Validating Bayesian truth serum in large-scale online human experiments

Bayesian truth serum (BTS) is an exciting new method for improving honesty and information quality in multiple-choice survey, but, despite the method's mathematical reliance on large sample sizes, existing literature about BTS only focuses on small experiments. Combined with the prevalence of o...

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Bibliographic Details
Published in:PloS one 2017-05, Vol.12 (5), p.e0177385-e0177385
Main Authors: Frank, Morgan R, Cebrian, Manuel, Pickard, Galen, Rahwan, Iyad
Format: Article
Language:English
Subjects:
Accuracy
Analysis
Bayes Theorem
Bayesian analysis
Biology and Life Sciences
Cheating
Dishonesty
Experiments
Honesty
Human Experimentation
Humans
Incentives
Internet
Laboratories
Marketing
Methods
Models, Statistical
Multiple choice
Narcotherapy
Physical Sciences
Polls & surveys
Psychology
Religion
Research and Analysis Methods
Researchers
Response bias
Science
Social networks
Social Sciences
Studies
Surveys and Questionnaires
Trust
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Summary:Bayesian truth serum (BTS) is an exciting new method for improving honesty and information quality in multiple-choice survey, but, despite the method's mathematical reliance on large sample sizes, existing literature about BTS only focuses on small experiments. Combined with the prevalence of online survey platforms, such as Amazon's Mechanical Turk, which facilitate surveys with hundreds or thousands of participants, BTS must be effective in large-scale experiments for BTS to become a readily accepted tool in real-world applications. We demonstrate that BTS quantifiably improves honesty in large-scale online surveys where the "honest" distribution of answers is known in expectation on aggregate. Furthermore, we explore a marketing application where "honest" answers cannot be known, but find that BTS treatment impacts the resulting distributions of answers.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0177385