This is the R code of the mathematical model that includes the decision making formulations for artificial agents. Plus, the code for graphical output is also added to the original code.

An agent-based model which explores Creativity and Urban Development

The Sediba socio-ecolgoical rangeland model is an biomass growth model coupled with a social model of pastoralist behaviour in a commmon pool resource setting. The social subsystem is an empircal ABM.

The purpose of this agent-based model is to explore the emergent phenomena associated with scientific publication, including quantity and quality, from different academic types based on their publication strategies.

The purpose of the model is to examine whether and how mobile pastoralists are able to achieve an Ideal Free Distribution (IFD).

Three policy scenarios for urban expansion under the influences of the behaviours and decision modes of four agents and their interactions have been applied to predict the future development patterns of the Guangzhou metropolitan region.

Implemented as a virtual laboratory, this model explores transitions in land-use and livelihood decisions that emerge from changing local and global conditions.

Cultural group selection model used to evaluate the conditions for agents to evolve who have other-regarding preferences in making decisions in public good games.

Ants in the genus Temnothorax use tandem runs (rather than pheromone trails) to recruit to food sources. This model explores the collective consequences of this linear recruitment (as opposed to highly nonlinear pheromone trails).

We reconstruct Cohen, March and Olsen’s Garbage Can model of organizational choice as an agent-based model. We add another means for avoiding making decisions: buck-passing difficult problems to colleagues.