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.

The name of the model, CoComForest, stands for COllaborative COMmunity FOREST management. The purposes of this model are to expose local resource harvesters to the competition with external resource harvesters, called outsiders, and to provide them the opportunity to collectively discuss on resource management. The model, which is made of a set of interconnected entities, including (i) community forest habitat, (ii) resource harvesters, (iii) market, and (iv) firebreak. More details about the CoComForest model are described based on the Overview, Design concept, and Details (ODD) protocol uploaded with the model.

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

This is the replication of the experiment performed by Eerkens and Lipo (2005) to look at the effect of copying errors when specific traits are transferred from an individual to another.

This is a replication of the altruistic trait selection model described in Pepper & Smuts (2000, 2002).

This model expands approaches from social practice theories and is used to investigate the ability of the underlying conceptual model to explain the emergence of social practices, defined as routine behaviour that is similar amoung peers.

An Agent-Based Model to simulate agent reactions to threatening information based on the anxiety-to-approach framework of Jonas et al. (2014).
The model showcases the framework of BIS/BAS (inhibitory and approach motivated behavior) for the case of climate information, including parameters for anxiety, environmental awareness, climate scepticism and pro-environmental behavior intention.

Agents receive external information according to threat-level and information frequency. The population dynamic is based on the learning from that information as well as social contagion mechanisms through a scale-free network topology.

The model uses Netlogo 6.2 and the network extension.
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This model simulates different farmers’ decisions and actions to adapt to the water scarce situation. This simulation helps to investigate how farmers’ strategies may impact macro-behavior of the social-ecological system i.e. overall groundwater use change and emigration of farmers. The environmental variables’ behavior and behavioral rules of stakeholders are captured with Fuzzy Cognitive Map (FCM) that is developed with both qualitative and quantitative data, i.e. stakeholders’ knowledge and empirical data from studies. This model have been used to compare the impact of different water scarcity policies on overall groundwater use in a farming community facing water scarcity.

Project for the course “Introduction to Agent-Based Modeling”.

The NetLogo model implements an Opinion Dynamics model with different confidence distributions, inspired by the Bounded Confidence model presented by Hegselmann and Krause in 2002. Hegselmann and Krause used a model with uniform distribution of confidence, but one could imagine agents that are more confident in their own opinions than others. Confidence with triangular, semi-circular, and Gaussian distributions are implemented. Moreover, network structure is optional and can be taken into account in the agent’s confidence such that agents assign less confidence the further away from them other agents are.

FNNR-ABM is an agent-based model that simulates human activity, Guizhou snub-nosed monkey movement, and GTGP-enrolled land parcel conversion in the Fanjingshan National Nature Reserve in Guizhou, China.

Quick-start guide:
1. Install Python and set environmental path variables.
2. Install the mesa, matplotlib (optional), and pyshp (optional) Python libraries.
3. Configure fnnr_config_file.py.
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