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Investigating Motivations for Information Avoidance - The Role of Certainty, Rewards and Overconfidence
Last registered on October 24, 2018


Trial Information
General Information
Investigating Motivations for Information Avoidance - The Role of Certainty, Rewards and Overconfidence
Initial registration date
October 20, 2018
Last updated
October 24, 2018 4:21 PM EDT
Primary Investigator
Norwegian School of Economics
Other Primary Investigator(s)
PI Affiliation
Norwegian School of Economics
Additional Trial Information
Start date
End date
Secondary IDs
This study investigates different motivations for information avoidance. For this purpose, participants take part in an intelligence test in which their results are compared to other participants. All participants are member of the same cohort of a Business School. Participants are asked to guess the share of others who performed the test better than them and are paid based on the accuracy of that guess. They are informed that they can find out the exact share after they made their initial estimate. If they find out their initial guess, they can revise their previous guess, ensuring full payout from the experiment but potentially also finding out unpleasant information about themselves.
We investigate the fraction of participants who actively avoid that information, thereby foregoing earnings from the experiment. We furthermore test for different motivations that these participants could have for that decision.
External Link(s)
Registration Citation
Ay, Fehime and Stefan Meißner. 2018. "Investigating Motivations for Information Avoidance - The Role of Certainty, Rewards and Overconfidence." AEA RCT Registry. October 24. https://doi.org/10.1257/rct.3473-1.0.
Former Citation
Ay, Fehime, Stefan Meißner and Stefan Meißner. 2018. "Investigating Motivations for Information Avoidance - The Role of Certainty, Rewards and Overconfidence." AEA RCT Registry. October 24. http://www.socialscienceregistry.org/trials/3473/history/36207.
Experimental Details
Intervention Start Date
Intervention End Date
Primary Outcomes
Primary Outcomes (end points)
We investigate two key variables in the experiment. In one treatment, participants are asked to state their willingness to pay to receive the information about the share of participants that did better than them. Here, they can avoid that information by stating a willingness of zero. In the other trial, participants can state their willingness to pay to avoid that information. They will receive a bonus earning of 50 Norwegian Kroner. If they are willing to pay this full amount, they can ensure that they will not find out the information.
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
First, participants take an IQ test and they are informed that that test was taken from a longer test. After taking the test they are asked to guess their rank compared to their peers in the session and if their guess is correct they are going to win 80 NOK. After making their guess they are assigned to two treatments randomly: costly information and costly avoidance. In both treatments a Becker-DeGroot-Marschak(BDM) (Becker et al., 1964) auction takes place to implement the participants’ decision. The aim of having a BDM auction is to elicit their real preferences and willingness to pay for that. A bonus payment of 50NOK will be introduced and they are asked to submit how much they would be willing to pay for their decision (getting/avoiding information)to be implemented. The submitted price is compared to a randomly chosen game price in the next stage and if the submitted price is higher participant pays the game price and the decision is implemented. If the auction is lost, participant’s decision is not implemented and bonus payment will be added to final payoff. If the participant gets the information (with or without choosing it)there is a chance to revise the guess. At the end of the game they will receive a payment from their guess (0or80) and the rest of the bonus payment after the BDM results.
Experimental Design Details
Randomization Method
Randomization is performed by zTree within sessions.
Randomization Unit
Was the treatment clustered?
Experiment Characteristics
Sample size: planned number of clusters
400 Individuals
Sample size: planned number of observations
400 Individuals
Sample size (or number of clusters) by treatment arms
Each treatment contains 200 individuals
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB Name
IRB Board of the Norwegian School of Economics
IRB Approval Date
IRB Approval Number
Analysis Plan
Analysis Plan Documents
Pre-Analysis-Plan: Fehime Ceren Ay and Stefan Meissner NHH

MD5: e1b61c944bdcdd88a1687b88ff233148

SHA1: c0920b9f077c3059d4a9d93994335edb84ac2f88

Uploaded At: October 20, 2018

Post Trial Information
Study Withdrawal
Is the intervention completed?
Is data collection complete?
Data Publication
Data Publication
Is public data available?
Program Files
Program Files
Reports, Papers & Other Materials
Relevant Paper(s)