How redundant information can help overcome bias

Last registered on May 18, 2026
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Pre-Trial

Trial Information

General Information

Title
How redundant information can help overcome bias
RCT ID
AEARCTR-0018310
Initial registration date
May 13, 2026

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
May 18, 2026, 7:02 AM EDT

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

Locations

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Primary Investigator

Name
Aaron Nicholas
Affiliation
Deakin University
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Other Primary Investigator(s)

PI Name
Francisco da Silva
PI Affiliation
Deakin University
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PI Name
Nicolás Figueroa González
PI Affiliation
Pontificia Universidad Católica de Chile
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PI Name
Ruben Jara
PI Affiliation
Pontificia Universidad Católica de Chile
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Additional Trial Information

Status
On going
Start date
2026-05-13
End date
2026-12-31
Keywords
Behavior, Lab
Additional Keywords
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
A common response to the potential manipulation of data by data collectors is the obscuring or transforming of outcome information in order to protect the integrity of the process; e.g. through double blinding, or encryption. This experimental research aims to:
1) check if individuals are able to recognize incentives to manipulate information even when such data have been encrypted by having their labels removed ('outcome blinding').
2) check whether a specific form of encryption (the introduction of redundant labels) successfully reduces manipulation that arose despite outcome blinding.
External Link(s)

Registration Citation

Citation
da Silva, Francisco et al. 2026. "How redundant information can help overcome bias." AEA RCT Registry. May 18. https://doi.org/10.1257/rct.18310-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
Two treatments to be conducted through Qualtrics, with recruitment from Prolific's panel.

*Baseline treatment*
This is a within-subject design, with three decisions in a fixed sequence.
Participants are given a hypothetical scenario where they are electoral officers attempting to forward the results of an election with two candidates A and B. However, the information they have has been encrypted, and they are uncertain whether the candidate they prefer to win has actually won. The encrypted message is of the form where they see two generic labels, of which one of them is crossed, but the participant does not know which candidate the crossed label belongs to. Participants have to decide whether to forward the information they receive, or to manipulate it by switching the cross to the other label. Participants are paid if the cross is located next to their preferred candidate.
Participants are told the probability that their preferred candidate won.
The three decisions vary as follows:
(1) The probability is set such that they have no incentive to manipulate the information.
(2) The probability is set such that they have incentive to manipulate the information.
(3) The probability is set equal to scenario (2); in addition, the set of labels are expanded to four, with participants told that one of them corresponds to their preferred candidate, and the remaining three correspond to the other candidate; this effective removes the incentive to manipulate the information.

*Do-no-harm treatment*
Identical to the baseline, except the following:
(1) The probability is set such that they have incentive to manipulate the information.
(2) The probability is set such that they have no incentive to manipulate the information.
(3) The probability is set equal to scenario (2); in addition, the set of labels are expanded to four, with participants told that three of them correspond to their preferred candidate, and the remaining one corresponds to the other candidate; while this increases the chance of being paid when manipulating information, the optimal decision remains not manipulating information
Intervention Start Date
2026-05-13
Intervention End Date
2026-12-31

Primary Outcomes

Primary Outcomes (end points)
Let B1, B2, B3 be the proportion of participants who choose to manipulate information in the three baseline treatment decisions, and D1, D2, D3 the corresponding proportions in the Do-no-harm treatment.

We have two primary tests:

(1) B2>B1; D1>D2 and (B2+D1)>(B1+D2). These are the tests that tell us whether participants recognize the incentive (to/to not) manipulate information in the presence of outcome blinding. These will be tested using within-subject non-parametric tests, as well as regressions.

(2) Let B3* and D3* be the proportion of participants who choose to manipulate information in the respective third decision conditional on 'optimal' behavior in the first and second decision. (1-B3*)>(D3*) tells us that the expansion of the encrypted labels successfully reduces the incidence of manipulation, even after accounting for any increased manipulation arising from the expansion cueing or nudging participants. These will be tested using between-subject non-parametric tests, as well as regressions.
Primary Outcomes (explanation)
B3* and D3* are constructed conditional on having behaved rationally/optimally in B1, B2 and D1 and D2 respectively. In addition, we will also condition it on questions relating to their understanding of the first two decisions. The goal here is to compare B3 and D3 behavior among participants who recognize they (should/should not) manipulate when there are only two labels.

Secondary Outcomes

Secondary Outcomes (end points)
We will also test B2>B3 which is a test of whether the expansion reduces manipulation (independent of whether they got B1 and B2 correct); and whether D3>D2, which is a test of whether expansion induces participants to manipulate even when they shouldn't. (B2+D2)>(B3+D3) then becomes a test of whether overall manipulation is reduced as a result of expansion. This is not a primary outcome because it potentially includes a sizable group of subjects who are confused by the incentives in even the first two decisions; one of the primary goals of the experiment is to test whether expansion reduces manipulation among those who recognize the incentive to manipulate even when there is outcome blinding. Hence even in these tests we will condition the sample to those who have appropriate understanding of their first two decisions (but not on their actual behavior).
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Please refer to intervention details above.
Experimental Design Details
Not available
Randomization Method
Randomization is done through Prolific's recruitment.
Randomization Unit
By survey treatment.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
No clustering.
Sample size: planned number of observations
500-600
Sample size (or number of clusters) by treatment arms
250-300 observations per treatment, 500-600 total.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Ethics and Integrity, Deakin University Research and Innovation
IRB Approval Date
2026-05-13
IRB Approval Number
2026/HE000393
Analysis Plan