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Related Experiment Videos

A Bayesian truth serum for subjective data.

Drazen Prelec1

  • 1Massachusetts Institute of Technology, Sloan School of Management, E56-320, 38 Memorial Drive, Cambridge, MA 02139, USA. dprelec@mit.edu.

Science (New York, N.Y.)
|October 16, 2004
PubMed
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This study introduces a novel scoring method to obtain truthful subjective data when objective truth is unknown. It rewards answers more common than predicted, ensuring accuracy regardless of perceived consensus.

Area of Science:

  • Social Sciences
  • Decision Sciences
  • Information Science

Background:

  • Subjective judgments are crucial for science and policy.
  • Assessing the truthfulness of subjective judgments is challenging due to a lack of public criteria.
  • Objective truth is often unknowable in many domains.

Purpose of the Study:

  • To present a scoring method for eliciting truthful subjective data.
  • To address the challenge of assessing judgmental truthfulness in the absence of objective truth.
  • To develop a method that works even when objective truth is unknowable.

Main Methods:

  • A novel scoring method is proposed.
  • The method assigns higher scores to answers that are more common than collectively predicted by the population.

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  • Predictions are drawn from the same population providing the answers.

    • Main Results:

    • The proposed scoring criterion removes bias towards consensus.
    • Truthful answers are incentivized by maximizing expected scores.
    • The method ensures truthful answers are rewarded even if respondents believe they hold a minority view.

    Conclusions:

    • The developed scoring method effectively elicits truthful subjective data.
    • This approach provides a reliable way to assess judgmental truthfulness when objective truth is unattainable.
    • It offers a valuable tool for improving the quality of information in science and policy-making.