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..
Competition Among Contests: a Safety Level Analysis
Authors: Ron Lavi, Omer Shiran-Shvarzbard
IJCAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study a model of two competing contests. Using Mathematica c we numerically solve Eq. 7. The main conclusions are: 1. In the limit as n goes to infinity (for any constant α, β) the safety level converges to the relative prize power t (Fig. 1, 2, 3). |
| Researcher Affiliation | Academia | Ron Lavi , Omer Shiran-Shvarzbard Technion Israel Institute of Technology EMAIL, EMAIL |
| Pseudocode | No | No pseudocode or clearly labeled algorithm block is present in the paper. |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of open-source code for the methodology described. |
| Open Datasets | No | The paper performs theoretical analysis and numerical solutions of derived equations, not empirical experiments using external datasets. |
| Dataset Splits | No | The paper performs theoretical analysis and numerical solutions of derived equations, and therefore does not involve training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for the numerical computations. |
| Software Dependencies | No | The paper mentions 'Using Mathematica c' but does not provide a specific version number for this or any other software dependency. |
| Experiment Setup | No | The paper focuses on theoretical modeling and numerical analysis, and as such, does not describe an experimental setup with hyperparameters or training configurations in the context of empirical experiments. |