Learning objectives
This is an interdisciplinary course relying on quantitative as well as qualitative methods. Each aspect will be taught as accessibly as possible so as to appeal to students from both backgrounds!
Basic knowledge of mathematics and statistics is recommended.
Students ...
- ... have understood the nature and sources of uncertainty in science and policy,
- ... have experienced and understood the key assumptions of Classic and Bayesian probability theory and the differences between the two,
- ... have acquired the skills to apply these appropriately,
- ... have an outlook on quantitative theories of uncertainty beyond probability theory,
- ... have examined the various dimensions of uncertainty in the science-policy process and
- ... have acquired the skills to reflect critically on the relationship between science and policy.
Topics
- Conceptions of risk, uncertainty & ignorance
- Sources of uncertainty & types of uncertainty analysis
- Probability theory: classic & Bayesian
- Limits of quantitative uncertainty theories
- Conceptions of science-policy interrelations
- Conceptions of expertise
- Wicked problems & Post-Normal Science
- Participation & transdisciplinarity
- Instrumental vs. collaborative rationality
- Adaptive management, public experiments & precaution
Format
2 SWS seminar + 2 SWS practical
In the 1st part of the semester we will do exercises in probability theory using spreadsheets. This will be taught as accessibly as possible!
In the 2nd half of the semester we will study and discuss original literature on uncertainty in the science-policy process. This requires willingness to read!
There may be homework.
Students are required to prepare and give a presentation of a topic.
Final exam: essay, choosing between (a) quantitative study (e.g. write-up of exercise, case study applying method) and (b) argumentative study (e.g. critique of method, discussion of science-policy aspect). |
