Incentives for Truthful Information Elicitation of Continuous Signals
Authors: Goran Radanovic, Boi Faltings
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We propose a novel mechanism called the Divergence-based Bayesian Truth Serum. It allows non-binary signals and is incentive-compatible even for small populations, without requiring additional restrictions on the BTS setting. Moreover, the divergence-based BTS is guaranteed to be individually rational with bounded payments, and, for discrete signals, it permits differences in agents prior beliefs. Furthermore, it is the first BTS mechanism that can be applied to continuous domains. The paper focuses on theoretical proofs and propositions (e.g., Theorem 1, Theorem 2, Theorem 3, Proposition 1, Proposition 2, Proposition 3, Proposition 4) for the proposed mechanism without conducting empirical studies or experiments on datasets. |
| 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 algorithms in prose and uses mathematical formulations (e.g., scoring functions) but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any information about open-source code availability or links to repositories. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments on a publicly available dataset. It discusses theoretical signal values and prior distributions (e.g., Gaussian prior) but does not refer to specific datasets with access information. |
| Dataset Splits | No | The paper does not conduct empirical experiments with datasets, and therefore does not specify training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not specify any software dependencies or their version numbers required for reproducibility. |
| Experiment Setup | No | The paper does not describe an experimental setup with specific hyperparameters or system-level training settings, as it is a theoretical work. |