The CHIME ABM explores information distribution networks and agents’ protective decision making in the context of hurricane landfall.

This multi-model (i.e. a model composed of interacting submodels) is a multi-level representation of a collective motion phenomenon. It was designed to study the impact of the mutual influences between individuals and groups in collective motion.

This is the same model as used in the article ‘Modelling Society’s Evolutionary Forces’ except the Fertility graph has been corrected. The Fertility graph was not used in the published article.

This is a multi-patch meta-population ecological model. It intended as a test-bed in which to test the impact of humans with different kinds of social structure.

A multi-agent model of oligarchy in a spatial election simulation.

Designed to capture the evolutionary forces of global society.

The various technologies used inside a Dutch greenhouse interact in combination with an external climate, resulting in an emergent internal climate, which contributes to the final productivity of the greenhouse. This model examines how differing technology development styles affects the overall ability of a community of growers to approach the theoretical maximum yield.

In the context switching model, a society of agents embedded in multiple social relations, engages in a simple abstract game: the consensus game. Each agent has to choose towards one of two possible choices which are basically arbitrary. The objective of the game is to reach a global consensus, but the particular choice that gets collectively selected is irrelevant.

This is a simple model replicating Hardin’s Tragedy of the Commons using reactive agents that have psychological behavioral and social preferences.

This model explores the effects of agent interaction, information feedback, and adaptive learning in repeated auctions for farmland. It gathers information for three types of sealed-bid auctions, and one English auction and compares the auctions on the basis of several measures, including efficiency, price information revelation, and ability to handle repeated bidding and agent learning.