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 objective of the model is to evaluate the impact of seasonal forecasts on a farmer’s net agricultural income when their crop choices have different and variable costs and returns.

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

Simulates the construction of scientific journal publications, including authors, references, contents and peer review. Also simulates collective learning on a fitness landscape. Described in: Watts, Christopher & Nigel Gilbert (forthcoming) “Does cumulative advantage affect collective learning in science? An agent-based simulation”, Scientometrics.

Positive feedback can lead to “trapping” in local optima. Adding a simple negative feedback effect, based on ant behaviour, prevents this trapping

CHALMS simulates housing and land market interactions between housing consumers, developers, and farmers in a growing ex-urban area.

The model is an experimental ground to study the impact of network structure on diffusion. It allows to construct a social network that already has some measurable level of homophily, and simulate a diffusion process over this social network.

To investigate the potential of using Social Psychology Theory in ABMs of natural resource use and show proof of concept, we present an exemplary agent-based modelling framework that explicitly represents multiple and hierarchical agent self-concepts

This model contains source code and a technical appendix for the paper “Effects of Resource Availability on Consensus Decision Making in Primates”.

An ABM, derived from a case study and a series of surveys with greenhouse growers in the Westland, Netherlands. Experiments using this model showshow that the greenhouse horticulture industry displays diversity, adaptive complexity and an uneven distribution, which all suggest that the industry is an evolving system.