Learning Quadratic Games on Networks
Authors: Yan Leng, Xiaowen Dong, Junfeng Wu, Alex Pentland
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Both synthetic and real-world experiments demonstrate the effectiveness of the proposed frameworks, which have theoretical as well as practical implications for understanding strategic interactions in a network environment. |
| Researcher Affiliation | Academia | 1Mc Combs School of Business, The University of Texas at Austin, Austin, TX, USA 2Department of Engineering Science, University of Oxford, Oxford, UK 3College of Control Science and Engineering, Zhejiang University, Hangzhou, China 4Media Lab, Massachusetts Institute of Technology, Cambridge, MA, USA. |
| Pseudocode | Yes | Algorithm 1 Learning Games with Independent Marginal Benefits Algorithm 2 Learning Games with Homophilous Marginal Benefits |
| Open Source Code | No | The information is insufficient. The paper does not contain any explicit statements or links indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We consider inferring a social network between households in a village in rural India (Banerjee et al., 2013). data can be accessed via https://atlas.media.mit. edu/en/resources/data/. The voting statistics were obtained via http://www.swissvotes.ch. |
| Dataset Splits | No | The information is insufficient. The paper does not explicitly provide specific dataset split information (percentages, sample counts, or citations to predefined splits) for training, validation, or test sets. |
| Hardware Specification | No | The information is insufficient. The paper does not provide any specific hardware details (such as GPU models, CPU types, or memory) used for running the experiments. |
| Software Dependencies | Yes | In our experiments, we solve the problem of Eq. (5) using the Python software package CVXOPT (Andersen et al., Version 1.2.0. Available at cvxopt.org, 2018). |
| Experiment Setup | Yes | In the following and all subsequent analyses, we choose ρ(βG) = 0.6, and fix the parameters in Algorithm 2 to be the ones that lead to the best learning performance. We tune β within the range of β [ 3, 3]. The best performance of Algorithm 1 is obtained with β = 0.1, θ1 = 2 8.5, and θ2 = 21, while that of Algorithm 2 is obtained with β = 2.6, θ1 = 27, and θ2 = 2 5.5. |