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.

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

The model explores food distribution patterns that emerge in a small-scale non-agricultural group when sharing individuals engage in intentional consumption leveling with a given probability.

Model of influence of access to social information spread via social network on decisions in a two-person game.

An agent-based model of echo chamber formation employing a Bayesian Source Credibility cognitive architecture limiting interactions to a single cascade.

Comparing 7 alternative models of human behavior and assess their performance on a high resolution dataset based on individual behavior performance in laboratory experiments.

This simulation is of the 2003 Station Nightclub Fire and is part of the Interdependencies in Community Resilience (ICoR) project (http://www-personal.umich.edu/~eltawil/icor.html). The git contains the simulation as well as csvs of data about the fire, smoke, building, and people involved.

WatASit is an agent-based model implemented in the CORMAS plateform. The model is developped to simulate irrigation situations at the operational level during a collective irrigation campaign.

The BENCH agent-based model is designed and developed to study shifts in residential energy use and corresponding emissions driven by behavioral changes among heterogeneous individuals.

The purpose of the model is to generate the spatio-temporal distribution of bicycle traffic flows at a regional scale level. Disaggregated results are computed for each network segment with the minute time step. The human decision-making is governed by probabilistic rules derived from the mobility survey.