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 Framework for Measuring Information Asymmetry
Authors: Yakoub Salhi2983-2990
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We propose in this work a general logic-based framework for measuring the information asymmetry between two parties. We define the notion of information asymmetry measure through rationality postulates. We further introduce a syntactic concept, called minimal question subset (MQS), to take into consideration the fact that answering some questions allows avoiding others. This concept is used for defining rationality postulates and measures. Finally, we propose a method for computing the MQSes of a given situation of information asymmetry. |
| Researcher Affiliation | Academia | Yakoub Salhi CRIL, U. Artois & CNRS F-62300 Lens, France |
| Pseudocode | Yes | Algorithm 1: An approach for computing the MASes w.r.t. a given model of the knowledge part. |
| Open Source Code | No | The paper does not provide any statement about releasing open-source code or a link to a code repository. |
| Open Datasets | No | The paper uses illustrative examples (e.g., voter and candidates, car statements) to explain concepts but does not utilize or refer to any publicly available datasets for training models. |
| Dataset Splits | No | The paper does not describe any training, validation, or testing splits as it is a theoretical work and does not involve dataset-based experiments. |
| Hardware Specification | No | The paper is theoretical and does not describe experiments that would require hardware specifications. |
| Software Dependencies | No | The paper mentions using a "SAT solver" but does not specify any version numbers for this or any other software dependencies. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup or hyperparameters. |