Stackelberg vs. Nash in the Lottery Colonel Blotto Game
Authors: Yan Liu, Bonan Ni, Weiran Shen, Zihe Wang, Jie Zhang
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We derive the Stackelberg equilibrium for this game, formulating the leader s strategy as a bilevel optimization problem. To solve this, we develop a constructive method based on iterative game reductions, which allows us to efficiently compute the leader s optimal commitment strategy in polynomial time. Additionally, we identify the conditions under which the Stackelberg equilibrium coincides with the Nash equilibrium. Specifically, this occurs when the budget ratio between the leader and the follower equals a certain threshold, which we can calculate in closed form. |
| Researcher Affiliation | Academia | 1Renmin University of China 2Tsinghua University 3University of Bath EMAIL, EMAIL, EMAIL, EMAIL, EMAIL. All authors are affiliated with universities, indicating an academic setting. |
| Pseudocode | No | The paper describes methods and derivations but does not include any structured pseudocode or algorithm blocks. For example, it describes developing a "constructive method based on iterative game reductions" but does not present it in a pseudocode format. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code, nor does it provide any links to a code repository. A link to the full paper on arXiv is provided but this is for the paper itself, not source code. |
| Open Datasets | No | The paper is theoretical, focusing on game theory models and mathematical derivations. It does not use or refer to any empirical datasets, publicly available or otherwise, for experiments or evaluation. The paper mentions constructing "a class of instances" for demonstration (Figure 1), which refers to theoretical scenarios rather than empirical data. |
| Dataset Splits | No | The paper does not involve experiments using datasets, therefore, there is no discussion of dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper focuses on theoretical game theory and mathematical derivations. It does not describe any experimental setup or computational evaluations that would require specific hardware, thus no hardware specifications are mentioned. |
| Software Dependencies | No | The paper presents theoretical research in game theory and does not describe any experimental implementation or computational framework that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper focuses on theoretical analysis and mathematical derivations of game theory models. It does not describe any empirical experiments, and therefore, no experimental setup details, hyperparameters, or training configurations are provided. |