Decision Sciences & Systems
Technical University of Munich

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.


Optimization and Market Design
(BSc or MSc thesis)
various topics

Prof. Martin Bichler

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)

Prof. Felix Brandt

Sample Efficiency in Multiagent Reinforcement Learning via Quasirandomness

Our current research explores the computation of market equilibria in game-theoretic settings via multi-agent machine learning techniques: To do so, market participants update their behavior according to data of past market outcomes. To achieve good precision, this method requires the simulation of millions of episodes and computationally intensive Monte-Carlo integration. 
In this thesis, you will focus on methods from numerical analysis (e.g. stratified sampling, quasirandom numbers, variance reduction techniques) to analyze and improve sample-efficiency in this setting. The thesis should comprise both theoretical analysis and empirical work using implementations in python/pytorch.

This thesis can be supervised for students in the MA or IN departments.

Required: Strong background in mathematics, esp. probability theory, and numerics. Previous experience with programming in python. Basic understanding of game theory and machine learning.

Stefan Heidekrüger
Experience Replay in Reinforcement Learning

In Reinforcement Learning, an agent continuously updating its strategy is prone to “forget” previously discovered strategic behavior when only learning based on short term feedback. One type of approach to handle this problem is by replaying past experiences once in a while in order to validate the performance of the current strategy. In this project, multiple approaches to Experience Replay will be analyzed in a specific multi-agent setting. References include “Prioritized Experience Replay” by T. Schaul et al., 2015 and “Stabilising experience replay for deep multi-agent reinforcement learning” by J. Foerster et al., 2017.

This thesis can be supervised for students in the MA or IN departments.

Mandatory Requirements: Python, Deep Learning. Optional Requirements: RL, MARL, Game Theory, PyTorch.

Nils Kohring
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.

Required: advanced programming skills (e.g., Python, Matlab, at least one), mathematics, operation research.

Donghao Zhu

Modifying a bid language for
spectrum auctions

(BA Thesis)

The Flexible Use and Efficient Licensing (FUEL) bid language was proposed for conducting a radio spectrum auction in the US to allocate licenses for the new 5G network to telecommunication providers. In this thesis it shall be analyzed how small modifications of this bid language influence the runtime and efficiency of the allocation problem.

required: advanced C++ skills, helpful: knowledge in auction theory & operations research

Gregor Schwarz
Simulation of airport time slot auctions

We provide a simulation system that can be extended by further valuation models for airlines and/or bidding languages. In simulation experiments, the impact on prices, the welfare distribution, and computational costs should be analyzed for different payment rules.

required: advanced programming skills (python); helpful: auction theory, operations research

Paul Karänke

Templates and Information for Creating Thesises:

Thesis Template (latex)

Slides Template (ppt & latex)

General Information for Theses


Decision Sciences & Systems (DSS), Department of Informatics (I18), Technische Universität München, Boltzmannstr. 3, 85748 Garching, Germany
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