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.

Using nodes from the 2002 General Social Survey sample, the code establishes a network of ties with a given homophily bias, and simulates Internet adoption rates in that network under three conditions: (i) no network externalities, (ii) general network externalities, where an individual’s reservation price is a function of the overall adoption rate in the network, (iii) specific network externalities, where reservation price is a function of the adoption rate in individual’s personal […]

This is an implementation of an agent based model for the evolution of ethnocentrism. While based off a model published by Hammond and Axelrod (2006), the code has been modified to allow for a more fine-grained analysis of evolutionary dynamics.

The WaterScape is an agent-based model of the South African water sector. This version of the model focuses on potential barriers to learning in water management that arise from interactions between human perceptions and social-ecological system conditions.

An agent-based model of the Free/Libre Open Source Software (FLOSS) development process designed around agents selecting FLOSS projects to contribute to and/or download.

We present an agent-based model that maps out and simulates the processes by which individuals within ecological restoration organizations communicate and collectively make restoration decisions.

In this model agents meet, evaluate one another, decide whether or not to date, if and when to become sexual partners, and when to break up.

This models simulates innovation diffusion curves and it tests the effects of the degree and the direction of social influences. This model replicates, extends and departs from classical percolation models.

This model simulates diffusion curves and it allows to test how social influence, network structure and consumer heterogeneity affect their spreads and their speeds.

This model is intended to explore the effectiveness of different courses of interventions on an abstract population of infections. Illustrative findings highlight the importance of the mechanisms for variability and mutation on the effectiveness of different interventions.