ABM2019 Agent-based modeling for geographical applications

Nature and society are intertwined complex systems made up of multiple agents such as humans, animals, firms or vehicles interacting across space and over time in a way that leads to the emergence of patterns at a variety of scales. Agent-based modeling is a powerful tool to investigate these systems, therefore supporting the design of policies that may improve environmental conditions and human wellbeing.

 

This course (20 hours) provides an introduction to the concepts of agent-based modeling, a space for discussion about using this technique to investigate geographical problems and an opportunity to learn how to build and test agent-based models with the NetLogo programming language

 

In particular, the course covers the following topics:

 

- Agent-based modeling as a bottom-up modeling technique 

- Basic concepts: emergence, learning, stochasticity, spatial and temporal scales

- Building a model from scratch: theory, practice and examples

- Analysis of geographical problems using GIS data

- Calibration and validation

- Uncertainty and sensitivity analysis

 

The teaching is based on theoretical lectures and extended hands-on sessions where participants can get familiar with the NetLogo modeling environment.

 

At the end of the course, participants will:

 

- Have a broad understanding of the theory of agent-based modeling

- Be familiar with some classic models

- Be able to build simple models using NetLogo

- Know how to handle georeferenced data in an agent-based modeling environment

- Know how to perform basic robustness tests on a model

- Understand how agent-based modeling fits into decision-making

 

The course is intended as an introduction to agent-based modeling, hence no prior knowledge of either the subject or an ABM language is expected from participants. 

 

The target audience of the course is represented by doctoral students, researchers, practitioners and public officers dealing with a variety of geographical problems.

 

Francesco Orsi
francesco.orsi [at] unitn.it

 

Davide Geneletti
davide.geneletti [at] unitn.it 
Planning for Ecosystem Services research group