This model describes a mechanism by which software agents can identify norms in an artificial agent society. In particular, this model uses a sequence mining approach to identify norms in an agent soc

The purpose of this model is to explore the influence of integrating individuals’ behavioral dynamics in an agent-based model of COVID-19, on the dynamics of disease transmission. The model is an agent-based extention of an established large-scale Individual-based model called STRIDE. Four risk factors determine the individual’s perception of the risk and how they behave accordingly. It is assumed that individuals with higher levels of risk perception adopt higher levels of contact reduction in their daily routines. Individuals can assign different weights to any of the four different risk factors, i.e., the modeler can model different populations and explore how the transmission dynamics vary among them.

FoxNet is an individual-based modelling framework that can be customised to generate high-resolution red fox Vulpes vulpes population models for both northern and southern hemispheres. FoxNet predicts red fox population dynamics, including responses to control and landscape productivity. Model landscapes (up to ~15,000 km^2 and bait layouts can be generated within FoxNet or imported as GIS layers.

If you use FoxNet, please cite:

Hradsky BA, Kelly L, Robley A, Wintle BA (in review). FoxNet: an individual-based modelling framework to support red fox management. Journal of Applied Ecology.

RHEA aims to provide a methodological platform to simulate the aggregated impact of households’ residential location choice and dynamic risk perceptions in response to flooding on urban land markets. It integrates adaptive behaviour into the spatial landscape using behavioural theories and empirical data sources. The platform can be used to assess: how changes in households’ preferences or risk perceptions capitalize in property values, how price dynamics in the housing market affect spatial demographics in hazard-prone urban areas, how structural non-marginal shifts in land markets emerge from the bottom up, and how economic land use systems react to climate change. RHEA allows direct modelling of interactions of many heterogeneous agents in a land market over a heterogeneous spatial landscape. As other ABMs of markets it helps to understand how aggregated patterns and economic indices result from many individual interactions of economic agents.
The model could be used by scientists to explore the impact of climate change and increased flood risk on urban resilience, and the effect of various behavioural assumptions on the choices that people make in response to flood risk. It can be used by policy-makers to explore the aggregated impact of climate adaptation policies aimed at minimizing flood damages and the social costs of flood risk.

This is the electronic companion to the paper “Modelling Electricity Consumption in Office Buildings: An Agent Based Approach”

Several taxonomies for empirical validation have been published. Our model integrates different methods to calibrate an innovation diffusion model, ranging from simple randomized input validation to complex calibration with the use of microdata.

This model is based on Joshua Epstein’s (2001) model on development of thoughtless conformity in an artificial society of agents.

This agent-based model represents a stylized inter-organizational innovation network where firms collaborate with each other in order to generate novel organizational knowledge.

The model was built to study the links between consumer credit, wealth distribution and aggregate demand in a complex macroeconomics system.

This model is a replication model which is constructed based on the existing model used by the following article:
Brown, D.G. and Robinson, D.T., 2006. Effects of heterogeneity in residential preferences on an agent-based model of urban sprawl. Ecology and society, 11(1).
The original model is called SLUCE’s Original Model for Experimentation (SOME). In Brown and Robinson (2006)’s article, the SOME model was used to explore the impacts of heterogeneity in residential location selections on the research of urban sprawl. The original model was constructed using Objective-C language based on SWARM platform. This replication model is built by NetLogo language on NetLogo platform. We successfully replicate that model and demonstrated the reliability and replicability of it.