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 aims to mimic human movement on a realistic topographical surface. The agent does not have a perfect knowledge of the whole surface, but rather evaluates the best path locally, at each step, thus mimicking imperfect human behavior.
Risk assessments are designed to measure cumulative risk and promotive factors for delinquency and recidivism, and are used by criminal and juvenile justice systems to inform sanctions and interventions. Yet, these risk assessments tend to focus on individual risk and often fail to capture each individual’s environmental risk. This agent-based model (ABM) explores the interaction of individual and environmental risk on the youth. The ABM is based on an interactional theory of delinquency and moves beyond more traditional statistical approaches used to study delinquency that tend to rely on point-in-time measures, and to focus on exploring the dynamics and processes that evolve from interactions between agents (i.e., youths) and their environments. Our ABM simulates a youth’s day, where they spend time in schools, their neighborhoods, and families. The youth has proclivities for engaging in prosocial or antisocial behaviors, and their environments have likelihoods of presenting prosocial or antisocial opportunities.
A minimal genetic algorithm was preliminarily developed to search for the solution of an elementary arithmetic problem. It has been modified to explore the effect of a mutator gene and the consequent entrance into a hypermutation state. The phenomenon is particularly important in some types of tumorigenesis and in a more general way, in cells and tissues submitted to chronic sublethal environmental or genomic stress.
Since a long time, some scholars suppose that organisms speed up their own evolution by varying mutation rate, but evolutionary biologists are not convinced that evolution can select a mechanism promoting more (often harmful) mutations looking forward an environmental challenge.
The model aims to shed light on these controversial points of view and it provides also the features required to check the role of sex and genetic recombination in the mutator genes diffusion.
The Holmestrand model is an epidemiological agent-based model. Its aim is to test hypotheses related to how the social and physical environment of a residential school for children with disabilities might influence the spread of an infectious disease epidemic among students and staff. Annual reports for the Holmestrand School for the Deaf (Norway) are the primary sources of inspiration for the modeled school, with additional insights drawn from other archival records for schools for children with disabilities in early 20th century Norway and data sources for the 1918 influenza pandemic. The model environment consists of a simplified boarding school that includes residential spaces for students and staff, classrooms, a dining room, common room, and an outdoor area. Students and staff engage in activities reflecting hourly schedules suggested by school reports. By default, a random staff member is selected as the first case and is infected with disease. Subsequent transmission is determined by agent movement and interactions between susceptible and infectious pairs.
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 purpose of the model is to explore the influence of actor behaviour, combined with environment and business model design, on the survival rates of Industrial Symbiosis Networks (ISN), and the cash flows of the agents. We define an ISN to be robust, when it is able to run for 10 years, without falling apart due to leaving agents.
The model simulates the implementation of local waste exchange collaborations for compost production, through the ISN implementation stages of awareness, planning, negotiation, implementation, and evaluation.
One central firm plays the role of waste processor in a local composting initiative. This firm negotiates with other firms to become a supplier of their organic residual streams. The waste suppliers in the model can decide to join the initiative, or to have the waste brought to the external waste incinerator. The focal point of the model are the company-level interactions during the implementation or ending of synergies.
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:
The model simulates the national Campaign-Based Watershed Management program of Ethiopia. It includes three agents (farmers, Kebele/ village administrator, extension workers) and the physical environment that interact with each other. The physical environment is represented by patches (fields). Farmers make decisions on the locations of micro-watersheds to be developed, participation in campaign works to construct soil and water conservation structures, and maintenance of these structures. These decisions affect the physical environment or generate model outcomes. The model is developed to explore conditions that enhance outcomes of the program by analyzing the effect on the area of land covered and quality of soil and water conservation structures of (1) enhancing farmers awareness and motivation, (2) establishing and strengthening micro-watershed associations, (3) introducing alternative livelihood opportunities, and (4) enhancing the commitment of local government actors.
Like many developing countries, Nigeria is faced with a number of tradeoffs that pit rapid economic development against environmental preservation. Environmentally sustainable, “green” economic development is slower, more costly, and more difficult than unrestricted, unregulated economic growth. The mathematical model that we develop in this code suggests that widespread public awareness of environmental issues is insufficient to prevent the tendency towards sacrificing the environment for the sake of growth. Even if people have an understanding of negative impacts and always choose to act in their own self-interest, they may still act collectively in such a way as to bring down the quality of life for the entire society. We conclude that additional actions must be taken besides raising public awareness of the environmental problem.
AgentEx aims to advance understanding of group processes for sustainable management of a common pool resource (CPR). By supporting the development and test explanations of cooperation and sustainable exploitation.