B2813 - BEHAVIORAL ECONOMICS FOR SUSTAINABLE SYSTEMS

Learning outcomes

This course is intended to allow students to become familiar with the literature in behavioral economics and decision making. The course has two facets: first it will uncover the inner workings of human biases and judgment and the students will gain insights into how inherent bias or poorly structured information can affect business decisions. Second, it will discuss how the adoption of a behavioral economics approach will support managers and decision makers to cope both with economic sustainability of businesses and environmental and social sustainability.

Course contents

From economic men to behavioral decision making

Human biases and emotional states: Systematic error in decision making?

Perception of risks and decision making under uncertainty

Intertemporal decision making: the sophisticated and naïve decision makers

Mental accounting and sunk costs.

Motives and Motivational Bias

Group Decision Making and social influence

Nudging: Understanding of choice architecture to promote sustainability

Experimentation and data to optimize performance accounting for environmental and social sustainability

How businesses and governments are using behavioral economics to design more sustainable systems

Readings/Bibliography

Angner. A. (2021) A Course in Behavioral Economics, London: Palgrave Macmillan, 2021, 3. , p. 327

Kahneman, D. (2011). Thinking Fast and Slow. New York: Farrar, Straus and Giroux.

Thaler, R. and Sunstein, C. (2009) Nudge: Improving Decisions about Health, Wealth and Happiness, London: Penguin.

Enste, Dominik; Wildner, Julia; Nafziger, Lucia (2021) : Going green with behavioural economics: How to combine business and ethics, IW-Report, No. 1/2021, Institut der deutschen Wirtschaft (IW), Köln

Additional readings will be provided by the teacher during the course.

Lecture notes:

The lecture notes will be made available on the IOL platform before each class (but might be corrected/updated shortly after the class). They should NOT be taken as the only reference, as they often do not cover the analytical details discussed in the manual, which are important for a thorough understanding of the subject matter.

Teaching methods

The course comprises both traditional lectures and a hands-on laboratory part.

Students will have the chance to play simple experiments in class. We will also discuss and analyze data from prominent research papers.

The class is designed to be highly interactive and students are expected to prepare presentations, videos/podcasts, and short essays.

Assessment methods

Attending students: the final grade will be determined by

• class participation, groups assignments and presentation: 50%
• final essay: 50%

Non attending students: will be evaluated with a written exam based on multiple choice questions and open questions.

The dates of the final exams are fixed and cannot be changed. Requests for additional dates will not be accepted.

The maximum possible grade is 30 cum laude. The grading scale is the following:

<18: Fail
18-23: Sufficient
24-27: Good
28-29: Very good
30: Excellent
30 cum laude: Outstanding (the instructor was impressed)

Grade rejection: students can reject the grade obtained at the exam only once. To this end, they must email a request to the instructor within the date set for registration. The instructor will confirm reception of the request asap.

Teaching tools

During the course, students will be involved in mock experiments, which should provide them with a more vivid idea of the issues to be examined later during the lecture, and active participation to the in-class discussion will be encouraged.

The mock experiments will be computer-based and will require the use of a pc, tablet or smartphone connected to the internet.

Office hours

See the website of Natalia Montinari