Pandemic (pip install pandemic)

An agent model in which commuting, compliance, testing and contagion parameters drive infection in a population of thousands of millions. Agents follow Ornstein-Uhlenbeck processes in the plane and collisions drive transmission. Results are stored at SwarmPrediction.com for further analysis, and can be retrieved by anyone.

This is a very simple simulation that in a special case can be shown to be approximated by a compartmental model with time varying infection rate.

This is a basic Susceptible, Infected, Recovered (SIR) model. This model explores the spread of disease in a space. In particular, it explores how changing assumptions about the number of susceptible people, starting number of infected people, as well as the disease’s infection probability, and average duration of infection. The model shows that the interactions of agents can drastically affect the results of the model.

We used it in our course on COVID-19: https://www.csats.psu.edu/science-of-covid19

Machine learning technologies have changed the paradigm of knowledge discovery in organizations and transformed traditional organizational learning to human-machine hybrid intelligent organizational learning. However, it remains unclear how human-machine trust, which is an important factor that influences human-machine knowledge exchange, affects the effectiveness of human-machine hybrid intelligent organizational learning. To explore this issue, we used multi-agent simulation to construct a knowledge learning model of a human-machine hybrid intelligent organization with human-machine trust.

This model is pertinent to our JASSS publication “Raising the Spectrum of Polarization: Generating Issue Alignment with a Weighted Balance Opinion Dynamics Model”. It shows how, based on the mechanisms of our Weighted Balance Theory (a development of Fritz Heider’s Cognitive Balance Theory), agents can self-organize in a multi-dimensional opinion space and form an emergent ideological spectrum. The degree of issue alignment and polarization realized by the model depends mainly on the agent-specific ‘equanimity parameter’ epsilon.

This is an agent-based model with two types of agents: customers and insurers. Insurers are price-takers who choose how much to spend on their service quality, and customers evaluate insurers based on premium, brand preference, and their perceived service quality. Customers are also connected in a small-world network and may share their opinions with their network.

The ABM contains two types of agents: insurers and customers. These act within the environment of a motor insurance market. At each simulation, the model undergoes the following steps:

1. Network generation: At the start of the simulation, the model generates a small world network of social links between the customers, and randomly assigns each customer to an initial insurer
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Diet breadth is a classic optimal foraging theory (OFT) model from human behavioral ecology (HBE). Different resources, ranked according to their food value and processing costs, are distributed in th

We demonstrate how a simple model of community associated Methicillin-resistant Staphylococcus aureus (CA-MRSA) can be easily constructed by leveraging the statecharts and ReLogo capabilities in Repast Simphony.

The model is about customers going to a restaurant when they are hungry. They wait in the queue if no tables are available. Customers can leave the restaurant and got upset and decide to never return to the restaurant. The model tries to show 2 things: 1.the main caracteristics of the people that decided to never return to the restaurant and 2.the main factors that can impact the total number of customers that decided to never return to the restaurant.

Agers and non-agers agent compete over a spatial landscape. When two agents occupy the same grid, who will survive is decided by a random draw where chances of survival are proportional to fitness. Agents have offspring each time step who are born at a distance b from the parent agent and the offpring inherits their genetic fitness plus a random term. Genetic fitness decreases with time, representing environmental change but effective non-inheritable fitness can increase as animals learn and get bigger.

Simulation-Framework to study the governance of complex, network-like sociotechnical systems by means of ABM. Agents’ behaviour is based on a sociological model of action. A set of basic governance mechanisms helps to conduct first experiments.