Accounting for Dissenting Evidence

Last registered on June 27, 2025
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Pre-Trial

Trial Information

General Information

Title
Accounting for Dissenting Evidence
RCT ID
AEARCTR-0016171
Initial registration date
June 24, 2025

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
June 27, 2025, 8:37 AM EDT

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Locations

Country
Austria
Region
Country
United Kingdom of Great Britain and Northern Ireland
Region
Country
United States of America
Region

Primary Investigator

Name
Felix Fattinger
Affiliation
Vienna University of Economics and Business
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Other Primary Investigator(s)

PI Name
Peter Bossaerts
PI Affiliation
University of Cambridge
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PI Name
Elena Asparouhova
PI Affiliation
University of Utah
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PI Name
Wenhao Yang
PI Affiliation
University of North Carolina at Charlotte
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PI Name
Akos Barnabas Barta
PI Affiliation
Vienna University of Economics and Business
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Additional Trial Information

Status
In development
Start date
2025-06-09
End date
2025-12-31
Keywords
Behavior, Finance & Microfinance
Additional Keywords
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
In this project, we invoke a result from mathematical statistics (Doob, 1949) to identify a curious phenomenon, namely: as it accumulates, dissenting evidence (i.e., evidence against the truth) necessarily becomes more extreme. With the vast majority of evidence eventually supporting the truth, the result is polarization. We design a laboratory experiment that investigates humans’ awareness (ignorance) of this phenomenon and the consequences on their beliefs and demand for additional evidence. Our findings may provide novel insights into social interaction and public opinion formation that are of particular relevance during times of algorithmic and attention-driven news filtering.
External Link(s)

Registration Citation

Citation
Asparouhova, Elena et al. 2025. "Accounting for Dissenting Evidence." AEA RCT Registry. June 27. https://doi.org/10.1257/rct.16171-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
Our experiment will consist of repeated (independent) individual decision-making tasks. Confronted with two possible scenarios (different two-color compositions of a given urn), participants will be provided with accumulating evidence with respect to two opposing hypotheses (regarding the urn's true majority color). Importantly, participants are fully informed about the probabilistic structure that underlies the evidence accumulation for either hypothesis. We will elicit participants’ beliefs about evidence realizations as well as their willingness to pay for additional evidence. To control for risk preferences, the additional evidence will be provided in two different forms: (1) sample based on one draw vs. (2) sample based on average across 10,000 independent draws (with replacement). Our experimental design entirely abstains from any deception.
Intervention (Hidden)
Intervention Start Date
2025-06-09
Intervention End Date
2025-07-31

Primary Outcomes

Primary Outcomes (end points)
We focus on particiapnts' beliefs about competing hypotheses and their willingness to pay for additional evidence. A main control will consist of their performance in a standardized probabilistic reasoning task.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The experiment consists of three parts. In Part 1, participants read through detailed instructions, including a visualized demonstration interactively showcasing how to resample from a given urn. At the end of Part 1, participants have to passe a comprehension test in order to proceed. Only if at least 6/8 comprehension questions are answered correctly, participants are allowed to continue to Part 2. If participants fail the comprehension question they are automatically returned to Prolific and only collect the base fee for their time.

In Part 2, the main part of the experiment, participants go through 3 periods, each consisting of three phases. For each period, they are endowed with 2 GBP. In phase 1, they observe the initial sample from the randomly chosen urn and are asked about their beliefs regarding the urn's true majority color. In phase 2, they can decide whether to resample a one-draw sample and if so, what their willingness-to-pay for such a sample is. We elicit participants' willingness-to-pay via a standard Becker–DeGroot–Marschak mechanism (prices ranging from 0 to 1 GBP). In phase 3, they can decided whether to resample an average-draw sample, averaged across 10,000 independent draws, a and if so, what their willingness-to-pay for such a sample is. For both phase 2 and phase 3, the resulting likelihood ratio for the two competing hypotheses (true majority color of the underlying urn) is computed. This likelihood-ratio (capped at 1) determines the chance of winning 3 GBP in a subsequent lottery. At the end, one of the 3 periods is randomly drawn and the two respective lotteries are played out for real.

In Part 3, participants are asked to complete a standard probabilistic reasoning task (6 questions) and fill in a short survey about their educational background. The experiment is implemented with oTree and conducted via Prolific with college-educated participants from the UK and US.
Experimental Design Details
Randomization Method
The randomization is implemented by a computer. The underlying urn selection, the initial samples, and all additional samples are drawn randomly. The only restriction in place is that participants initial sample is informative (excluding samples with 50:50 color distributions). Finally, lottery outcomes are also fully determined randomly adhering to the probabilities implied by participants' choices.
Randomization Unit
Randomization (draws from urns and lottery outcomes) occurs at the individual participant level.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
individual decision-making tasks without further clustering.
Sample size: planned number of observations
Per participant, we collect 3x2 belief estimates (before and after observing the implied likelihood-ratio), 3 resampling decisions for the one-draw sample (including conditional willingness-to-pay measures), and 3 resampling decisions for the average-draw sample (including conditional willingness-to-pay measures). In total, we are expecting approximately 1,500 observations for each of the above variables.
Sample size (or number of clusters) by treatment arms
Approximately 500 participants (depending on budget restrictions implied by random payments)
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Director of Research in the Faculty of Economics, University of Cambridge
IRB Approval Date
2025-06-05
IRB Approval Number
UCAM-FoE-25-04
Analysis Plan

Post-Trial

Post Trial Information

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Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials