1. Decision Making and Choice Behavior
October 30 – December 4, 2017
Mondays and Wednesdays 10:15-11:45 am
Instructor: Tommi Pajala
LECTURE 1

2. Outline of this lecture
Outline of this lecture
Overview of the course
Objectives
Evaluation and Grading
Contents
Introduction to the analysis of decisions
An example about decision trees

3. Objectives, overview of the course
Objectives, overview of the course
The purpose of this course is, within risky and riskless choice to:
provide an overview of rational decision making, including
subjective measurement (utilities, probabilities)
axioms of choice
discuss many behavioral decision theory issues, such as
prospect theory, heuristics and biases, framing, and the role of emotions
differentiate between descriptive, prescriptive, and normative
provide an idea how to improve decision making (your and others’)
relate the theories to the domains of business and consumer decision making

4. Contact details
Tommi Pajala If you have a problem, a question, or you need assistance, you can me. No fixed office hours, appointments can be scheduled as necessary.

5. Teaching schedule 5 weeks of lectures, 2 x per week Final exam: 13.12.
Also two retake exams:
Lecture 1:
Lecture 2: 1.11.
Lecture 3: 6.11.
Lecture 4: 7.11.
Lecture 5:
Lecture 6:
Lecture 7:
Lecture 8:
Lecture 9:
Lecture 10:

6. Course assignments Part Points Rational Decision Making (part I)
7 x pop quiz 35
1 x assignment 10
1 x essay 15
Final exam 40
Total
100

7. Lecture system Before class In class After class
Please read the required article before class!
The article is in MyCourses under ”Reading”
You will get a better score in the quiz…
In class
Be active, ask questions, make notes, think about the topic
After class
Questions? Feedback? Go to:

8. Pop quizzes 9 pop quizzes Once per lecture, but not for the first
Held always in the beginning, no exceptions (don’t be late!)
7 best scores are counted
5p / quiz = 35 % of grade from quizzes
Considers only one article
When the readings have 2, only the first one is required before the lecture
Should be very simple, just checking if you have read the article
If you can’t make it to class:
Worst TWO scores are ignored, this should be enough in a 10-class format

9. Lecture topics (1/3) Introduction, Decision Analysis (30.10.)
Utility & Bayes’ Theorem (1.11.)
Baron: Normative theory of choice under uncertainty. In Thinking and Deciding, 4th ed., 2008, pp
Hastie & Dawes: Changing Our Minds: Bayes’ Theorem. In Rational Choice in an Uncertain World, 2nd ed., 2010, pp
Axioms & elicitation (6.11.)
Hastie & Dawes: Defining Rationality: Expected Utility Theory. In Rational Choice in an Uncertain World, 2nd ed., 2010, pp
Chesley: Elicitation of Subjective Probabilities: A Review, Accounting Review 50 (2), April 1975, pp
Basic concepts of behavioral decision making, prospect theory (7.11.)
(ONLY SEC. III) in Kahneman: Maps of Bounded Rationality: Psychology for Behavioral Economics, The American Economic Review, Vol. 93, No , pp
Camerer: Prospect Theory in the Wild: Evidence from the Field. In Choices, Values, and Frames, (Eds.) Kahneman & Tversky, reprinted 2005,

10. Lecture topics (2/3)
Anchoring, availability and representativeness (13.11.)
Tversky-Kahneman: Judgment under Uncertainty: Heuristics and Biases, Science 185, 1974, pp
Hammond, Keeney, Raiffa: The Hidden Traps in Decision Making, Harvard Business Review, 1998, pp.47-58
Framing and mental accounting (15.11.)
Tversky-Kahneman: The Framing of Decisions and the Psychology of Choice, Science 211, 1981, pp
Thaler: Mental Accounting and Consumer Choice, Marketing Science 4, 1985, pp. 199 – 214
Fast & frugal decision making (20.11.)
Simon: A Behavioral Model of Rational Choice, Quarterly Journal of Economics 69 (1), 1955, pp
Gigerenzer & Goldstein: Reasoning the Fast and Frugal Way: Models of Bounded Rationality, Psychological Review 103(4), 1996, pp

11. Lecture topics (3/3) Emotions / embodied cognition (22.11.)
Hsee & Rottenstreich: Music, Pandas, and Muggers: On the Affective Psychology of Value, Journal of Experimental Psychology, 2004, 133 (1),  (student presentation)
Bechara & Damasio: The somatic marker hypothesis: An neural theory of economic decision, Games and Economic Behavior, 52, 2005,
Dealing with conflicting objectives (27.11.)
Baird: Multicriteria Decisions. In Managerial Decisions Under Uncertainty, 1990, chapter 13.
Aiding decision making (29.11.)
Hammond, J. S., Keeney, R. L., & Raiffa, H. Even Swaps: A Rational Method for Making Trade-Offs. Harvard Business Review, 1998, 76(2), pp. 137–150.
Münscher, R., Vetter, M., & Scheuerle, T. A Review and Taxonomy of Choice Architecture Techniques. Journal of Behavioral Decision Making, 2015.
Final exam (13.12.)
Deadline for assignments!

12. Assignment 1 Calculation exercises about:
expected value
utility
decision trees
Bayes’ theorem
prospect theory
Total of 10 points, 2p / task

13. Assignment 2 1500-word essay (15 p)
You can select your topic from a list
This gets you deeper into one issue
Using material from outside the curriculum is required
And of course: no plagiarizing! Essays are checked with Turnitin

14. Exam
Both the articles and material covered in lectures Short essays, some calculation tasks Minimum requirement to pass: 50%

15. Grading
Grades from quizzes / homeworks are posted on MyCourses as soon as possible
Grade limits set in advance (I reserve the right to make them easier)
Also: minimum requirement 50% in the exam
Points
Grade 50 1 57 2 63 3 70 4 80 5

16. Introduction to the analysis of decisions … Intuition and reasoning
Intuition developed through experience is a valuable tool. Necessary for creative activity! Relying on it everywhere, however, may lead to problems. Try first your intuition, then attempt to calculate!
Rope around the globe!
Given a disease whose prevalence is 1:1000 and a diagnostics procedure whose false-positive and false-negative rate is 5% (test errs in 5% of the cases), what is the probability that a patient who was diagnosed as being inflicted with the disease, actually has it?”

17. Intuition example 1
You have a piece of rope that just fits around the Earth. If you put 1 metre high sticks right around the equator and lay the rope on top, how much longer does the rope need to be to make ends meet?

18. 𝑳 𝑹 =𝟐𝝅 𝑹 𝑹 𝑳 𝑹 =𝟐𝝅 𝑹 𝑬 +𝒉 𝑳 𝑹 =𝟐𝝅 𝑹 𝑬 +𝟐𝝅𝒉 𝑳 𝑹 = 𝑪 𝑬 +𝟐𝝅𝒉 With 𝐡=𝟏𝒎 𝑳 𝑹 = 𝑪 𝑬 +𝟐𝝅 So about 6.31 meters!

19. Intuition example 2
Given a disease whose prevalence is 1:1000 and a diagnostics procedure whose false-positive and false-negative rates are 5% (test errs in 5% of the cases), what is the probability that a patient who was diagnosed as being inflicted with the disease, actually has it?

20. Positive: 0.95*100 =95 Have disease: 0.001*100 000 =100
Total people:
Have disease: 0.001* =100
Positive: 0.95*100 =95
Negative: 0.05*100 =5
No disease: 0.999* =99 900
Positive: 0.05*99900 =4995
Negative: 0.95*99900 =94 905

21. Introduction to the analysis of decisions: descriptive vs normative
Introduction to the analysis of decisions: descriptive vs normative
Descriptive: How are decisions made?
Normative: How should decisions be made?
Prescriptive: How to improve human decision making? What decisions should we make, what to ignore?
60% descriptive, 40% prescriptive in the course
Sometimes same decision model is used for both purposes
Descriptive = behavioral decision theory … but also interested in improving decisions
Prescriptive = decision analysis, management science (“the science of better”); interested in improving decisions

22. The automatic – deliberate continuum
Not all decision making is the same!
Some decisions are more automatic, others deliberate
Depends on:
Activity
Environment
Goals …

23. Automatic
Deliberate

24. Automatic
Deliberate

25. Automatic
Deliberate

26. Automatic
Deliberate

27. Automatic
Deliberate

28. Introduction to the analysis of decisions … examples
Introduction to the analysis of decisions … examples
Examples of decision problems (personal, corporate)
Choice of consumer durables
Company recruiting decision
Buying a home
Choice of a location for a commercial airport
Whom to vote in elections
Pricing alcoholic beverages
Deciding about the rate at which random sampling of the production line is to take place
Uncertainty, multiple conflicting objectives (attributes, dimensions) – either or both; small versus large number of alternatives

29. Good outcome = good decision?
Luck?
Good decision
Bad decision
Bad luck?
Bad process
Good process
Bad outcome