Bayesian Networks (BNs) are a flexible modelling method that can be used in various ways to address different types of research questions. Their strength comprises the ability to combine different types of data and knowledge, as well as include, integrate and represent uncertainty. The graphical nature of BNs makes them easy to communicate across scientific disciplines, and to diverse groups of stakeholders.
The objective of this course is to learn how Bayesian Networks can be used in inter- and transdisciplinary analysis of socio-ecological systems.
After the course, the student can:
- explain what Bayesian Networks (BN) are and how they work
- explain what inter- and transdisciplinary research mean and what is their value
- estimate and evaluate the need for inter/transdisciplinarity and participatory approach in socio-ecological research questions
- create a BN model that reflects a socio-ecological research question
- evaluate theoretical, scientific, and cognitive factors that need to be considered when designing an inter/transdisciplinary BN model
- use a readily available software package to build a BN
- find and evaluate information sources to populate the model
Practicalities:
The course is organized as online teaching on 7-11 December 2020. The teaching includes short lectures, reading selected articles, peer group discussions, discussions and Q&A sessions with the teachers, guided exercises to learn the BN software, and framing, building, and presenting your own model (and giving and receiving feedback).
Requirements for attending:
- installation of the BN software (Hugin Lite, available free of charge for evaluation purposes for Windows, iOS and Linux)
- possibility to attend online meetings during European office hours (including microphone and preferably camera use)
- possibility to dedicate the whole week for the course – work is required also outside of the online meetings
