This code simulates individual-level, longitudinal substance use patterns that can be used to understand how cross-sectional U-shaped distributions of population substance use emerge. Each independent computational object transitions between two states: using a substance (State 1), or not using a substance (State 2). The simulation has two core components. Component 1: each object is assigned a unique risk factor transition probability and unique protective factor transition probability. Component 2: each object’s current decision to use or not use the substance is influenced by the object’s history of decisions (i.e., “path dependence”).

An agent-based model of Thomas Kuhn’s Structure of Scientific Revolutions

This is the replication of the experiment performed by Eerkens and Lipo (2005) to look at the effect of copying errors when specific traits are transferred from an individual to another.

This model can be used to optimize intervention strategies for inspection services.

This is a simplified version of a Complex Model of Voter Turnout by Edmonds et al.(2014). It was developed to better understand the mechanisms at play on that complex model.

This model simulate product diffusion on different social network structures.

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

This model simulates the dynamics of eighteenth-century infantry battle tactics. The goal is to explore the effect of different tactics and individual traits in the dynamics of the combat.

An individual-based model to evaluate, whether time delays in plant responses to insect herbivory can be beneficial for the plant.

The simulation generates two kinds of agents, whose proposals are generated accordingly to their selfish or selfless behaviour. Then, agents compete in order to increase their portfolio playing the ultimatum game with a random-stranger matching.