CoMSES Net maintains cyberinfrastructure to foster FAIR data principles for access to and (re)use of computational models. Model authors can publish their model code in the Computational Model Library with documentation, metadata, and data dependencies and support these FAIR data principles as well as best practices for software citation. Model authors can also request that their model code be peer reviewed to receive a DOI. All users of models published in the library must cite model authors when they use and benefit from their code.
CoMSES Net also maintains a curated database of over 7500 publications of agent-based and individual based models with additional metadata on availability of code and bibliometric information on the landscape of ABM/IBM publications that we welcome you to explore.
This model allows for analyzing the most efficient levers for enhancing the use of recycled construction materials, and the role of empirically based decision parameters.
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.
The simulation model conducts fine-grained population projection by specifying life course dynamics of individuals and couples by means of traditional demographic microsimulation and by using agent-based modeling for mate matching.
This is the R code of the mathematical model that includes the decision making formulations for artificial agents. Plus, the code for graphical output is also added to the original code.
The objective of the model is to evaluate the impact of seasonal forecasts on a farmer’s net agricultural income when their crop choices have different and variable costs and returns.
Implemented as a virtual laboratory, this model explores transitions in land-use and livelihood decisions that emerge from changing local and global conditions.
Simulates the construction of scientific journal publications, including authors, references, contents and peer review. Also simulates collective learning on a fitness landscape. Described in: Watts, Christopher & Nigel Gilbert (forthcoming) “Does cumulative advantage affect collective learning in science? An agent-based simulation”, Scientometrics.