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 [1].
Learning to Resolve Social Dilemmas: A Survey
Authors: Shaheen Fatima, Nicholas R. Jennings, Michael Wooldridge
JAIR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This article is a comprehensive integrated survey of these learning approaches in the context of dilemma games. We formally introduce dilemma games and their inherent challenges. We then outline the three learning approaches and, for each approach, provide a survey of the solutions proposed for dilemma resolution. Finally, we provide a comparative summary and discuss directions in which further research is needed. |
| Researcher Affiliation | Academia | Shaheen Fatima EMAIL Nicholas R Jennings EMAIL Loughborough University, UK Michael Wooldridge EMAIL Oxford University, UK |
| Pseudocode | No | The paper is a survey of learning approaches and mechanisms. It describes various models and dynamics (e.g., replicator dynamics equation 2, Q-learning update rule equation 9), but it does not contain any structured pseudocode or algorithm blocks for a specific method or procedure developed by the authors of this paper. |
| Open Source Code | No | The paper is a survey and does not present new methodology requiring source code. It mentions 'software tools such as Open Spiel (Lanctot et al., 2019) can be greatly useful for doing an empirical comparative analysis', but this refers to a third-party tool, not source code developed by the authors for the work described in this paper. |
| Open Datasets | No | The paper is a survey of existing research and does not conduct its own experiments using a specific dataset, nor does it provide concrete access information for a dataset. It references studies that used experimental data, but no dataset is used by the authors of this paper. |
| Dataset Splits | No | The paper is a survey and does not describe any experimental work or dataset usage by the authors, therefore no dataset splits are provided. |
| Hardware Specification | No | The paper is a survey and does not describe any experimental work conducted by the authors. Therefore, no hardware specifications for running experiments are provided. |
| Software Dependencies | No | The paper is a survey and does not describe any software implementation by the authors. It mentions 'Open Spiel' as a software tool, but it does not specify versions of any software dependencies used by the authors for their own work described in this paper. |
| Experiment Setup | No | The paper is a survey of existing literature and does not present any new experimental results by the authors. Therefore, no experimental setup details, hyperparameters, or training configurations are provided. |