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 implements a combined Protective Action Decision Model (PADM) and Protection Motivation Theory (PAM) model for human decision making regarding hazard mitigations. The model is developed and integrated into the MASON modeling framework. The ABM implements a hind-cast of Hurricane Sandy’s damage to Sea Bright, NJ and homeowner post-flood reconstruction decisions. It was validated against FEMA damage assessments and post-storm surveys (O’Neil 2017).
Our model allows simulating repeated conservation auctions in low-income countries. It is designed to assess policy-making by exploring the extent to which non-targeted repeated auctions can provide biodiversity conservation cost-effectively, while alleviating poverty. Targeting landholders in order to integrate both goals is claimed to be overambitious and underachieving because of the trade-offs they imply. The simulations offer insight on the possible outcomes that can derive from implementing conservation auctions in low-income countries, where landholders are likely to be risk averse and to face uncertainty.
Individually parameterized mussels (Mytilus californianus) recruit, grow, move and die in a 3D environment while facing predation (in the form of seastar agents), heat and desiccation with increased tide height, and storms. Parameterized with data collected by Wootton, Paine, Kandur, Donahue, Robles and others. See my 2019 CoMSES video presentation to learn more.
This generic model simulates climate change adaptation in the form of resistance, accommodation, and retreat in coastal regions vulnerable to sea level rise and flooding. It tracks how population changes as households retreat to higher ground.
Next generation of the CHALMS model applied to a coastal setting to investigate the effects of subjective risk perception and salience decision-making on adaptive behavior by residents.
The spatially-explicit AgriculTuralLandscApe Simulator (ATLAS) simulates realistic spatial-temporal crop availability at the landscape scale through crop rotations and crop phenology.