Expected Variational Inequalities
Authors: Brian Hu Zhang, Ioannis Anagnostides, Emanuel Tewolde, Ratip Emin Berker, Gabriele Farina, Vincent Conitzer, Tuomas Sandholm
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | First, there exist games for which a CE need not be a solution to the ΦLIN-EVI. In this sense, ΦLIN-EVIs yield a computationally tractable superset of Nash equilibria that is tighter than CEs. Second, computation suggests that the set of solutions of the ΦLIN-EVI for the game need not be a polyhedron, unlike the set of CEs. We provide a graphical depiction of this phenomenon in Figure 1. The figure depicts the set of ΦLIN-EVI solutions to a simple Bach or Stravinsky game... The set of marginals of ΦLIN-EVI solutions appears to have a curved boundary corresponding, we believe, to the hyperbola 10x2 - 25xy + 10y2 - 6x + 11y = 0. |
| Researcher Affiliation | Collaboration | 1Carnegie Mellon University 2Foundations of Cooperative AI Lab (FOCAL) 3Massachusetts Institute of Technology 4University of Oxford 5Strategy Robot, Inc. 6Strategic Machine, Inc. 7Optimized Markets, Inc. |
| Pseudocode | Yes | Algorithm 1 Ellipsoid against hope (EAH) under SEPor GER oracle (Daskalakis et al., 2025) input Parameters Ry, ry > 0 such that Bry( ) Y BRy(0) precision parameter ϵ > 0 constant B 1 such that µ A B for all µ (X) a SEPor GER oracle output A sparse, ϵ-approximate solution µ (X) of (14) |
| Open Source Code | No | The paper does not contain any explicit statement about releasing its own source code, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper discusses theoretical concepts and uses a simple "Bach or Stravinsky game" as an illustrative example. This is a theoretical game definition, not an empirical dataset. The paper does not mention using or making available any publicly accessible datasets for experiments. |
| Dataset Splits | No | The paper primarily presents theoretical work and uses conceptual examples rather than empirical datasets. Therefore, there is no mention of dataset splits such as training, validation, or test sets. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for computations or simulations. It focuses on theoretical algorithms and complexity without mentioning hardware specifications like CPU/GPU models or memory. |
| Software Dependencies | No | The paper does not mention any specific software dependencies or versions (e.g., programming languages, libraries, frameworks with version numbers) used for implementing the algorithms or conducting the analyses. |
| Experiment Setup | No | The paper primarily focuses on theoretical analysis and algorithm development. It does not describe any experimental setup with specific hyperparameters, training configurations, or system-level settings for machine learning models or other empirical systems. |