Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Computing Nash Equilibria in Potential Games with Private Uncoupled Constraints
Authors: Nikolas Patris, Stelios Stavroulakis, Fivos Kalogiannis, Rose Zhang, Ioannis Panageas
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we empirically validate our theoretical results using constrained congestion games as a testing ground. |
| Researcher Affiliation | Academia | 1University of California, Irvine, 2Archimedes Research Unit |
| Pseudocode | Yes | Algorithm 1: IGDλ: Independent Gradient Descent on ϕ( ) = maxλ L( , λ) of the regularized Lagrangian L |
| Open Source Code | Yes | 1The code is available at the Git Hub repository: https://github.com/steliostavroulakis/constrained-potential-games |
| Open Datasets | No | The paper describes a simulation environment for 'constrained congestion games' with a 'rooted directed acyclic graph (DAG)' and 'five players' with defined 'congestion functions'. This describes the experimental setup and parameters, not a pre-existing or released dataset. |
| Dataset Splits | No | The paper describes metrics of convergence for evaluating the algorithm but does not mention specific training, validation, or test dataset splits, nor does it describe a cross-validation setup. |
| Hardware Specification | No | The paper describes its experimental setup in Section 5 but does not specify any particular hardware, such as GPU or CPU models, or cloud computing resources used for the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies or their version numbers, such as programming language versions, library versions, or specific solver versions. |
| Experiment Setup | Yes | Our experimental setup involves a rooted directed acyclic graph (DAG)... The graph comprises four paths connecting a source node s and a target node t... In addition, there are five players... The congestion experienced on each path is influenced by the number of players selecting that particular route... Let the learning rate η be 1/β, where β = c/µ and c = 4((n Amax)2 + (Λmaxγ)2) is constant. |