Incentives for truthful information elicitation of continuous signals

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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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Radanovic, G., & Faltings, B. (2014). Incentives for truthful information elicitation of continuous signals. In Proceedings of the National Conference on Artificial Intelligence (Vol. 1, pp. 770–776). AI Access Foundation. https://doi.org/10.1609/aaai.v28i1.8797

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