Short Bio

I'm a Ph.D. student at the DSS chair supervised by Prof. Bichler. My research focuses on the computation of equilibrium strategies in incomplete information games using methods from convex optimization.

Education

• since 09/2020.   Ph.D. Student, Department of Computer Science, TUM (Munich, Germany)
• 2017 - 2020:      M.Sc. Mathematics, TUM (Munich, Germany)
• 2013 - 2017:      B.Sc. Mathematics, TUM (Munich, Germany)

Other Experiences

• Program Associate supported by the Alfred P. Sloan Foundation in the program Mathematics and Computer Science of Market and Mechanism Design at SLMath
  Berkeley, USA, 08/2023 - 12/2023
• Associated Member of the DFG Research Training Group AdONE at TUM
  Munich, Germany, since 11/2020
• Erasmus Student at Uppsala University
  Uppsala, Sweden, 08/2018 - 01/2019

 

Publications

Working Paper

• Low Revenue in Display Ad Auctions: Algorithmic Collusion vs. Non-Quasilinear Preferences
  Martin Bichler, Alok Gupta, Laura Mathews, Matthias Oberlechner
  Preprint, 2023. [ arXiv ]
   
• On the Convergence of Learning Algorithms in Bayesian Auction Games
  Martin Bichler, Stephan B. Lunowa, Matthias Oberlechner, Fabian R. Pieroth, Barabara Wohlmut
  Preprint, 2023. [ arXiv ]

Journal Publications

• Computing Bayes Nash Equilibrium Strategies in Auction Games via Simultaneous Online Dual Averaging
  Martin Bichler, Maximilian Fichtl, Matthias Oberlechner
  Operations Research, 2023, Forthcoming.
   
• Learning equilibrium in bilateral bargaining games
  Martin Bichler, Nils Kohring, Matthias Oberlechner, Fabian R. Pieroth.
  In European Journal of Operational Research, 2023 [ link ]

Conference Proceedings

• Computing Bayes Nash Equilibrium Strategies in Auction Games via Simultaneous Online Dual Averaging
  Martin Bichler, Maximilian Fichtl, Matthias Oberlechner
  24th ACM Conference on Economics and Computation (ACM-EC), 2023. [ link | arXiv ]

Peer-Reviewed Workshop Paper

• Computing Bayes Nash Equilibrium Strategies in Crowdsourcing Contests
  Martin Bichler, Markus Ewert, Matthias Oberlechner.
  In 32nd Workshop on Information Technologies and Systems (WITS-22), Copenhagen, Denmark.
   
• Computing Distributional Bayes Nash Equilibria in Auction Games via Gradient Dynamics
  Martin Bichler, Maximilian Fichtl, Matthias Oberlechner
  In Workshop on Reinforcement Learning in Games (AAAI-22, Online). [ venue | pdf ]
  Previous Version in Workshop on Learning in Presence of Strategic Behavior (NeurIPS21, Online). [ venue ]

Teaching

Classes

• Operations Research (IN0024)
  Teaching Assistant: SS21, SS22, SS23
   
• Auction Theory and Market Design (IN2211)
  Teaching Assistant: WS21/22, WS22/23

Supervised Theses

• Numerical Experiments for Equilibrium Learning with Bandit-Feedback in Contests
  Thesis, Master - Informatics
• Equilibrium Learning in Bertrand and Cournot Competition
  Interdisciplinary Project (IDP), Master - Informatics, 2023
• Development of a DGS Auction Problem Generator
  Thesis, Bachelor - Information Systems, 2023
• Application-oriented Analysis of Inventory Management in the Context of Strategic Portfolio Planning in the Automotive Sector Using Operations Research
  Thesis, Bachelor - Information Systems, 2023
• Learning Discrete Equilibrium Strategies in Auction Games
  Thesis, Master Robotics, Cognition, Intelligence, 2022
• Visualisation of different Learning Algorithms in Matrix Games
  Thesis, Bachelor - Information Systems, 2022
• Multiplicative Weights Update in Congestion Games
  Thesis, Bachelor Information Systems, 2022
• Solving the CVRPTW: Quantum Annealing vs. Exact Optimization
  Thesis, Master - Mathematics, 2022
• Visualization of Different Equilibrium Concepts in Matrix Games
  Thesis, Bachelor Information Systems, 2022
• A Comparison of No-External-Regret and No-Internal-Regret Learning Algorithmis in Matrix Games
  Thesis, Bachelor - Informatics, 2022
• No-Regret Learning in Finite Games
  Thesis, Bachelor - Information Systems, 2022
• Stable Marriage Problem: Fair Algorithms and Applications
  Thesis, Bachelor - Information Systems, 2021

For available theses topics, check out this page.