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149

Title *

Taking A Closer Look At The Bayesian Truth Serum: A Registered Reportuse asterix (*) to get italics

Authors *

Philipp Schoenegger & Steven VerheyenPlease use the format "First name initials family name" as in "Marie S. Curie, Niels H. D. Bohr, Albert Einstein, John R. R. Tolkien, Donna T. Strickland"

Year *

2022

Picture

Abstract *

<p>Over the past decades, psychology and its cognate disciplines have undergone substantial reform, ranging from advances in statistical methodology to significant changes in academic norms. One aspect of experimental design that has received comparatively little attention is incentivisation, i.e. the way that participants are rewarded and incentivised monetarily for their participation. While incentive compatible designs are in use in disciplines like economics, the majority of studies in psychology and experimental philosophy are constructed such that individuals’ incentives to maximise their payoffs in many cases counteract their incentives to state their true preferences honestly. This is in part because the subject matter is often self-report data about subjective topics. One mechanism that allows for the introduction of an incentive-compatible design in such circumstances is the Bayesian Truth Serum (Prelec, 2004), which rewards participants based on how surprisingly common their answer are. Recently, Schoenegger (2021) applied this mechanism in the context of Likert-scale self-reports, finding that the introduction of this mechanism significantly altered response behaviour. In this registered report, we further investigate this mechanism by (i) replicating the original result and (ii) teasing out whether the effect may be explainable by an increase in expected earnings or the addition of a prediction task. We take this project to help introduce incentivisation mechanisms into fields where they were not widely used before. </p>

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Keywords (optional)

Incentivisation, Bayesian Truth Serum, Methods, Open Science

Methods that require specific expertise (optional)

NonePlease indicate the methods that may require specialised expertise during the peer review process (use a comma to separate various required expertises).

Thematic fields *

Social sciences

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Submission date

2021-12-06 17:36:15

Recommender

Ljerka Ostojic

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