Short Bio

I am a Ph.D. student supervised by Prof. Bichler. I design scalable algorithms and theory to learn and verify equilibria in auctions and other economic games.

Education:

• 10/2017 - 01/2021:        Master in Mathematics (M. Sc.), Technical University Munich
• 10/2013 - 10/2016:        Bachelor in Mathematics (B. Sc.), Ludwig-Maximilians-University Munich

Working Experience:

• 08/2018 - 05/2021:        Data Science Intern, MaibornWolff
• 09/2017 - 08/2018:        Data Science Intern, Horvath&Partners
• 10/2016 - 01/2017:        Intern in Credit Risk and Methodology, Bayern LB

Publications

Working Papers

Verifying Approximate Equilibrium in Auctions
Fabian R. Pieroth, Tuomas Sandholm
Target: Journal of the ACM, 2025

Algorithmic Predation: Equilibrium Analysis in Dynamic Oligopolies with Smooth Market Sharing
Fabian R. Pieroth, Ole Petersen, Martin Bichler
Target: European Journal of Operational Research, 2025

Competitive Newsvendors in Agentic Supply Chains
Martin Bichler, Kassian Koeck, Fabian R. Pieroth
Target: Manufacturing & Service Operations Management, 2025

Journal Publications

Deep reinforcement learning for equilibrium computation in multi-stage auctions and contests
Fabian R. Pieroth, Nils Kohring, Martin Bichler
Management Science, 2025

Learning Equilibrium in Bilateral Bargaining Games
Martin Bichler, Nils Kohring, Matthias Oberlechner, Fabian R. Pieroth
European Journal of Operational Research (EJOR), 2022

Peer-Reviewed Conference and Workshop Papers

Algorithmic Predation: Equilibrium Analysis in Dynamic Oligopolies with Smooth Market Sharing
Fabian R. Pieroth, Ole Petersen, Martin Bichler
Accepted at Conference on Information Systems and Technology (CIST), 2025

Beyond Monotonicity: On the Convergence of Learning Algorithms in Standard Auction Games
Martin Bichler, Stephan B. Lunowa, Matthias Oberlechner, Fabian R. Pieroth, Barbara Wohlmuth
Proceedings of the 39th AAAI Conference on Artificial Intelligence (AAAI), 2025

Alpha-Rank-Collections: Analyzing Expected Strategic Behavior with Uncertain Utilities
Fabian R. Pieroth, Martin Bichler
Proceedings of the 25th ACM Conference on Economics and Computation (EC), 2024

Detecting Influence Structures in Multi-Agent Reinforcement Learning
Fabian R. Pieroth, Katherine Fitch, Lenz Belzner
Proceddings of the 41st International Conference on Machine Learning (ICML), 2024

Enabling First-Order Gradient-Based Learning for Equilibrium Computation in Markets
Nils Kohring, Fabian R. Pieroth, Martin Bichler
Proceedings of the 40th International Conference on Machine Learning, PMLR 202:17327-17342, 2023.

On Learning Stable Cooperation in the Iterated Prisoner's Dilemma with Paid Incentives
Xiyue Sun, Fabian R. Pieroth, Kyrill Schmid, Martin Wirsing, Lenz Belzner
Proceedings of the 42nd IEEE International Conference on Distributed Computing Systems Workshops (ICDCSW), 2022

 

Selected Talks

• 39th AAAI Conference on Artificial Intelligence, Philadelphia PA USA, "Beyond Monotonicity: On the Convergence of Learning Algorithms in Standard Auction Games", 2025
• 25th ACM Conference on Economics and Computation (EC), New Haven CT USA, "$\alpha$-Rank-Collections: Analyzing Expected Strategic Behavior with Uncertain Utilities", 2024
• International Conference on Operations Research, Munich Germany, "Deep Reinforcement Learning for Equilibrium Computation in Multi-Stage Auctions and Contests", 2024

Referee Services

• Programm committee, ACM Conference on Economics & Computation (EC), 2025
• Reviewer, INFORMS Journal on Computing (JOC), 2024
• Reviewer, Mathematics of Operations Research (MOR), 2024
• Programm committee, Conference on Uncertainty in AI (UAI), 2021

Grants

• German academic exchange serivce (DAAD), International research stays for computer scientists, Sep-2023 -- Mar-2024

Teaching

For available theses topics, check out this page.

Courses

• Operations Research (SS21, SS22, SS23, SS24, SS25)
• Learning in Games Seminar (SS22, SS23, SS24)
• Seminar ITUB - "IT and Management Consulting" (WS 21/22, WS22/23, WS23/24, WS24/25)
• Auction Theory and Market Design (WS21/22, WS22/23)

Supervised Theses

• Learning to Endure - An Analysis of the War of Attrition
  Bachelor - Informatics, 2025
• Reinforcement Learning for Equilibrium Computation in the Competitive Inventory Stocking Problem
  Master - Informatics, 2025
• Enhanced Policy Optimization using Reasoning with Causal Reinforcement Learning
  Master - Electrical Engineering, 2025
• Maximizing Information Gain to Improve Sample Complexity in Two-Player Zero-Sum Games
  Bachelor - Information Systems, 2025
• Equilibrium Learning in Stackelberg Colonel Blotto Games
  Master - Informatics, 2024
• Effects of Exploration and Exploitation in Sampling Strategies for Monte-Carlo Counterfactual Regret Minimization
  Master - Informatics, 2023
• Dynamics of Multi-Agent Reinforcement Learning Algorithms in a Discrete Time Oligopoly Price Competition
  Bachelor - Engineering Science, 2023
• Comparison of Deterministic and Distributional Reinforcement Learning Algorithms in Continous Auction Games
  Bachelor - Informatics, 2023
• A Comparison Between the Use of Unlabeled and Weakly Labeled Data in Active Learning for a Text Classification Problem (collaboration with MaibornWolff)
  Bachelor - Informatics, 2023
• Approximating Equilibrium Strategies in Sequential Colonel Blotto Games with Multi-Agent Reinforcement Learning
  Master - Informatics, 2022
• Analyzing Learning Dynamics in Finite N-Player Normal-Form Games with Varying Degrees of Cooperation
  Bachelor - Information Systems, 2022
• Imitation Learning for Robust Strategies in Multi-Agent Reinforcement Learning
  Master - Informatics, 2022
• Measuring Interdependencies in Multi-Agent Reinforcement Learning Systems in the Discounted Reward Setting
  Bachelor - Informatics, 2021
• Learning Dynamics in Two-sided Matching Markets
  Bachelor - Information Systems, 2021