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..
Contest Design with Uncertain Performance and Costly Participation
Authors: Priel Levy, David Sarne, Igor Rochlin
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper studies the problem of designing contests... The paper provides a comparative game-theoretic based solution... The paper provides a comprehensive game-theoretic based analysis... As demonstrated numerically, the preference of the model to be used highly varies in the setting parameters. |
| Researcher Affiliation | Academia | Priel Levy and David Sarne Department of Computer Science Bar Ilan University, Israel EMAIL, EMAIL; Igor Rochlin School of Computer Science College of Management, Israel EMAIL |
| Pseudocode | No | The paper contains mathematical formulas, theorems, and proofs but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper uses a theoretical model with a uniform performance distribution and specific parameters (c1 = c2 = 0.16, c3 as independent parameter, M = 0.4, v0 = 0) for numerical illustration, which does not constitute a publicly available dataset. |
| Dataset Splits | No | The paper focuses on theoretical analysis and numerical illustration using a defined model. It does not involve experimental data that would require train/validation/test dataset splits. |
| Hardware Specification | No | The paper describes theoretical models and numerical illustrations but does not mention any specific hardware used for computations. |
| Software Dependencies | No | The paper does not mention any specific software or programming language versions used for its analysis or numerical illustrations. |
| Experiment Setup | Yes | The setting used includes three agents, where c1 = c2 = 0.16 and c3 is the independent parameter. All three agents are characterized by a uniform performance distribution function between 0 and 1 (i.e., f1(x) = f2(x) = f3(x) = 1 for 0 x 1 and zero otherwise). The prize to be awarded to the winner is M = 0.4 and the fallback performance is v0 = 0. |