The core algorithm is an agent-based model, which simulates travel patterns on a network based on microscopic decision-making by each traveler.

We built a model using R,polr package, to assess 55 published case studies from developing countries to determine what factors influence the level of compliance of local communities with protected area regulations.

The model is designed to simulate the behavior and decision-making processes of individuals (agents) in a social network. It aims to represent the changes in individual probability to take any action based on changes in attributes. The action is anything that can be reasonably influenced by the three influencing methods implemented in this model: peer pressure, social media, and state campaigns, and for which the user has a decision-making model. The model is implemented in the multi-agent programmable environment NetLogo 6.3.0.

In this model, we simulate the navigation behavior of homing pigeons. Specifically we use genetic algorithms to optimize the navigation and flocking parameters of pigeon agents.

MASTOC is a replication of the Tragedy of the Commons by G. Hardin, programmed in NetLogo 4.0.4, based on behavioral game theory and Nash solution.

This model looks at the effects of a “control” on agent populations. Much like farmers spraying pesticides/herbicides to manage pest populations, the user sets a control management regiment to be use

Demographic microsimulation model used in speed tests against LIAM 2.

The model investigates conditions, scenarios and strategies for future planning of energy in Egypt, with an emphasis on alternative energy pathways and a sustainable electricity supply mix as part of an energy roadmap till the year 2100. It combines the multi-criteria decision analysis (MCDA) with agent-based modeling (ABM) and Geographic Information Systems (GIS) visualization to integrate the interactions of the decisions of multi-agents, the multi-criteria evaluation of sustainability, the time factor and the site factors to assess the transformation of energy landscapes.

MHCABM is an agent-based, multi-hazard risk interaction model with an integrated applied dynamic adaptive pathways planning component. It is designed to explore the impacts of climate change adaptation decisions on the form and function of a coastal human-environment system, using as a case study an idealised patch based representation of the Mount North-Omanu area of Tauranga city, New Zealand. The interacting hazards represented are erosion, inundation, groundwater intrusion driven by intermittent heavy rainfall / inundations (storm) impacts, and sea level rise.

Our model shows how disinformation spreads on a random network of individuals. The network is weighted and directed. We are looking at how different factors affect how fast, or how many people get “infected” with the misinformation. One of the main factors that we were curious about was perceived trustworthiness. This is because we want to see if people of power, or a high degree of perceived trustworthiness, were able to push misinformation to more people and convert more people to believe the information.