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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Blameworthiness in Strategic Games
Authors: Pavel Naumov, Jia Tao3011-3018
AAAI 2019 | Venue PDF | LLM Run Details | Input Tokens: 17,806 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,675 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The main technical result is a sound and complete bimodal logical system that describes properties of blameworthiness in one-shot games. In this paper we propose a complete logical system for reasoning about another form of responsibility that we call blameworthiness: a coalition is blamable for an outcome ϕ if ϕ is true, but the coalition had a strategy to prevent ϕ. The main technical result of this paper is a sound and complete bimodal logical system describing the interplay between group blameworthiness modality and necessity (or universal truth) modality. |
| Researcher Affiliation | Academia | Pavel Naumov Department of Mathematical Sciences Claremont Mc Kenna College Claremont, California 91711 EMAIL Jia Tao Department of Computer Science Lafayette College Easton, Pennsylvania 18042 EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not mention providing access to source code. |
| Open Datasets | No | The paper does not discuss the use of any datasets for training. |
| Dataset Splits | No | The paper does not discuss training/validation/test dataset splits. |
| Hardware Specification | No | The paper does not mention any hardware specifications used for experiments. |
| Software Dependencies | No | The paper does not mention any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper does not provide details about an experimental setup or hyperparameters. |