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].
A Mechanism Design Approach to Measure Awareness
Authors: Diodato Ferraioli, Carmine Ventre, Gabor Aranyi
AAAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Using mechanism design tools, we study the barrier between possibility and impossibility of incentive compatibility with respect to the aforementioned characteristics of subjects. We complete this study by showing how to use our mechanisms to potentially get a better understanding of consciousness. However, our interest is mainly theoretical and we do not imagine/propose to run these mechanisms in a real experimental setting. |
| Researcher Affiliation | Academia | Diodato Ferraioli DI, Universit a degli Studi di Salerno, Italy EMAIL Carmine Ventre SCM, Teesside University, UK EMAIL Gabor Aranyi SCM, Teesside University, UK EMAIL |
| Pseudocode | No | The paper describes the mechanisms (D-PDW, S-PDW) using textual descriptions and mathematical formulas, but does not present them in a structured pseudocode or algorithm block. |
| Open Source Code | No | The paper does not mention any release of source code or provide links to code repositories. |
| Open Datasets | No | The paper does not describe any dataset used for training, nor does it provide access information for any public datasets. It refers to experiments from prior work (Persaud, Mc Leod, and Cowey 2007) but does not conduct its own empirical studies requiring data. |
| Dataset Splits | No | The paper does not mention any validation dataset splits or methods for its research. |
| Hardware Specification | No | The paper is theoretical and does not describe any computational experiments or mention specific hardware specifications used for its research. |
| Software Dependencies | No | The paper does not specify any software dependencies or version numbers, as it focuses on theoretical contributions rather than practical implementation or experimental setup. |
| Experiment Setup | No | The paper does not describe any experimental setup details, hyperparameters, or system-level training settings, as its focus is on theoretical mechanism design. |