Available Theses Topics

If you are interested in a particular topic listed here for a Bachelor or Master thesis, please contact the corresponding person from the table below.

If you are interested in writing a thesis on another (non-listed) topic within the scope of our group or you want to participate in guided research or an interdisciplinary project, write an email to Mete Ahunbay. Please state your skills and interests and also attach a current CV and a recent grade report. First contact should be established at least one month before registration of the project in order to allow for sufficient time to settle for a suitable topic.

Optimization and Market Design (BSc or MSc thesis) Various topics Prof. Martin Bichler
Computational Social Choice and Algorithmic Game Theory Various topics (having passed "Computational Social Choice", "Algorithmic Game Theory", "Markets Algorithms Incentives and Networks" or "Economics & Computation" is required) Prof. Felix Brandt
Simulations and analysis in shared-economy markets The sharing economy depends on the development of the sharing platform. Different platforms (e.g., ride-hailing, freight exchange, kidney exchange, resource allocation, ... ) have different characteristics. We are committed to abstracting mathematical models from reality to simulate, analyze and provide theory. Research issues include but are not limited to matching strategies, pricing issues, and online prediction. Requirements: advanced programming skills (e.g., Python, Matlab, at least one), mathematics, operation research Donghao Zhu
Implementation of Electricity Market Models As part of our research on electricity markets and our involvement in the Kopernikus SynErgie project we are developing large-scale and realistic models for clearing and pricing on electricity markets. In particular, we want to build on several libraries, implementations, and data sources to create a code base that enables extensive testing and impact analysis of different market designs.    Requirements: advanced programming skills (Python, possibly Julia), mathematical optimization Johannes Knörr
Electricity Market Design Optimization Electricity Market Design: Electricity market design is dynamic in its nature and has recently been exposed to fundamental changes due to the integration of renewable energy resources. We examine sustainable market designs and the underlying allocation and pricing problems as part of the Kopernikus SynErgie projectPricing in non-convex markets: Although nonconvex markets (such as electricity markets) are widespread, finding appropriate prices is not trivial. We study different pricing approaches and associated properties. Requirements: programming skills, operations research Johannes Knörr
Numerical Methods for Variational Inequalities Nash equilibria can be formulated as solutions of variational inequalities. We are interested in numerical methods which are used for such problems (e.g. methods for for monotone VI) and their applicability to equilibrium problems.  Depending on the focus of the topic one could implement different algorithms (e.g., extragradient methods), give an overview of different methods and their convergence behaviour (literature review), or apply such a method to our framework in order to compute Bayes Nash equilibria in auction games. Requirements: good mathematical background, interest in algorithms and optimization (e.g. gradient descent methods), ideally basic knowledge on game theory, programming skills (pref. in Python) A more detailed proposal can be found here. Matthias Oberlechner  
Learning in Games Computing Nash Equilibria in complete-information games is known to be PPAD-complete [1]. Thus, determining equilibria in more realistic, Bayesian games is even more challenging and analytically often intractable. However, some learning algorithms emerged in recent years that can approximate equilibria in many of those games [2], [3]. The thesis aims to analyze alternative models that challenge established algorithms. Requirements: good programming skills (pref. in Python and Pytorch), understanding of fundamental game theoretical concepts, good mathematical background

[1] C. Daskalakis, P. Goldberg, and C. Papadimitriou. 2009. The Complexity of Computing a Nash Equilibrium. SIAM J. Comput. 39, 1 (Jan. 2009), 195–259. https://doi.org/10.1137/070699652

[2] Bichler, M., Fichtl, M., Heidekrüger, S., Kohring, N., & Sutterer, P. (2021). Learning equilibria in symmetric auction games using artificial neural networks. Nature machine intelligence, 3(8), 687-695.

[3] Fichtl, M., Oberlechner, M., & Bichler, M. (2022). Computing Bayes Nash equilibrium strategies in auction games via simultaneous online dual averaging. arXiv preprint arXiv:2208.02036.

Markus Ewert