This class introduces students to key concepts and topics in the field of contemporary climate policy. We focus on public policies for mitigating climate change, with EU climate policy serving as the main (but not the only) case study. The class draws heavily on economics and political science concepts but remains non-formal throughout (no calculus required). The first part of the class introduces the key physical, economic, technological, welfare, and political dimensions of climate change policy. Students learn core analytical frameworks, concepts, and topics. The second part offers a deep dive into the analysis and design of domestic climate policy instruments and institutions, providing an overview of the field as well as introductions into the relevant economics and political science perspectives. The third part covers international climate policy, climate policymaking in multi-level governance systems, and EU climate policy as one major regional case study. The seminar concludes with a session on different actor groups (potential) strategies in climate politics.
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This class is tailored to students with prior knowledge and/or training in (micro) economics. The course will cover a selected set of policy-relevant topics in applied microeconomics. In doing so, we will discuss conceptual foundations particularly empirical methods for establishing causal relationships and apply these to a rich set of policy domains. Starting from evidence on income inequality, the first part of the class (Sessions 1-8) discusses redistributive policies: income taxation, welfare programmes (incl. universal basic income), and minimum wages. We will empirically explore (and quantify) responses to these policies and analyse welfare implications. The last third of the course (Sessions 9-12) discusses corrective policies in the context of externalities. Next to traditional environmental externalities (and conventional, Pigouvian responses such as CO2 taxation), we will also explore the correction of internalities (e.g., in the context of sugar taxation). Note that the course will be centred on policies affecting individual and household decision-making. We will not tackle Macroeconomic questions and hardly discuss the choices of firms.
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This course gives students an advanced theory-driven overview of global politics, focusing on how states approach governance problems that they cannot solve alone, and on the international institutions created to provide public goods. The guiding theme is the contestation of global authority and governance structures. By the end of the course, students will be able to understand and explain the trajectory and the challenges of global politics from a political science perspective. They will be able to review academic literature relating to global politics and formulate research designs at the Master level. The goal is to practice evidence-based scientific arguments on these topics. The course will also provide insights into various topics that relate to the three streams of the Master of International Affairs. This course is suitable for more advanced students who have already done substantial coursework in international relations (IR). Choose this class if you have an undergraduate degree/major in International Relations, International Politics, and/or Political Science and if you know the basic theories of IR such as realism, (liberal) institutionalism, or constructivism and if you are familiar with introductory-level governance concepts. Students who do not have a full IR degree but have done substantial coursework on IR topics should also take this class.
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The course is on exploratory statistical methods for data analysis. Hence its aim is somewhat comparable to Hertie's Statistics I and Statistics II. However, it will go beyond these courses in terms of methodological/mathematical depth. Advanced quantitative methods and modeling tools will be introduced and used for making sense of numbers. Examples and data are taken mainly from economics and health sciences. Data analysis will be performed using R.
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This course begins with an introduction to game theoretic analysis. We study the concepts and models used to analyze strategic behavior, including normal and extensive form games, games of incomplete information, finite and infinitely repeated games, auctions, and mechanism design. We then review recent advances in computer science in the development of algorithms to analyze these problems, and to design a better world. Finally, we explore applications of algorithmic game theory to the development of AI; to structuring social interactions in markets and on the Internet; and to the future of social science research. This course requires as a prerequisite Mathematics for Data Science or a similar course, in particular a working knowledge of multivariate calculus and optimization. The course will occasionally rely on some methods of mathematical proof (e.g., proof by contradiction, proof by induction, etc.), but prior exposure to them is not assumed.
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This course provides "hands-on" experience with logistic regression analysis. This course does not teach "dry" statistical techniques. Instead, all lectures start with a social policy-relevant research question. Together with the instructor, students will develop an understanding of how to choose the right data set, how to prepare the data for empirical investigation, and how to conduct simple but sound empirical investigations. We focus on methods for discrete data. This is because most processes in political science are discrete. To give some typical examples: What determines xenophobic attitudes? What affects poverty and social exclusion? What factors relate to intimate partner violence? To answer such questions in a regression framework, logistic regression techniques are commonly used. We draw on data from across the globe, including data from Latin America, Africa and Asia.
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This course teaches basic sociological concepts and empirical methods that enable students to study mobility across the life course. How long does it take to leave poverty or unemployment? What determines upward mobility in the labour market? When do individuals leave education, the parental home, get divorced or have a child? How do patterns vary by gender, education, family context, migration background and country? In order to answer questions of this kind, we use micro-level data from various sources. Moreover, students will get familiar with classical methods for longitudinal data. These include, in particular: Event history modelling, sequence analysis and fixed-effects regression. Some R skills (usually obtained in STATS I) are useful.
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This course will cover applied research methods frequently used in health policy research. The course will cover causal analysis methods applied to policy problems in the field of healthcare. Students will be introduced to randomized control trials and other quasi-experimental methods with a focus on recent development in health economics and policy. This is a methods course and therefore a significant portion of the course will be dedicated to in-class application of research methods.
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Artificial intelligence (AI) and climate change are both topics on top of the policy agenda that require a deep technological understanding of the problem space. It does not come as a surprise that these two topics also affect one another in multifaceted ways. This course will explore the relationship of AI and climate change through a policy lens, and ask the question of what policy-makers can do to align AI with climate change goals. Readings will provide students with insights into cutting edge research using AI and machine learning (ML) to address climate change. The course will also cover how AI is deployed in ways that are detrimental to these goals, provide a perspective on systemic effects of AI-driven technologies and their impacts on social well-being, and discuss energy and resource consumption related to AI's computational requirements. Together, we will explore technology assessment and design possible policy instruments. Students will learn how to navigate this intersection of two hot button topics and provide informed and practical advice to policy makers.
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This course provides an in-depth exploration of the ethical and societal dimensions of Artificial Intelligence (AI), aiming to equip students with the skills to make responsible decisions when assessing, building, or deploying technology. Through interactive lectures, discussions, scenario simulations, and student presentations, supported by readings from various fields, we will delve into the historical origins of AI and its contemporary impacts. Topics will include bias and fairness in machine learning, AI alignment, democratic processes, job markets, social interaction, international competition, communication, AI and climate, social media, privacy, copyright, and emerging regulatory boundaries. Utilizing the Code Capital framework (Conception, Operations, Data, and Environment), students will learn to analyze AI systems and their normative forces from design to deployment.
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Organisations face numerous challenges when evaluating investments in AI initiatives, including uncertainty about their potential value, difficulty prioritising use cases, and navigating the complexities of operational integration, data readiness, and ethical concerns. Without a structured approach, organisations risk making misaligned investments that fail to deliver meaningful impact and outcomes.
This course tackles these challenges by providing participants with practical AI use case evaluation frameworks that enable a systematic and holistic assessment of AI initiatives. Through real-world examples and case studies, participants will learn to evaluate, prioritise, and select AI use cases based on factors such as return on investment, strategic alignment, data and model requirements, integration complexity, operational impacts, and legal and ethical considerations. This structured approach ensures informed and effective decision-making for AI adoption in both public and private sector organisations.
By the end of this course, participants will be able to:
- Identify and prioritize AI use cases based on organizational needs.
- Assess the technical, operational, and financial feasibility of AI solutions.
- Evaluate value creation through ROI, strategic alignment, and business impact analysis.
- Apply frameworks to justify and prioritise AI investments.
- Communicate AI use case evaluations effectively to stakeholders.
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