The goal of the seminar is to discuss current issues in behavioral and to shed light on their relationship to economic theory and our own behavior. Students will first read a current popular science book (in English) as well as the relevant scientific literature. Then the individual topics will be presented and critically discussed during the seminar. Furthermore, students will develop individual research questions.
This term, we will read the following book: Thaler, R. H. (2016). Misbehaving: The making of Behavioral Economics. W.W. Norton & Company, New York. Available at the university library: https://katalog.tub.tuhh.de/Record/1621785726
Voraussetzungen:
None
Lernorganisation:
- First meeting (Introduction to course and procedures): October 17, 2023
- Second meeting (Questions and clarifications): October 24, 2023
- Start date of the presentations and comments: November 14, 2023
In each session there will be one presentation, followed by the comments.
Leistungsnachweis:
Presentation: You will work on a section of the book by Thaler (2016). Allocation of the topics will be done via Stud.IP. Details will be announced in the first meeting. Evaluation will be based on the following elements: 20 min group presentation (up to 3 students) and 10 min comment. You can find more information in uploaded syllabus.
Sonstiges:
Admission
You can sign up to the course in Stud.IP. Please note that you are expected to attend the first session, otherwise we may sign you out of the course in order to grant admission to students from the waiting list.
In order to participate in the course, you will have to select a section to present in the group presentation and to discuss in the comment. Please make your section selections as soon as possible after the first meeting (October 18, 2023 at 12:00 noon), but at the latest by the end of October 31, 2023. Later selections will not be accepted. Procedures will be explained in the first meeting.
ECTS-Kreditpunkte:
2
Weitere Informationen aus Stud.IP zu dieser Veranstaltung
Hund, P. (2021). Modellierung eines elektrischen Netzes zur Demonstration des Einflusses von virtueller Trägheit durch umrichterbasierte Energieanlagen.
Hund, P. (2021). Koordinierte Bereitstellung von virtueller Trägheit durch erneuerbare umrichterbasierte Energieanlagen in Verteilnetzen mithilfe von künstlicher Intelligenz.
Möller, P. (2021). Erfassung der Knotenspannung in Niederspannungsnetzen auf Basis von dezentralen Messeinrichtungen mithilfe von Machine learning.
Plant, R. (2021). Estimation of Power System Inertia in an Inverter-Dominated Distribution Grid Using Machine Learning.
2020
Dressel, M. (2020). Modellierung der Zustandsschätzung eines elektrischen Netzes mit Hilfe von Graph neuronalen Netzen.
Schmidt, M. (2020). Vorhersage von zuverlässig bereitstellbarer Regelleistung aus Erneuerbaren Energien mithilfe von neuronalen Netzen.