If you are interested in a particular topic listed here for a Bachelor or Master thesis, please contact the corresponding person from the list 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 Felipe Maldonado. 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.
| Title | Focus | Contact |
|---|---|---|
| Optimization and Market Design (BSc or MSc thesis) |
various topics | |
| Computational Social Choice and Algorithmic Game Theory | various topics (having attended one of the courses "Computational Social Choice" or "Algorithmic Game Theory" or seminars "Multiagent Systems" or "Economics & Computation" is recommended) | |
| Equilibrium Learning in Auctions and Markets | various topics (required: previous experience with at least one of the following: nonlinear optimization, algorithmic game theory, neural networks, reinforcement learning) | Stefan Heidekrüger |
| (added 15.01.2020) Improving Effectiveness and Efficiency of a Deep Reinforcement Learning System for Equilibrium Computation |
The DSS chair has developed a system to compute approximate Bayesian-Nash equilibria in auction markets leveraging deep multi-agent reinforcement learning. The goal of this thesis is improving robustness (quality of solutions) and performance (computation time) of the existing system on a set of multiple application settings. Possible approaches include choice in neural network architectures (layout, size, regularization, activation functions), optimization algorithms (type, hyperparameters, early-stopping, adaptive learning rate schedules, etc) and reinforcement learning methods. You will be working in the pytorch framework. Requirements: first experience with python and basic familiarity with deep learning concepts. |
Nils Kohring |
|
Geographic Visualization of |
The goal of this thesis is to develop a tool that visualizes the market area of bidders and their package bids, as well as the final allocation on a geographic map. With the help of this tool, different strategies of how to distribute bidders over PEAs shall be explored and compared among each other in terms of efficiency and runtime of the auction. The visualization tool can be programmed with any programming language. For the runtime and efficiency tests advanced C++ skills are very helpful. Requirements: Auction Theory & Market Design (IN2211) and/or advanced C++ skills. |
Gregor Schwarz |
|
Incorporating Early Bids in |
In the FUEL bid language bidders can formulate their demand for early and late bids by stating suitable adjustments in their package bids. Two other approaches come to mind and the goal of this thesis is to explore both of them more in detail and compare them with the original version of the FUEL bid language in terms of runtime and efficiency. Requirements: Auction Theory & Market Design (IN2211) and/or advanced C++ skills. |
Gregor Schwarz |
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General Information for Theses