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].

Blameworthiness in Strategic Games

Authors: Pavel Naumov, Jia Tao3011-3018

AAAI 2019 | Venue PDF | LLM Run Details

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.