitor sanctions the logger on observed infractions, reshaping the pattern of road development.
An individual-based model to evaluate, whether time delays in plant responses to insect herbivory can be beneficial for the plant.
The model explores the impact of journal metrics (e.g., the notorious impact factor) on the perception that academics have of an article’s scientific value.
CRESY-I stands for CREativity from a SYstems perspetive, Model I. This is the base model in a series designed to describe a systems approach to creativity in terms of variation, selection and retention (VSR) subprocesses.
The set of models test how receivers ability to accurately rank signalers under various ecological and behavioral contexts.
This model employs optimal foraging theory principles to generate predictions of which coastal habitats are exploited in climatically stable versus variable environments, using the American Samoa as a study area.
This is a computational model to articulate the theory and test some assumption and axioms for the trust model and its relationship to SBH.
A discrete-time stochastic model with state-dependent transmission probabilities and multi-agent simulations focusing on possible risks that could materialize in the final phase of the epidemic.
An empirically validated agent-based model of circular migration
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