The standard economic approach to model human decision-making rest on the assumptions of rationality and selfishness. Behavioral economics investigates departures from these assumptions and develops alternative models. In this course, we will first study the standard economic model and then take a look at empirical evidence which reveals systematic deviations from the theoretical predictions, for example, in risky and inter-temporal decision-making. Finally, we will analyze models that aim at a better description of actual human behavior. More detailed information will be made available on Stud.IP.

This course provides a theoretical introduction to the use of quantitative methods in political science and international relations. It deals with quantitative research design, causal inference, and the assumptions and interpretation of linear regression models. The primary aim is to foster confident understanding and discussion of scientific studies that employ quantitative methods. Participants can earn 3 CPs by completing a take-home exam (5 questions) between 5th and 12th December.

Literatur zur Vorbereitung/Preparatory Reading:

Kellstedt, Paul M., and Guy D. Whitten (2008). The Fundamentals of Political Science Research. Cambridge: Cambridge University Press.

Prüfungsleistungen und CP/Assignments and Credits:
3 CP Take-home exam

Participation in the Introduction to Quantitative Methods in the first half of the semester is a prerequisite for this course. The specialization seminar provides an applied introduction to the use of quantitative methods in political science and international relations. The primary aim is to enable the independent application of multiple regression analysis. The course also introduces more advanced methods including nonlinear models. Participants will be trained and supported to use the statistical software “R” for data processing, visualization, analysis and reporting. Those with prior experience may choose to complete the class work and assessment using Stata.

Literatur zur Vorbereitung/Preparatory Reading:

Kellstedt, Paul M., and Guy D. Whitten (2008). The Fundamentals of Political Science Research. Cambridge: Cambridge University Press.

Prüfungsleistungen und CP/Assignments and Credits:
6 CP Research report

In the course “AI and Computational Management Information Systems”, students learn how artificial intelligence (AI) and management information systems (MIS) can be used in companies and public institutions. The focus is on practical applications to support data-driven decisions and optimize business processes. Theoretical concepts are taught on essential principles in combination with case studies and practical exercises. Particular attention is paid to ethical issues and data protection in order to teach the responsible use of AI. The aim is to equip students with the necessary tools to implement AI-based systems efficiently and securely.

Technological innovations, especially in information and communication technologies, are not only the subject of political and regulatory debates but also improve the basis of political decision-making processes and can simplify the provision of public services. The combination of advances in machine learning and the availability of large (behavioral) data sets opens up new opportunities to automate decision-making processes in the public sector and the provision of public services. Using algorithmic decision-making systems can improve the quality of public services, promote co-production processes, and improve participation in policy-making. At the same time, questions are increasingly being raised about the normative requirements and regulatory limits of using algorithmic decision-making systems in the public sector. This interdisciplinary project seminar aims to enable students to critically reflect on the design, control, and regulation of algorithmic decision-making systems in the public sector. The seminar, therefore, deals with using algorithmic systems for decision support in the public sector from a behavioral science perspective and conveys current research results from political science, public management, social psychology, and human-computer interaction. The participants will independently develop a survey experiment in the field of Algorithmic Decision-Making in the Public Sector. The experiment will be implemented and conducted with the help of an online survey application, which makes it possible to create high-quality survey experiments without programming knowledge.