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

Outline of this lecture 19.9.2018 Outline of this lecture Overview of the course Objectives Evaluation and Grading Contents Introduction to the analysis of decisions An example about decision trees

Objectives, overview of the course 19.9.2018 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

Contact details Tommi Pajala tommi.pajala@aalto.fi If you have a problem, a question, or you need assistance, you can email me. No fixed office hours, appointments can be scheduled as necessary.

Teaching schedule 5 weeks of lectures, 2 x per week Final exam: 13.12. Also two retake exams: 10.02.2017 31.03.2017 Lecture 1: 30.10. Lecture 2: 1.11. Lecture 3: 6.11. Lecture 4: 7.11. Lecture 5: 13.11. Lecture 6: 15.11. Lecture 7: 20.11. Lecture 8: 22.11. Lecture 9: 27.11. Lecture 10: 29.11.

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

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: http://presemo.aalto.fi/decisioncoursefeedback

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

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. 233-243 Hastie & Dawes: Changing Our Minds: Bayes’ Theorem. In Rational Choice in an Uncertain World, 2nd ed., 2010, pp. 178-188 Axioms & elicitation (6.11.) Hastie & Dawes: Defining Rationality: Expected Utility Theory. In Rational Choice in an Uncertain World, 2nd ed., 2010, pp. 245-259 Chesley: Elicitation of Subjective Probabilities: A Review, Accounting Review 50 (2), April 1975, pp. 325-337 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. 5 2003, pp. 1449-1475 Camerer: Prospect Theory in the Wild: Evidence from the Field. In Choices, Values, and Frames, (Eds.) Kahneman & Tversky, reprinted 2005, 288-300

Lecture topics (2/3) Anchoring, availability and representativeness (13.11.) Tversky-Kahneman: Judgment under Uncertainty: Heuristics and Biases, Science 185, 1974, pp. 1124-1131 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. 453-458 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. 99-118 Gigerenzer & Goldstein: Reasoning the Fast and Frugal Way: Models of Bounded Rationality, Psychological Review 103(4), 1996, pp. 650-669.

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), 23-30  (student presentation) Bechara & Damasio: The somatic marker hypothesis: An neural theory of economic decision, Games and Economic Behavior, 52, 2005, 336-372 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!

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

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

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

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

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?”

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? 19.9.2018

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! 19.9.2018

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? 19.9.2018

Positive: 0.95*100 =95 Have disease: 0.001*100 000 =100 Total people: 100 000 Have disease: 0.001*100 000 =100 Positive: 0.95*100 =95 Negative: 0.05*100 =5 No disease: 0.999*100 000 =99 900 Positive: 0.05*99900 =4995 Negative: 0.95*99900 =94 905 19.9.2018

Introduction to the analysis of decisions: descriptive vs normative 19.9.2018 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

The automatic – deliberate continuum Not all decision making is the same! Some decisions are more automatic, others deliberate Depends on: Activity Environment Goals … 19.9.2018

Automatic Deliberate 19.9.2018

Automatic Deliberate 19.9.2018

Automatic Deliberate 19.9.2018

Automatic Deliberate 19.9.2018

Automatic Deliberate 19.9.2018

Introduction to the analysis of decisions … examples 19.9.2018 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

Good outcome = good decision? Luck? Good decision Bad decision Bad luck? Bad process Good process Bad outcome 19.9.2018