Ge, J., & Polhill, G. (2016). Exploring the Combined Impact of Factors Influencing Commuting Patterns and CO2 Emission in Aberdeen Using an Agent-Based Model. Journal of Artificial Societies and Social Simulation, 19(3). http://jasss.soc.surrey.ac.uk/19/3/11.html
We develop an agent-based transport model using a realistic GIS-enabled road network and the car following method. The model can be used to study the impact of social interventions such as flexi-time and workplace sharing, as well as large infrastructure such as the construction of a bypass or highway. The model is developed in Netlogo version 5 and requires road network data in GIS format to run.

The mode implements a variant of Ant Colony Optimization to explore routing on infrastructures through a landscape with forbidden zones, connecting multiple sinks to one source.

The model’s purpose is to provide a potential explanation for the emergence, sustenance and decline of unpopular norms based on pluralistic ignorance on a social network.

The purpose of the presented ABM is to explore how system resilience is affected by external disturbances and internal dynamics by using the stylized model of an agricultural land use system.

We explore land system resilience with a stylized land use model in which agents’ land use activities are affected by external shocks, agent interactions, and endogenous feedbacks. External shocks are designed as yield loss in crops, which is ubiquitous in almost every land use system where perturbations can occur due to e.g. extreme weather conditions or diseases. Agent interactions are designed as the transfer of buffer capacity from farmers who can and are willing to provide help to other farmers within their social network. For endogenous feedbacks, we consider land use as an economic activity which is regulated by markets — an increase in crop production results in lower price (a negative feedback) and an agglomeration of a land use results in lower production costs for the land use type (a positive feedback).

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.

Bayesian network is used to modelling the behavior of an individual level and multi-agent system is used to simulate the meme diffusion through the whole network.

This model represents informal information transmission networks among medieval Genoese investors used to inform each other about cheating merchants they employed as part of long-distance trade operations.

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

This is an adaptation and extension of Robert Axtell’s model (2013) of endogenous firms, in Python 3.4

We used a computer simulation to measure how well different network structures (fully connected, small world, lattice, and random) find and exploit resource peaks in a variable environment.