Maximilian Fichtl

Phone: +49 (0) 89 289 - 17528
Office: Room 01.10.058
Boltzmannstr. 3
85748 Munich, Germany
Hours: by arrangement

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


  • Study Award 2014 for excellent study performance


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.





  • 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