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.
MELBIS-V1 is a spatially explicit agent-based model that allows the geospatial simulation of the decision-making process of newcomers arriving in the bilingual cities and boroughs of the island of Montreal, Quebec in CANADA, and the resulting urban segregation spatial patterns. The model was implemented in NetLogo, using geospatial raster datasets of 120m spatial resolution.
MELBIS-V2 enhances MELBIS-V1 to implement and simulate the decision-making processes of incoming immigrants, and to analyze the resulting spatial patterns of segregation as immigrants arrive and settle in various cities in Canada. The arrival and segregation of immigrants is modeled with MELBIS-V2 and compared for three major Canadian immigration gateways, including the City of Toronto, Metro Vancouver, and the City of Calgary.
The Urban Traffic Simulator is an agent-based model developed in the Unity platform. The model allows the user to simulate several autonomous vehicles (AVs) and tune granular parameters such as vehicle downforce, adherence to speed limits, top speed in mph and mass. The model allows researchers to tune these parameters, run the simulator for a given period and export data from the model for analysis (an example is provided in Jupyter Notebook).
The data the model is currently able to output are the following:
This is an agent-based model of a population of scientists alternatively authoring or reviewing manuscripts submitted to a scholarly journal for peer review. Peer-review evaluation can be either ‘confidential’, i.e. the identity of authors and reviewers is not disclosed, or ‘open’, i.e. authors’ identity is disclosed to reviewers. The quality of the submitted manuscripts vary according to their authors’ resources, which vary according to the number of publications. Reviewers can assess the assigned manuscript’s quality either reliably of unreliably according to varying behavioural assumptions, i.e. direct/indirect reciprocation of past outcome as authors, or deference towards higher-status authors.
this agent-based model explores the dynamics of volunteer participation in urban community gardens, by combining behavioral theory and institutional theory
Ecosystems are among the most complex structures studied. They comprise elements that seem both stable and contingent. The stability of these systems depends on interactions among their evolutionary history, including the accidents of organisms moving through the landscape and microhabitats of the earth, and the biotic and abiotic conditions in which they occur. When ecosystems are stable, how is that achieved? Here we look at ecosystem stability through a computer simulation model that suggests that it may depend on what constrains the system and how those constraints are structured. Specifically, if the constraints found in an ecological community form a closed loop, that allows particular kinds of feedback may give structure to the ecosystem processes for a period of time. In this simulation model, we look at how evolutionary forces act in such a way these closed constraint loops may form. This may explain some kinds of ecosystem stability. This work will also be valuable to ecological theorists in understanding general ideas of stability in such systems.
This model examines how financial and social top-down interventions interplay with the internal self-organizing dynamics of a fishing community. The aim is to transform from hierarchical fishbuyer-fisher relationship into fishing cooperatives.
This model is a replication model which is constructed based on the existing model used by the following article:
Brown, D.G. and Robinson, D.T., 2006. Effects of heterogeneity in residential preferences on an agent-based model of urban sprawl. Ecology and society, 11(1).
The original model is called SLUCE’s Original Model for Experimentation (SOME). In Brown and Robinson (2006)’s article, the SOME model was used to explore the impacts of heterogeneity in residential location selections on the research of urban sprawl. The original model was constructed using Objective-C language based on SWARM platform. This replication model is built by NetLogo language on NetLogo platform. We successfully replicate that model and demonstrated the reliability and replicability of it.
The Episim framework builds upon the established transportation simulation MATSim and is capable of tracking agents’ movements within a network and thus computing infection chains. Several characteristics of the virus and the environment can be parametred, whilst the infection dynamics is computed based upon a compartment model. The spread of the virus can be mitigated by restricting the agents’ activity in certain places.
The name of the model, CoComForest, stands for COllaborative COMmunity FOREST management. The purposes of this model are to expose local resource harvesters to the competition with external resource harvesters, called outsiders, and to provide them the opportunity to collectively discuss on resource management. The model, which is made of a set of interconnected entities, including (i) community forest habitat, (ii) resource harvesters, (iii) market, and (iv) firebreak. More details about the CoComForest model are described based on the Overview, Design concept, and Details (ODD) protocol uploaded with the model.
While the world’s total urban population continues to grow, not all cities are witnessing such growth, some are actually shrinking. This shrinkage causes several problems to emerge including population loss, economic depression, vacant properties and the contraction of housing markets. Such problems challenge efforts to make cities sustainable. While there is a growing body of work on study shrinking cities, few explore such a phenomenon from the bottom up using dynamic computational models. To overcome this issue this paper presents an spatially explicit agent-based model stylized on the Detroit Tri-county area, an area witnessing shrinkage. Specifically, the model demonstrates how through the buying and selling of houses can lead to urban shrinkage from the bottom up. The model results indicate that along with the lower level housing transactions being captured, the aggregated level market conditions relating to urban shrinkage are also captured (i.e., the contraction of housing markets). As such, the paper demonstrates the potential of simulation to explore urban shrinkage and potentially offers a means to test polices to achieve urban sustainability.