General Information

Course instructors: Andreas S. Schulz, Alexander Grosz

The presentations will take place online or in seminar room 2906.DG.009 (local room number 6009). Some helpful advice for your presentation can be found here.

Strategic behaviour is commonplace in all kinds of competitive (and collaborative) settings, ranging from far-reaching political and economical decisions through evolutionary behaviour all the way down to two children sharing a piece of cake. Within the context of operations research, prominent examples of such problems are, e.g., establishing strategy-proof mechanisms of price setting for selling goods in auctions and more general markets and resource allocation problems of scarce resources under different notions of fairness. In this seminar, we are going to discuss a variety of game theoretic problems from a computational perspective. Based on scientific literature, we will discover different types of game theoretic models and their respective solution concepts, as well as methods to find those solutions computationally.

Prerequisites

Students are expected to have an interest in understanding and using complex quantitative models and methods. Participants should be familiar with Operations Research techniques. It is strongly advised that interested students have previously taken part in the module "Modeling and Optimization in Operations Management."

Topics

• A: Riascos-Álvarez, L.C. et al. 2024. A Branch-and-Price Algorithm Enhanced by Decision Diagrams for the Kidney Exchange Problem. Manufacturing & Service Operations Management. 26, 2 (Mar. 2024), 485–499. DOI:https://doi.org/10.1287/msom.2022.0192.
• B: Zhu, F. et al. 2023. Assign-to-Seat: Dynamic Capacity Control for Selling High-Speed Train Tickets. Manufacturing & Service Operations Management. 25, 3 (May 2023), 921–938. DOI:https://doi.org/10.1287/msom.2023.1188.
• C: Kaźmierowski, S. and Dziubiński, M. 2023. Computation of Nash Equilibria of Attack and Defense Games on Networks. Algorithmic Game Theory. Springer Nature Switzerland. 3–21. DOI:https://doi.org/10.1007/978-3-031-43254-5_1
• D: Bichler, M. and Waldherr, S. 2022. Core Pricing in Combinatorial Exchanges with Financially Constrained Buyers: Computational Hardness and Algorithmic Solutions. Operations Research. 70, 1 (Jan. 2022), 241–264. DOI:https://doi.org/10.1287/opre.2021.2132.
• E: Kobayashi, Y. et al. 2023. EFX Allocations for Indivisible Chores: Matching-Based Approach. Algorithmic Game Theory. Springer Nature Switzerland. 257–270. DOI:https://doi.org/10.1007/978-3-031-43254-5_15
• F: Elkind, E. et al. 2024. Fair Division of Chores with Budget Constraints. Algorithmic Game Theory. Springer Nature Switzerland. 55–71. DOI:https://doi.org/10.1007/978-3-031-71033-9_4
• G: Behnezhad, S. et al. 2023. Fast and Simple Solutions of Blotto Games. Operations Research. 71, 2 (Mar. 2023), 506–516. DOI:https://doi.org/10.1287/opre.2022.2261.
• H: Niazadeh, R. et al. 2022. Fast Core Pricing for Rich Advertising Auctions. Operations Research. 70, 1 (Jan. 2022), 223–240. DOI:https://doi.org/10.1287/opre.2021.2104.
• I: Conitzer, V. et al. 2022. Multiplicative Pacing Equilibria in Auction Markets. Operations Research. 70, 2 (Mar. 2022), 963–989. DOI:https://doi.org/10.1287/opre.2021.2167.
• J: Delorme, M. et al. 2024. New Algorithms for Hierarchical Optimization in Kidney Exchange Programs. Operations Research. 72, 4 (Jul. 2024), 1654–1673. DOI:https://doi.org/10.1287/opre.2022.2374.
• K: Wang, G. et al. 2024. On-Demand Ride-Matching in a Spatial Model with Abandonment and Cancellation. Operations Research. 72, 3 (May 2024), 1278–1297. DOI:https://doi.org/10.1287/opre.2022.2399.
• L: Niazadeh, R. et al. 2023. Online Learning via Offline Greedy Algorithms: Applications in Market Design and Optimization. Management Science. 69, 7 (Jul. 2023), 3797–3817. DOI:https://doi.org/10.1287/mnsc.2022.4558.
• M: Liang, J. et al. 2023. Online Passenger Flow Control in Metro Lines. Operations Research. 71, 2 (Mar. 2023), 768–775. DOI:https://doi.org/10.1287/opre.2022.2417.
• N: Chen, Q. et al. 2024. Real-Time Spatial–Intertemporal Pricing and Relocation in a Ride-Hailing Network: Near-Optimal Policies and the Value of Dynamic Pricing. Operations Research. 72, 5 (Sep. 2024), 2097–2118. DOI:https://doi.org/10.1287/opre.2022.2425.
• O: Qi, M. et al. 2024. Sequential Competitive Facility Location: Exact and Approximate Algorithms. Operations Research. 72, 1 (Jan. 2024), 300–316. DOI:https://doi.org/10.1287/opre.2022.2339.