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 embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Strategic Signaling for Selling Information Goods
Authors: Shani Alkoby, David Sarne, Igal Milchtaich
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The paper provides a game theoretic analysis of this strategic signaling model. The analysis makes three primary contributions. [...] We use this application domain for some of the numerical examples introduced onwards. |
| Researcher Affiliation | Academia | 1Ariel University, Ariel, Israel 2Bar Ilan University, Ramat-Gan, Israel |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any information about open-source code for the described methodology. |
| Open Datasets | No | The paper uses numerical examples with illustrative values rather than publicly available datasets, and does not provide concrete access information for any dataset. |
| Dataset Splits | No | The paper describes a theoretical model and numerical examples, and does not mention specific training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific hardware specifications used for running computations or simulations. |
| Software Dependencies | No | The paper does not mention any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper focuses on theoretical analysis and game theory; therefore, it does not describe an experimental setup with hyperparameters or training settings. |