Headings denoted with an asterisk ( * ) is retrieved from the course syllabus version Autumn 2020
Content and learning outcomes
Course contents
introduction to decision theory and concepts such as rationality, preferences, aims, uncertainty and utiliity,
presentation and application of decision-theoretical methods,
introduction to decision making under risk and uncertainty,
philosophical perspective on decision-theoretical questions,
exercise in formalisation and analysis of decision problems.
Intended learning outcomes
On completion of the course, the student should be able to:
give an account of the basic concepts that are used in decision theory,
give an account of the basics of formalisation of decision problems, choices of decision-theoretical method and decision during risk under risk and uncertainty
discuss and problematise the formalisation of decision problems and justify choice of decision-theoretical method given a certain decision problem under risk and uncertainty,
discuss decision-theoretical issues from a philosophical perspective
discuss and problematise decision theory and decision making method in practical and professional contexts.
Learning activities
Lecture 1: Introduction to Decision Problems and Decision Rules
Lecture 2: Probability & Expected Utility
Seminar 1: Bayesian vs. Ecological Rationality
Lecture 3: Bayesian Decision Theory
Lecture 4: Multi-Criteria decision making
Seminar 2: Newcomb's Paradox
Q&A session (for questions related to examination project)
Detailed plan
Learning activities
Content
Preparations
Lecture 1
Introduction to Decision Problems and Decision Rules
Peterson chapters 1&2 and Peterson chapter 3 (note that this is from the 1st edition, you are highly recommended to get the 2nd edition as early as possible).
Lecture 2
Probability & Expected Utility
Peterson chapters 4-7
Seminar 1
Bayesian vs. Ecological Rationality
Gigerenzer, G., & Brighton, H. (2009). Homo heuristicus: Why biased minds make better inferences. Topics in Cognitive Science, 1(1), 107-143.
Simon, Herbert A. (1973). The Structure of Ill-structured Problems. Artificial Intelligence 4: 181-201.
Sniedovich, M. (2012). Black Swans, New Nostradamuses, Voodoo decision theories, and the science of decision making in the face of severe uncertainty. International Transactions in Operational Research, 19(1-2), 253-281.
In order to participate in the seminar, you need to submit an essay on the paper assigned to you. Detailed instructions are available on Canvas.
Lecture 3
Bayesian Decision Theory
Peterson chapter 8-10
Jeffrey on measuring p AND U from preferences
Lecture 4
Multi-Criteria decision making
Keeney & Raiffa, pp. 82-100
Chapter 6 in Multi-criteria Analysis: a manual
Seminar 2
Newcomb's Paradox
Nozick, Robert. (1969) Newcomb's Problem and Two Principles of Choice. In Nicholas Rescher, ed., Essays in Honor of Carl G. Hempel, pp. 114–146. Dordrecht: Reidel.
Mackie, John L. "Newcomb's Paradox and the Direction of Causation."Canadian Journal of Philosophy (1977): 213-225.
Egan, Andy (2007) Some Counterexamples to Causal Decision Theory. Philosophical Review, 116: 93–114.
In order to participate in the seminar, you need to submit an essay on the paper assigned to you. Detailed instructions are available on Canvas.
Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.
The examiner may apply another examination format when re-examining individual students.
Ethical approach
All members of a group are responsible for the group's work.
In any assessment, every student shall honestly disclose any help received and sources used.
In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.
