Incentives for Subjective Evaluations with Private Beliefs
Authors: Goran Radanovic, Boi Faltings
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We first construct a minimal peer prediction mechanism that elicits honest evaluations from a homogeneous population of agents with different private beliefs. Second, we show that it is impossible to strictly elicit honest evaluations from a heterogeneous group of agents with different private beliefs. Nevertheless, we provide a modified version of a divergence-based Bayesian Truth Serum that incentivizes agents to report consistently, making truthful reporting a weak equilibrium of the mechanism. |
| Researcher Affiliation | Academia | Goran Radanovic and Boi Faltings Ecole Polytechnique Federale de Lausanne (EPFL) Artificial Intelligence Laboratory CH-1015 Lausanne, Switzerland {goran.radanovic, boi.faltings}@epfl.ch |
| Pseudocode | No | The paper describes the mechanisms step-by-step in prose, but does not include formal pseudocode blocks or algorithms labeled as such. |
| Open Source Code | No | The paper does not provide any statement or link indicating the release of open-source code for the described methodology. |
| Open Datasets | No | This is a theoretical paper and does not use or reference any datasets for training or evaluation. |
| Dataset Splits | No | This is a theoretical paper and does not specify any dataset splits for validation. |
| Hardware Specification | No | This is a theoretical paper and does not describe the hardware used for experiments. |
| Software Dependencies | No | This is a theoretical paper and does not specify any software dependencies with version numbers. |
| Experiment Setup | No | This is a theoretical paper and does not describe any experimental setup details or hyperparameters. |