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..
Multi-Agent Systems with Quantitative Satisficing Goals
Authors: Senthil Rajasekaran, Suguman Bansal, Moshe Y. Vardi
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The paper focuses on "automata-based algorithms to find pure-strategy Nash equilibria" and showing "these algorithms extend to scenarios in which agents have multiple thresholds". It also discusses "complexity-theoretic benefit" and "PSPACE upper bound". It lacks any mention of empirical evaluation, datasets, or performance metrics from experiments. |
| Researcher Affiliation | Academia | Senthil Rajasekaran1 , Suguman Bansal2 and Moshe Y. Vardi1 1Rice University 2Georgia Institute of Technology EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper describes theoretical constructions and algorithms in prose and mathematical notation but does not include any blocks labeled as "Pseudocode" or "Algorithm". |
| Open Source Code | No | The paper does not mention providing open-source code for the methodology it describes. |
| Open Datasets | No | The paper is theoretical and does not discuss datasets for training or any experimental data. |
| Dataset Splits | No | The paper is theoretical and does not discuss dataset splits for validation or any experimental data. |
| Hardware Specification | No | This is a theoretical paper. It does not describe any hardware used for running experiments. |
| Software Dependencies | No | This is a theoretical paper. It does not list any specific software dependencies with version numbers for experimental reproducibility. |
| Experiment Setup | No | This is a theoretical paper. It does not describe any experimental setup details such as hyperparameters or training configurations. |