The model examines cattle herd dynamics on a patchy grassland subject to two exogenous pressures: periodic raiding events that remove animals and scheduled management culling that can target males and/or females. It is intended for comparative experiments on how raiding frequency, culling schedules, vegetation dynamics, and life-history parameters interact to shape herd persistence. The model was specifically designed to test the scenario of cattle herding in the arid grasslands of southern Arizona and northern Sonora during the mission period (late 17th through late 18th centuries, CE). In this period, herds were locally managed by Spanish mission personnel and local O’odham groups. Herds were culled mostly for local consumption of meat, hides, and tallow, but the mission herds were often targets for raiding by neighboring groups. The main purpose of the model is to examine herd dynamics in a seasonally variable, arid environment where herds are subject to both intentional internal harvest (culling) and external harvest (raiding).

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

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.

Investigate spatial adaptive behaviors of narco-trafficking networks in response to various counterdrug interdiction strategies within the cocaine transit zone of Central America and associated maritime areas. Through the novel application of the ‘complex adaptive systems’ paradigm, we implement a potentially transformative coupled agent-based and interdiction optimization modeling approach to compellingly demonstrate: (a) how current efforts to disrupt narco-trafficking networks are in fact making them more widespread, resilient, and economically powerful; (b) the potential for alternative interdiction approaches to weaken and contain traffickers.

This model was created to investigate the potential impacts of large-scale recreational and transport-related physical activity promotion strategies on six United Nations Sustainable Development Goals (SDGs) related outcomes—road traffic deaths (SDG 3), transportation mode share (SDG 9), convenient access to public transport, levels of fine particulate matter, and access to public open spaces (SDG 11), and levels of carbon dioxide emissions (SDG 13)—in three cities designed as abstract representations of common city types in high-, middle-, and low-income countries.

A spatio-temporal Agent Based Modeling (ABM) framework is developed to probabilistically predict farmers’ decisions in the context of climate-induced water scarcity under varying utility optimization functions. The proposed framework forecasts farmers’ behavior assuming varying utility functions. The framework allows decision makers to forecast the behavior of farmers through a user-friendly platform with clear output visualization. The functionality of the proposed ABM is illustrated in an agriculturally dominated plain along the Eastern Mediterranean coastline.

Study area GIS data available upon request to gxh00@mail.aub.edu

Development of a Multiagent System for the Analysis of Gentrification in Latin America, an Agent-Based Model

A system of nonlinear differential equations, modelled in MATLAB Simulink, simulating the world of George Orwell’s 1984.

Simulation of irrigation system management using archaeological data from southern Arizona

An ABM to simulate the spatio-temporal distribution of cyclists across the road network of the city of Salzburg.