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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This model implements a Bayesian belief revision model that contrasts an ideal agent in possesion of true likelihoods, an agent using a fixed estimate of trusting its source of information, and an agent updating its trust estimate.
The mode implements a variant of Ant Colony Optimization to explore routing on infrastructures through a landscape with forbidden zones, connecting multiple sinks to one source.
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
Continuing on from the Adder model, this adaptation explores how rationality, learning and uncertainty influence the exploration of complex landscapes representing technological evolution.
One of four extensions to the standard Adder model that replicates a common type of transition experiment.
This is one of four extensions to the standard Adder model that replicate the various interventions typical of transition experiments.
One of four extensions to the standard Adder model that replicates the various interventions typically associated with transition experiments.
The fourth and final extension to the standard Adder model to replicate the various interventions typically associated with Transition Experiments.
The purpose of this model is to project the dynamics of technology adoption of autonomous weeding robots by sugar beet producing farmers in North Rhine-Westphalia (NRW). Moreover, the design of the model serves the purpose to investigate second-order effects of robot adoption on shifts in farm income and on production quantities of main crops produced in North Rhine-Westphalia. One aim is to analyse the impact of technology attributes and costs of pesticides on adoption patterns.
A dynamic model of social network formation on single-layer and multiplex networks with structural incentives that vary over time.
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