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 simulates different spread hypotheses proposed for the introduction of agriculture on the Iberian peninsula. We include three dispersal types: neighborhood, leapfrog, and ideal despotic distribution (IDD).
This is a model of root disease spread between trees in the landscape. The disease spreads via two transmission processes: (a) root contact/root graft transmission between adjacent trees and (b) insect vectors that carry spores between trees. Full details can be found in the “Info” tab in the model and in the readme file in the GitHub repository.
This model aims to investigate how different type of learning (social system) and disturbance specific attributes (ecological system) influence adoption of treatment strategies to treat the effects of ecological disturbances.
The model explores how corruption may spread endogenously within a closed society by depicting the behavior within a cellular automaton context (CA) between bureaucrats and citizens. Within the model, corruption is characterized as a behavior product dependent upon an individual’s personal disposition towards honesty, rational decisionmaking processes, and neighbors’ behavior.
The model analyzes the economic and ecological effects of a provision of livestock drought insurance for dryland pastoralists. More precisely, it yields qualitative insights into how long-term herd and pasture dynamics change through insurance.
The purpose of the model is to simulate the spatial dynamics of potato late blight to analyse whether resistant varieties can be used effectively for sustainable disease control. The model represents an agricultural landscape with potato fields and data of a Dutch agricultural region is used as input for the model. We simulated potato production, disease spread and pathogen evolution during the growing season (April to September) for 36 years. Since late blight development and crop growth is weather dependent, measured weather data is used as model input. A susceptible and late blight resistant potato variety are distinguished. The resistant variety has a potentially lower yield but cannot get infected with the disease. However, during the growing season virulent spores can emerge as a result of mutations during spore production. This new virulent strain is able to infect the resistant fields, resulting in resistance breakdown. The model shows how disease severity, resistance durability and potato yield are affected by the fraction of fields across a landscape with a disease-resistant potato variety.
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.
The purpose of the model is to explore how the unique socioeconomic variables underlying Kibera, local interactions, and the spread of a rumor, may trigger a riot.
The core algorithm is an agent-based model, which simulates travel patterns on a network based on microscopic decision-making by each traveler.