Deforestation Simulation Model in NetLogo with GIS Layers

This model has developed in Netlogo software and utilizes
the GIS extension.
This NetLogo-based agent-based model (ABM) simulates deforestation dynamics using the GIS extension. It incorporates parameters like wood extraction, forest regeneration, agricultural expansion, and livestock impact. The model integrates spatial layers, including forest areas, agriculture zones, rural settlements, elevation, slope, and livestock distribution. Outputs include real-time graphical representations of forest loss, regeneration, and land-use changes. This model helps analyze deforestation patterns and conservation strategies using ABM and GIS.

The purpose of this model is to explore the effects of different power structures on a cross-functional team’s prosocial decision making. Are certain power distributions more conducive to the team making prosocial decisions?

This model can be used to optimize intervention strategies for inspection services.

This model includes an innovation search environment. Agents search and can share their findings. It’s implemented in Netlogo-Hubnet & a parallel Netlogo model. This allows for validation of search strategies against experimental findings.

This model represents an agent-based social simulation for citizenship competences. In this model people interact by solving different conflicts and a conflict is solved or not considering two possible escenarios: when individual citizenship competences are considered and when not. In both cases the TKI conflict resolution styles are considered. Each conflict has associated a competence and the information about the conflicts and their competences is retrieved from an ontology which was developed in Protégé. To do so, a NetLogo extension was developed using the Java programming language and the JENA API (to make queries over the ontology).

Plastics and the pollution caused by their waste have always been a menace to both nature and humans. With the continual increase in plastic waste, the contamination due to plastic has stretched to the oceans. Many plastics are being drained into the oceans and rose to accumulate in the oceans. These plastics have seemed to form large patches of debris that keep floating in the oceans over the years. Identification of the plastic debris in the ocean is challenging and it is essential to clean plastic debris from the ocean. We propose a simple tool built using the agent-based modeling framework NetLogo. The tool uses ocean currents data and plastic data both being loaded using GIS (Geographic Information System) to simulate and visualize the movement of floatable plastic and debris in the oceans. The tool can be used to identify the plastic debris that has been piled up in the oceans. The tool can also be used as a teaching aid in classrooms to bring awareness about the impact of plastic pollution. This tool could additionally assist people to realize how a small plastic chunk discarded can end up as large debris drifting in the oceans. The same tool might help us narrow down the search area while looking out for missing cargo and wreckage parts of ships or flights. Though the tool does not pinpoint the location, it might help in reducing the search area and might be a rudimentary alternative for more computationally expensive models.

We propose here a computational model of school segregation that is aligned with a corresponding Schelling-type model of residential segregation. To adapt the model for application to school segregation, we move beyond previous work by combining two preference arguments in modeling parents’ school choice, preferences for the ethnic composition of a school and preferences for minimizing the travelling distance to the school.

Bayesian network is used to modelling the behavior of an individual level and multi-agent system is used to simulate the meme diffusion through the whole network.

The model simulates interactions in small, task focused groups that might lead to the emergence of status beliefs among group members.

This model represents the flight paths of a flock of homing pigeons according to their flocking-, orientation- and leadership behaviour.