Multi-Scale Games: Representing and Solving Games on Networks with Group Structure

Authors: Kun Jin, Yevgeniy Vorobeychik, Mingyan Liu5497-5505

AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental Our numerical experiments demonstrate that the proposed approaches enable orders of magnitude improvements in scalability when computing Nash equilibria in such games.
Researcher Affiliation Academia 1 University of Michigan, Ann Arbor 2 Washington University in St. Louis
Pseudocode Yes Algorithm 1: BRD Algorithm
Open Source Code No The paper does not provide any concrete access information (link, explicit statement, or reference to supplementary materials) for open-source code for the described methodology.
Open Datasets No The paper mentions generating synthetic data ('We construct random 2-level games', 'The parameters of the utility functions are sampled uniformly in [0, 1]') but does not provide access information (link, DOI, repository, or formal citation) for a publicly available or open dataset.
Dataset Splits No The paper does not specify explicit training, validation, or test dataset splits (e.g., percentages, sample counts, or predefined citations) required for reproduction.
Hardware Specification Yes All experiments were performed on a machine with A 6-core 2.60/4.50 GHz CPU with hyperthreaded cores, 12MB Cache, and 16GB RAM.
Software Dependencies No The paper does not provide specific software dependencies with version numbers (e.g., programming languages, libraries, or solvers with their versions) that would be needed for replication.
Experiment Setup Yes We construct random 2-level games with utility functions based on Equation (11). Specifically, we generalize this utility so that Equation (11) represents only the level-1 portion, u(1) i , and let the level-2 utilities be u(2) k (xk,x Ik) = x(2) k P p=k vkpx(2) p for each group k. At every level, the existence of a link between two agents follows the Bernoulli distribution where Pexist = 0.1. If a link exists, we then generate a parameter for it. The parameters of the utility functions are sampled uniformly in [0, 1] without requiring symmetry.