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 is an agent-based model that allows to test alternative designs for three model components. The model was built using the LUDAS design strategy, while each alternative is in line with the strategy. Using the model, it can be shown that alternative designs, though built on the same strategy, lead to different land-use patterns over time.
We used a computer simulation to measure how well different network structures (fully connected, small world, lattice, and random) find and exploit resource peaks in a variable environment.
The purpose of this model is to help understand how prehistoric societies adapted to the prehistoric American southwest landscape. In the American southwest there is a high degree of environmental var
This model extends the bounded confidence model of Deffuant and Weisbuch. It introduces online contexts in which a person can deliver his or her opinion to several other persons. There are 2 additional parameters accessibility and connectivity.
This model studies the emergence and dynamics of generalized trust. It does so by modeling agents that engage in trust games and, based on their experience, slowly determine whether others are, in general, trustworthy.
Continuing on from the Adder model, this adaptation explores how rationality, learning and uncertainty influence the exploration of complex landscapes representing technological evolution.
We investigate the interplay of homophily, differentiation, and in-group cooperation mechanisms on the formation of opinion clusters and emergence of radical opinions.