Preference reversals and preference uncertainty

Last registered on June 29, 2021


Trial Information

General Information

Preference reversals and preference uncertainty
Initial registration date
October 23, 2020

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
October 26, 2020, 2:16 PM EDT

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

Last updated
June 29, 2021, 7:30 AM EDT

Last updated is the most recent time when changes to the trial's registration were published.



Primary Investigator


Other Primary Investigator(s)

Additional Trial Information

In development
Start date
End date
Secondary IDs
We will explain the entire choice pattern in experiments of preference reversals by combining deliberate randomization with valuation uncertainty. In the experiment, we elicit each subject’s randomization probability between P-bet and $-Bet and valuation uncertainty over the two bets. We also repeat the standard preference reversal experiment. We will show that the choice probabilities calculated from randomization probability and valuation uncertainty can predict the subject’s choice pattern in the preference-reversal experiment.
External Link(s)

Registration Citation

Shi, Liu. 2021. "Preference reversals and preference uncertainty." AEA RCT Registry. June 29.
Experimental Details


Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
Randomization probability between P-bet and $-bet
Valuation uncertainty over the two bets
Standard choice pattern in the preference-reversal experiment.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
There are four parts in our experiment. In the first part, we repeat the standard preference reversal experiment. In the second part and third part, we elicit the subjects’ randomization probability and valuation uncertainty. We further elicit decision confidence using standard self-reports. In the last part, we obtain the subjects’ loss aversion and ambiguity attitudes.
Experimental Design Details
Tasks in this experiment are mainly choices between lotteries (P-bet and $-bet) and sure payments. There are four parts in this experiment.

Part 1. Repeat standard Preference Reversal experiment.
This part has two tasks, the choice task and the valuation task. In the choice task, we let subjects choose between P-bet and $-bet. In the valuation task, we elicit valuation by choices between P-bet (or $-bet) and a series of sure payments. The changing of sure payments is based on the bisection method.

Part 2. Randomization choice
In this part, we measure the subjects' choice uncertainty. Different from the choice task in part 1, subjects can assign the randomization probability to combine P-bet and $-bet. To decide the payment in this part, the computer will randomly choose one lottery according to the randomization probability. The closer randomization probability subjects assign to both lotteries, the larger choice uncertainty they perceive.

Part 3. Measure uncertainty interval
In this part, we measure the subjects' uncertainty interval. Similar to part one, we still let subjects choose between P-bet (or $-bet) and a series of sure payments. But we add an option "keep both options" in each choice. After the subject chooses this option, the computer will randomly choose one option to determine her payment. Our basic idea is that subjects will choose "keep both options" when they perceived uncertainty. The payment intervals where subjects choose "keep both options" are their uncertainty interval.

Part 4. Measure ambiguity aversion and loss aversion
We measure the subjects' ambiguity attitude and loss attitudes by multiple price list (MPL).
Randomization Method
randomization done in laboratory by computer
Randomization Unit
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
150 college students
Sample size: planned number of observations
150 college students
Sample size (or number of clusters) by treatment arms
150 college students
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Institutional Review Boards (IRBs)

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IRB Approval Date
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Post Trial Information

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Data Publication

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Program Files

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Reports, Papers & Other Materials

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Reports & Other Materials