Short Bio

I'm a PhD student at the Chair of Decision Sciences and Systems supervised by Martin Bichler. My research focusses on algorithms for computing Walrasian and Bayes-Nash equilibria in auction games. I mainly use methods from online learning, optimization, and (discrete) convex analysis.

Curriculum Vitae

• 09/2021-01/2022: Research internship at Facebook Core Data Science 
• Since 2019: AdONE associated researcher, TUM
• Since 2018: PhD student at the Chair of Decision Sciences and Systems, TUM
• 2014-2017: Graduation as Master of Science in Mathematics, TUM
• 2011-2014: Undergraduate studies in Mathematics (elite program Topmath) with minor Computer Science, TUM

Awards

• Study Award 2014 for excellent study performance

Publications

Working Papers

Computing Bayes Nash Equilibrium strategies in auction games via gradient dynamics.
M. Fichtl, M. Oberlechner, M. Bichler. Major revision, Operations Research [arXiv]

Computing Candidate Prices in Budget-Constrained Product-Mix Auctions.
M. Fichtl. [arXiv]

Journal Publications

Strong substitutes: structural properties, and a new algorithm for competitive equilibrium prices.
E. Baldwin, M. Bichler, M. Fichtl, and P. Klemperer. Mathematical Programming, 2022. [link]

On the expressiveness of assignment messages.
M. Fichtl. Economics Letters, 2021. [link | arXiv]

Learning equilibria in symmetric auction games using artificial neural networks.
M. Bichler, M. Fichtl, S. Heidekrüger, N. Kohring, P. Sutterer.  Nature Machine Intelligence, (3), 2021. [pdf]

Walrasian equilibria from an optimization perspective: A guide to the literature.
M. Bichler, M. Fichtl, G. Schwarz. Naval Research Logistics, 2020; 1 – 18. [link]

Conference Proceedings

Core-stability in assignment markets with financially constrained buyers.
E. Batziou, M. Bichler, M. Fichtl. EC '22: Proceedings of the 23rd ACM Conference on Economics and Computation, 2022. [link | arXiv]

Peer-Reviewed Workshop Papers

Computing distributional bayes nash equilibria in auction games via gradient dynamics.
M. Fichtl, M. Oberlechner, M. Bichler. AAAI-22 Workshop on Reinforcement Learning in Games, 2022.

 

Teaching

Courses

• Operations Research (Summer Terms 18, 19, 20, 21, 22)
• Auction Theory (Winter Terms 18/10, 19/20)

Supervised Student Projects

• Bachelor Thesis: Rebalancing Optimization of Bike Sharing Systems, Nicolas Ullmann, 2022
• Bachelor Thesis: Implementation and Visualization of the Branch-and-Bound Algorithm in Java using the Simplex Algorithm, Marc Fett, 2022
• Bachelor Thesis: Visualization of TSP algorithms and heuristics, Nejla Zenuni, 2022
• Master Thesis: Computing Equilibria in Auction Games: A Gradient-Based Approach, Laura Mathews, 2021
• Bachelor Thesis: Computation of a Home Office Plan, Daniel Anderson, 2021
• Bachelor Thesis: Developing A Visualizer for the Simplex Method, Ilias Sulgin, 2021
• Bachelor Thesis: Approximation and the Competitive Equilibrium in Indivisible Fisher Markets, Peter Pfrommer, 2021
• Bachelor Thesis: Experimental Evaluation of Submodular Flow Algorithms, Giuliano Gaub, 2020
• Bachelor Thesis: Algorithmen zur Maximierung Submodularer Funktionen, Yongli Huang, 2020
• Bachelor Thesis: Computing equilibrium prices in Fisher Markets, Hasan Postoglu, 2019
• Bachelor Thesis: Computing bids for product-mix auctions, Patrick Ennemoser, 2019