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.
Please check out our model archive tutorial or contact us if you have any questions or concerns about archiving your model.
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.
At the heart of a study of Social-Ecological Systems, this model is built by coupling together two independently developed models of social and ecological phenomena. The social component of the model is an abstract model of interactions of a governing agent and several user agents, where the governing agent aims to promote a particular behavior among the user agents. The ecological model is a spatial model of spread of the Mountain Pine Beetle in the forests of British Columbia, Canada. The coupled model allowed us to simulate various hypothetical management scenarios in a context of forest insect infestations. The social and ecological components of this model are developed in two different environments. In order to establish the connection between those components, this model is equipped with a ‘FlipFlop’ - a structure of storage directories and communication protocols which allows each of the models to process its inputs, send an output message to the other, and/or wait for an input message from the other, when necessary. To see the publications associated with the social and ecological components of this coupled model please see the References section.
The purpose of this model is to explain the post-disaster recovery of households residing in their own single-family homes and to predict households’ recovery decisions from drivers of recovery. Herein, a household’s recovery decision is repair/reconstruction of its damaged house to the pre-disaster condition, waiting without repair/reconstruction, or selling the house (and relocating). Recovery drivers include financial conditions and functionality of the community that is most important to a household. Financial conditions are evaluated by two categories of variables: costs and resources. Costs include repair/reconstruction costs and rent of another property when the primary house is uninhabitable. Resources comprise the money required to cover the costs of repair/reconstruction and to pay the rent (if required). The repair/reconstruction resources include settlement from the National Flood Insurance (NFI), Housing Assistance provided by the Federal Emergency Management Agency (FEMA-HA), disaster loan offered by the Small Business Administration (SBA loan), a share of household liquid assets, and Community Development Block Grant Disaster Recovery (CDBG-DR) fund provided by the Department of Housing and Urban Development (HUD). Further, household income determines the amount of rent that it can afford. Community conditions are assessed for each household based on the restoration of specific anchors. ASNA indexes (Nejat, Moradi, & Ghosh 2019) are used to identify the category of community anchors that is important to a recovery decision of each household. Accordingly, households are indexed into three classes for each of which recovery of infrastructure, neighbors, or community assets matters most. Further, among similar anchors, those anchors are important to a household that are located in its perceived neighborhood area (Moradi, Nejat, Hu, & Ghosh 2020).
RefugeePathSIM is an agent-based model to simulate the movement behavior of refugees in order to identify pathways of forced migration under crisis. The model generates migrants and lets them leave conflict areas for a destination that they choose based on their characteristics and desires. RefugeePathSIM has been developed and applied in a study of the Syrian war, using monthly data in years 2011-2015.