E. Baldwin, M. Bichler, M. Fichtl, and P. Klemperer. Strong substitutes: structural properties, and a new algorithm for competitive equilibrium prices. Mathematical Programming, 191(2):1436–4646, 2022. [ DOI | pdf ]

E. Batziou, M. Bichler, and M. Fichtl. Core-stability in assignment markets with financially constrained buyers. ACM Conference on Economics and Computation, 23, 2022. [ DOI | pdf ]

M. Fichtl, M. Oberlechner, and M. Bichler. Computing distributional bayes nash equilibria in auction games via gradient dynamics. In AAAI-22 Workshop on Reinforcement Learning in Games (AAAI-RLG 22), Online, Online, 2022. [ pdf ]

M. Fichtl. On the expressiveness of assignment messages. Economics Letters, 208:110051, 2021. [ DOI | http ]
In this note we prove that the class of valuation functions representable via integer assignment messages is a proper subset of strong substitutes valuations. Thus, there are strong substitutes valuations not expressible via assignment messages.

M. Bichler, M. Fichtl, S. Heidekrüger, N. Kohring, and P. Sutterer. Learning equilibria in symmetric auction games using artificial neural networks. Nature Machine Intelligence, 3:687–695, August 2021. [ DOI | link | pdf ]

M. Fichtl, M. Oberlechner, and M. Bichler. Approximating bayes nash equilibria in auction games via gradient dynamics. In 2021 NeurIPS Workshops on Strategic Machine Learning, Online, Online, 2021.

M. Bichler, M. Fichtl, and G. Schwarz. Walrasian equilibria from an optimization perspective: A guide to the literature. Naval Research Logistics, 68(4):496–513, 2020. [ DOI ]