Socially Optimal Non-discriminatory Restrictions for Continuous-Action Games
Authors: Michael Oesterle, Guni Sharon
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental results with Braess Paradox and the Cournot Game show that this method leads to an optimized social utility of the Nash Equilibria, even when the assumptions are not guaranteed to hold. |
| Researcher Affiliation | Academia | Michael Oesterle1, Guni Sharon2 1Institute for Enterprise Systems, University of Mannheim, Germany 2Department of Computer Science & Engineering, Texas A&M University |
| Pseudocode | Yes | Algorithm 1: Socially Optimal Action-Space Restrictor (SOAR) |
| Open Source Code | Yes | The notebook is publicly available at https://github.com/michoest/aaai-2023. |
| Open Datasets | No | The paper defines parameterized versions of the Cournot Game and Braess Paradox rather than utilizing external, publicly available datasets. No specific links, DOIs, or citations to public datasets are provided. |
| Dataset Splits | No | The paper conducts experiments on defined game models (Cournot Game, Braess Paradox) rather than using traditional datasets with explicit training, validation, and test splits. |
| Hardware Specification | No | The paper states that experiments were executed in a Google Colaboratory notebook but does not provide specific hardware details such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions that experiments were conducted in a Google Colaboratory notebook and provides a link to a GitHub repository, but it does not specify software dependencies with version numbers (e.g., Python version, library versions). |
| Experiment Setup | Yes | The values of ϵ were empirically chosen to provide a good balance between run-time and accuracy, but the results are actually reasonably insensitive to this choice: Varying ϵ by a factor of 5 caused the MESU to change by less than 1% in both games. Cournot Game Figure 3 shows the results of SOAR for λ {10, 11, ..., 200} with ϵ = 0.1. Braess Paradox Figure 4 shows the results of SOAR for b [4, 18] in steps of 0.1 with ϵ = 0.001. |