Short Bio

After receiving my M.Sc. in Mathematics at the Technical University of Munich, I started my my Ph.D. in the computer science department under the supervision of Martin Bichler. In my research, I investigate learning dynamics in multi-agent systems with application to economic settings such as auctions and contests. In particular, I am interested in the underlying structures that allow simple learning algorithms to reach the equilibrium strategies in these games. In most of my projects I rely on methods from online convex optimization and numerical simulations.

Education

• Ph.D. Student in the Department of Computer Science at TUM
  Munich, Germany, since 09/2020
• M.Sc. Mathematics at TUM
  Munich, Germany, 04/2017 - 06/2020
• B.Sc. Mathematics at TUM
  Munich, Germany, 10/2013 - 01/2017

Other Experiences

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

 

Publications

Working Paper

• Online Optimization Algorithms in Repeated Price Competition: Equilibrium Learning and Algorithmic Collusion
  Martin Bichler, Julius Durmann,  Matthias Oberlechner
  Working Paper, 2024 [arXiv]

Journal Publications

• Algorithmic Pricing and Algorithmic Collusion
  Martin Bichler, Julius Durmann, Matthias Oberlechner
  Accepted as Catchwords Article in Business & Information Systems Engineering (BISE), 2025 [arXiv]
   
• Characterizing the Convergence of Game Dynamics via Potentialness
  Martin Bichler, Davide Legacci, Panayotis Mertikopoulos, Matthias Oberlechner, Bary Pradelski
  Transactions on Machine Learning Research, 2025 [OpenReview| arXiv]
   
• Computing Bayes Nash Equilibrium Strategies in Auction Games via Simultaneous Online Dual Averaging
  Martin Bichler, Maximilian Fichtl, Matthias Oberlechner
  Operations Research, 2023 [DOI| arXiv]
   
• Learning equilibrium in bilateral bargaining games
  Martin Bichler, Nils Kohring, Matthias Oberlechner, Fabian R. Pieroth
  European Journal of Operational Research, 2023 [DOI]

Conference Papers

• On the Convergence of Learning Algorithms in Bayesian Auction Games
  Martin Bichler, Stephan B. Lunowa, Matthias Oberlechner, Fabian R. Pieroth, Barabara Wohlmuth
  39th Annual AAAI Conference on Artificial Intelligence (AAAI-25), 2025, Philadelphia (USA) [arXiv]
   
• Low Revenue in Display Ad Auctions: Algorithmic Collusion vs. Non-Quasilinear Preferences
  Martin Bichler, Alok Gupta, Laura Mathews, Matthias Oberlechner
  Conference on Information Systems and Technology (CIST), 2024, Seattle (USA). [arXiv]
  Presented at International Conference on Operations Research 2024 (OR2024), 2024, Munich (Germany).
  Previous version presented at Ninth Marketplace Innovation Workshop (MIW), 2024, Online.
   
• 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, London (UK). [DOI| arXiv]

Workshop Papers

• Computing Bayes Nash Equilibrium Strategies in Crowdsourcing Contests
  Martin Bichler, Markus Ewert, Matthias Oberlechner
  In 32nd Workshop on Information Technologies and Systems (WITS-22), 2022, 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), 2022, Online. [venue | pdf]
  Previous version in Workshop on Learning in Presence of Strategic Behavior (NeurIPS21), 2021, Online.

Teaching

Classes

• Operations Research (IN0024) 
  Teaching Assistant: SS21, SS22, SS23, SS24
   
• Auction Theory and Market Design (IN2211)
  Teaching Assistant: WS21/22, WS22/23

Supervised Theses

• Learning with Contextual Bandits in Auctions
  Thesis, Master - Information Systems, 2025
• Learning Bayes-Nash Equilibrium Strategies in Auctions using Discontinuous Neural Networks
  Thesis, Bachelor - Information Systems, 2024
• Analyzing the Potentialness of Random Games using the Hodge Decomposition
  Thesis, Bachelor - Informatics, 2024
• Numerical Methods for Variational Inequalities and Application to Equilibrium Learning in Game Theory
  Thesis, Master - Mathematics in Data Science, 2024
• Learning in Continuous Games with Bandit Feedback
  Thesis, Bachelor - Information Systems, 2024
• Numerical Experiments for Equilibrium Learning with Bandit-Feedback in Contests
  Thesis, Master - Informatics, 2023
• 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.