Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Approval Voting and Incentives in Crowdsourcing
Authors: Nihar Shah, Dengyong Zhou, Yuval Peres
ICML 2015 | Venue PDF | LLM Run Details | Input Tokens: 16,446 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,484 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also conduct preliminary empirical studies on Amazon Mechanical Turk which validate our approach. |
| Researcher Affiliation | Collaboration | Nihar B. Shah EMAIL University of California, Berkeley, CA 94720 Dengyong Zhou EMAIL Microsoft Research, Redmond, WA 98052 Yuval Peres EMAIL Microsoft Research, Redmond, WA 98052 |
| Pseudocode | Yes | Algorithm 1 Incentive mechanism for approval voting |
| Open Source Code | No | The paper states 'The entire data related to the experiments is available on the website of the first author' but does not mention code for the methodology. |
| Open Datasets | No | The paper states 'The entire data related to the experiments is available on the website of the first author' but does not provide a specific link, DOI, repository, or formal citation for a publicly available dataset. It refers to data collected by the authors. |
| Dataset Splits | No | The paper does not explicitly provide details about training, validation, or test dataset splits. |
| Hardware Specification | No | The paper mentions Amazon Mechanical Turk as the platform for crowdsourcing but does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments or models. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | In each experiment, every worker was assigned one of four mechanisms uniformly at random. The mechanisms were executed as a bonus payment based on the evaluation of the worker s performance on the gold standard questions, on top of a guaranteed payment of 10 cents. The four mechanisms tested were: Single-selection interface with additive payments: The bonus starts at zero and is increased by a fixed amount for every correct answer. Skip-based single-selection interface with multiplicative payments (Shah & Zhou, 2014): The bonus starts at a certain positive value, is reduced by a certain fraction for each skipped question, and becomes zero in case of an incorrect answer. Approval-voting interface with a fixed payment: The bonus is fixed. Approval-voting interface with the payment defined in Algorithm 1. |