Literature seminar

Learning goals

  • Digest a Bayesian decision analysis for a concrete problem
  • Practice to identify sources of uncertainty in an assessment
  • Be able to give examples of principles to quantify and treat uncertainty in a Bayesian analysis 
  • Be able to give an account of science theoretic arguments behind principles to quantify and treat uncertainty in knowledge production and decision making
  • Reflect on the limitations and justification of Bayesian principles and subjective probability to produce scientific advice or decision support
  • Practice collaboration and presentation in an interdisciplinary context

Instructions for the literature seminar

The literature seminar is constructed around three themes (see below). Download the literature (can be found here or click on the link for every paper below). You have been placed in a group (A-C). You are to read the three papers assigned to your group. At the seminar, you will together with your group prepare a presentation of the three papers (try to keep it short < 8 min per paper). Structure the presentation around the answers to the questions under that theme. We recommend thinking through the answers before the literature seminar. It is also helpful to skim through the other papers. 

These topics and papers may be unfamiliar to some of you. If you find the papers difficult to understand, focus on understanding the aims and conclusions of the paper. You could reach out to your group or Ullrika to get support. 

Schedule for the seminar

Preparatory group work

Theme 1. Summaries by Group A, Group B and Group C followed by general discussion. 

Theme 2. Summaries by Group B, Group C and Group A followed by general discussion. 

Theme 3. Summaries by Group C, Group A and Group B followed by general discussion. 

Theme 1: The Bayesian analysis combined with decision making 

Group A. Augustynczik, A. L. et al. Productivity of Fagus sylvatica under climate change–A Bayesian analysis of risk and uncertainty using the model 3-PG. Forest Ecology and Management 401, 192–206 (2017). link to paper Download link to paper

Group B. Spiegelhalter, D. J. & Best, N. G. Bayesian approaches to multiple sources of evidence and uncertainty in complex cost-effectiveness modelling. Statist. Med. 22, 3687–3709 (2003). link to paper Download link to paper

Group C. Theobald, C. M. & Talbot, M. The Bayesian choice of crop variety and fertilizer dose. Journal of the Royal Statistical Society: Series C (Applied Statistics) 51, 23–36 (2002). link to paper Download link to paper

Questions 

  • Describe the decision problem! Who is the decision-maker? What are the decision alternatives? How are values/preferences defined and used? What qualifies as a good decision?
  • Describe the model to inform the decision problem. What is the quantify of interest? What type of data was used? How were priors specified?
  • List sources of uncertainty for this assessment!
  • Describe strengths and weaknesses of the methods and principles underlying the paper as a method in research and as a method to produce scientific advice (decision-support).  

Theme 2: Uncertainty and subjective probability 

Group A. Meder, B., Le Lec, F. & Osman, M. Decision making in uncertain times: what can cognitive and decision sciences say about or learn from economic crises? Trends in Cognitive Sciences 17, 257–260 (2013). link to paper Download link to paper

Group B. Paté-Cornell, E. On “Black Swans” and “Perfect Storms”: Risk Analysis and Management When Statistics Are Not Enough: On Black Swans and Perfect Storms. Risk Analysis 32, 1823–1833 (2012). link to paper Download link to paper

Group C. Parker, W. S. Whose probabilities? Predicting climate change with ensembles of models. Philosophy of Science 77, 985–997 (2010). link to paper Download link to paper

Questions 

  • What type of assessment or decision problem is used as context for the paper?
  • How is uncertainty presented or defined by the authors?
  • What use of subjective probability is described and are there any requirements or justifications for this use?
  • Do the authors suggest any limitations with subjective probability as a measure to quantify uncertainty in research and in processes that produce scientific advice? If so, which?

Theme 3: Beyond the Bayesian paradigm

Group A. French, S. Axiomatizing the Bayesian Paradigm in Parallel Small Worlds. Operations Research (2020). link to paper Download link to paper

Group B. O’Hagan, A. & Oakley, J. E. Probability is perfect, but we can’t elicit it perfectly. Reliability Engineering & System Safety 85, 239–248 (2004). link to paper Download link to paper

Group C. Gärdenfors, Peter. & Sahlin, Nils. E. Unreliable probabilities, risk taking, and decision making. Synthese 53, 361–386 (1982). link to paper Download link to paper

Questions 

  • What challenges with the Bayesian paradigm are raised by the authors?
  • What is their solution?
  • Describe strengths and weaknesses of the methods and principles proposed in the paper relevant and useful in research and in processes that produce scientific advice (decision-support).