Deep Bayesian Trust: A Dominant and Fair Incentive Mechanism for Crowd
Authors: Naman Goel, Boi Faltings1996-2003
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through numerical experiments, we show the robustness of our mechanism under various reporting strategies of the workers. In a preliminary study conducted on Amazon Mechanical Turk, we observe that the mechanism helps in eliciting effort and improving the quality of responses. |
| Researcher Affiliation | Academia | Naman Goel, Boi Faltings Artificial Intelligence Laboratory Ecole Polytechnique F ed erale de Lausanne Lausanne, Switzerland, 1015 {naman.goel, boi.faltings}@epfl.ch |
| Pseudocode | Yes | Mechanism 1 : The Deep Bayesian Trust Mechanism 1. Assign a set of tasks to the oracle o and obtain its answers on the tasks. 2. Initialize an Informative Answer Pool (IAP) with the answers given by oracle. [...] |
| Open Source Code | No | The supplementary material for this paper is available on authors website. |
| Open Datasets | No | The paper mentions 'Numerical Simulations' where 'The proficiency matrices of different workers were generated independently'. This implies simulated data, not a publicly available dataset. It also mentions 'a preliminary study conducted on Amazon Mechanical Turk' but provides no access details for any data from this study. |
| Dataset Splits | No | The paper describes simulation settings ('Workers were simulated to be hired in 4 rounds, with 5, 25, 125 and 625 workers in successive rounds.') but does not specify any training, validation, or test dataset splits for real-world datasets. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU, CPU models, memory) used for running the numerical simulations or the Amazon Mechanical Turk study. |
| Software Dependencies | No | The paper does not specify any software dependencies or their version numbers used for the experiments or simulations. |
| Experiment Setup | Yes | Workers were simulated to be hired in 4 rounds, with 5, 25, 125 and 625 workers in successive rounds. K was set to 2 in all the experiments discussed in the paper. The proficiency matrices of different workers were generated independently such that the diagonal entries Ai[g, g] g [K] were β(5, 1) distributed. |