Behavioral Decision Theory
Modeling, avoiding/exploiting irrationalities in human decision making; providing tools to nudge or convince others of best decisions
Behavioral decision theory incorporates commonly found deviations from rationality in decision models. People are driven by emotions and use simplifying heuristics rather than advanced optimization. We thus observe excessive risk aversion in overly prudent investments and in the equity premium puzzle. At the same time, risk seeking underlies public lotteries, speculation, entrepreneurial activities, and the absence of sufficient security measures in health and business. People systematically lose money due to inconsistencies in their intertemporal and risky decisions (arbitrage). We analyze these phenomena quantitatively, indicating possibilities to benefit from irrationalities in decisions, to nudge or improve decisions. Applications are given to finance, marketing, management science, health economics, and: your private life. Relative to “Behavioral Foundations,” this course is more advanced, quantitative, and prescriptively oriented. It is nevertheless accessible to students with no maths background.
Take-home exercise or experiment; oral exam
Course notes and articles
Recommended but not required:
Kahneman, Daniel (2011) “Thinking: Fast and Slow.” Penguin Books, London.
Thaler, Richard H. & Cass R. Sunstein (2008) “Nudge: Improving Decisions about Health, Wealth, and Happiness.” Yale University Press, New Haven.
The material of this course comprises fewer pages than for other courses, but takes more time and requires better understanding per page than for other courses.
The timetable for this course can be found in the EUR course guide.
ERIM PhD candidates and Research Master students can register for this course via Osiris Student.
External (non-ERIM) participants are welcome to this course. To register, please fill in the registration form and e-mail it to the ERIM Doctoral Office by four weeks prior to the start of the course. For external participants, the course fee is 260 euro per ECTS credit.