This model is based on the Narragansett Bay, RI recreational fishery. The two types of agents are piscivorous fish and fishers (shore and boat fishers are separate “breeds”). Each time step represents one week. Open season is weeks 1-26, assuming fishing occurs during half the year. At each weekly time step, fish agents grow, reproduce, and die. Fisher agents decide whether or not to fish based on their current satisfaction level, and those that do go fishing attempt to catch a fish. If they are successful, they decide whether to keep or release the fish. In our publication, this model was linked to an Ecopath with Ecosim food web model where the commercial harvest of forage fish affected the biomass of piscivorous fish - which then became the starting number of piscivorous fish for this ABM. The number of fish caught in a season of this ABM was converted to a fishing pressure and input back into the food web model.

The model simulates agents behaviour in wine market parallel trading systems: auctions, OTC and Liv-ex. Models are written in JAVA and use MASON framework. To run a simulation download source files with additional src folder with sobol.csv file. In WineSimulation.java set RESULTS_FOLDER parameter. Uses following external libraries mason19..jar, opencsv.jar, commons-lang3-3.5.jar and commons-math3-3.6.1.jar.

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).

We used a computer simulation to measure how well different network structures (fully connected, small world, lattice, and random) find and exploit resource peaks in a variable environment.

A multi-agent model of oligarchy in a spatial election simulation.

A reimplementation of the Wedding Ring model by Francesco Billari. We investigate partnership formation in an agent-based framework, and combine this with statistical demographic projections using real empirical data.

This purpose of this model is to understand how the coupled demographic dynamics of herds and households constrain the growth of livestock populations in pastoral systems.

Leptospirosis is a neglected, bacterial zoonosis with worldwide distribution, primarily a disease of poverty. More than 200 pathogenic serovars of Leptospira bacteria exist, and a variety of species may act as reservoirs for these serovars. Human infection is the result of direct or indirect contact with Leptospira bacteria in the urine of infected animal hosts, primarily livestock, dogs, and rodents. There is increasing evidence that dogs and dog-adapted serovar Canicola play an important role in the burden of leptospirosis in humans in marginalized urban communities. What is needed is a more thorough understanding of the transmission dynamics of Leptospira in these marginalized urban communities, specifically the relative importance of dogs and rodents in the transmission of Leptospira to humans. This understanding will be vital for identifying meaningful intervention strategies.
One of the main objectives of MHMSLeptoDy is to elucidate transmission dynamics of host-adapted Leptospira strains in multi-host system. The model can also be used to evaluate alternate interventions aimed at reducing human infection risk in small-scale communities like urban slums.

Our model is hybrid agent-based and equation based model for human air-borne infectious diseases measles. It follows an SEIR (susceptible, exposed,infected, and recovered) type compartmental model with the agents moving be-tween the four state relating to infectiousness. However, the disease model canswitch back and forth between agent-based and equation based depending onthe number of infected agents. Our society model is specific using the datato create a realistic synthetic population for a county in Ireland. The modelincludes transportation with agents moving between their current location anddesired destination using predetermined destinations or destinations selectedusing a gravity model.

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:

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