Incentives for Truthful Information Elicitation of Continuous Signals

Authors

  • Goran Radanovic Ecole Polytechnique Federale de Lausanne (EPFL)
  • Boi Faltings Ecole Polytechnique Federale de Lausanne (EPFL)

DOI:

https://doi.org/10.1609/aaai.v28i1.8797

Keywords:

Mechanism Design, Information Elicitation, Peer Prediction

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.

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Published

2014-06-21

How to Cite

Radanovic, G., & Faltings, B. (2014). Incentives for Truthful Information Elicitation of Continuous Signals. Proceedings of the AAAI Conference on Artificial Intelligence, 28(1). https://doi.org/10.1609/aaai.v28i1.8797

Issue

Section

AAAI Technical Track: Game Theory and Economic Paradigms