Incentives for Truthful Information Elicitation of Continuous Signals
Abstract
We consider settings where a collective intelligence is formed by aggregating information contributed from many independent agents, such as product reviews, community sensing, or opinion polls. We propose a novel mechanism that elicits both private signals and beliefs. The mechanism extends the previous versions of the Bayesian Truth Serum (the original BTS, the RBTS, and the multi-valued BTS), by allowing small populations and non-binary private signals, while not requiring additional assumptions on the belief updating process. For priors that are sufficiently smooth, such as Gaussians, the mechanism allows signals to be continuous.
Cite
Text
Radanovic and Faltings. "Incentives for Truthful Information Elicitation of Continuous Signals." AAAI Conference on Artificial Intelligence, 2014. doi:10.1609/AAAI.V28I1.8797Markdown
[Radanovic and Faltings. "Incentives for Truthful Information Elicitation of Continuous Signals." AAAI Conference on Artificial Intelligence, 2014.](https://mlanthology.org/aaai/2014/radanovic2014aaai-incentives/) doi:10.1609/AAAI.V28I1.8797BibTeX
@inproceedings{radanovic2014aaai-incentives,
title = {{Incentives for Truthful Information Elicitation of Continuous Signals}},
author = {Radanovic, Goran and Faltings, Boi},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2014},
pages = {770-776},
doi = {10.1609/AAAI.V28I1.8797},
url = {https://mlanthology.org/aaai/2014/radanovic2014aaai-incentives/}
}