Cognitive Noise in Belief Formation

Last registered on May 11, 2021

Pre-Trial

Trial Information

General Information

Title
Cognitive Noise in Belief Formation
RCT ID
AEARCTR-0007653
Initial registration date
May 11, 2021
Last updated
May 11, 2021, 11:49 AM EDT

Locations

Region

Primary Investigator

Affiliation
University of Bonn

Other Primary Investigator(s)

Additional Trial Information

Status
In development
Start date
2021-05-17
End date
2021-06-30
Secondary IDs
Abstract
The study's objective is to test whether there is more cognitive noise in subjects' belief formation in an environment with more uncertainty.
External Link(s)

Registration Citation

Citation
Mauersberger, Felix. 2021. "Cognitive Noise in Belief Formation." AEA RCT Registry. May 11. https://doi.org/10.1257/rct.7653-1.0
Sponsors & Partners

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Experimental Details

Interventions

Intervention(s)
I conduct a laboratory experiment using the subject pool of the BonnEconLab at the University of Bonn. Due to the COVID-19 crisis, the experiment will be conducted online.

See the experimental design for details.
Intervention Start Date
2021-05-17
Intervention End Date
2021-06-30

Primary Outcomes

Primary Outcomes (end points)
Noise in decision-making
Primary Outcomes (explanation)
Noise is measured by the distance between the Bayesian forecast and the subject’s stated forecast.

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
For each subject, the computerized experiment consists of two blocks, each containing eight subsequent trials. The computer presents the blocks to the subject in random order. On each trial, a value of x is chosen from a known prior distribution. This prior distribution is the same across the 16 trials (and is the same across blocks). The computer then draws a random observation y1 from a normal distribution with mean x and variance s^2. The variance s^2 is the same for all eight trials in a block but different across blocks. The subject is shown the value of y1 and has to forecast what the NEXT independent draw y2 from that same distribution (with mean x) will be. They make their forecast, then the second draw occurs. The subject receives a score, which depends on the squared distance between a subject’s forecast and the outcome of the second independent draw. The closer the subject’s forecast is to the actual outcome y2, the higher the score. Then, for the subsequent trial, a new value of x is drawn from the same prior distribution, and the whole procedure is repeated.
Experimental Design Details
I adopt a within-subject design with two blocks that are presented to the subject in random order:

a) Block "Low Variance":

The variance of the random variables y1 and y2 is set to s^2=40,000, implying a standard deviation of s=200.

b) Block "High Variance":

The variance of the random variables y1 and y2 is set to s^2=360,000, implying a standard deviation of s=600.

Randomization Method
The computer randomly determines a) the order in which the two blocks appear; and b) which series of stochastic realizations subjects face. Half of the subjects face the same series of stochastic realizations since this allows cross-sectional comparison. The other half faces different, randomly selected series of stochastic realizations.
Randomization Unit
Order of the blocks within subjects
Series of stochastic outcomes that subjects will face
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
N/A
Sample size: planned number of observations
140 subjects completing the experiment; 16 observations per subject
Sample size (or number of clusters) by treatment arms
70 out of 140 subjects face the low variance block first, while the other 70 subjects first face the high variance block.
70 out of 140 subjects face the same series of stochastic realizations for the purpose of comparability (so 35 of these subjects face the low variance block first; the other 35 subjects face the high variance block first). The other 70 subjects face different series of stochastic realizations (again, 35 of these subjects face the low variance block first; the other 35 subjects face the high variance block first).
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
The sample size allows detecting medium-sized effects (Cohen’s d = 0.5) at a power of 80%.
IRB

Institutional Review Boards (IRBs)

IRB Name
German Association for Experimental Economic Research e.V.
IRB Approval Date
2021-05-10
IRB Approval Number
Vuj7iT5G

Post-Trial

Post Trial Information

Study Withdrawal

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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