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Salience and Preference Reversals (Online Study)
Last registered on July 08, 2020


Trial Information
General Information
Salience and Preference Reversals (Online Study)
Initial registration date
July 06, 2020
Last updated
July 08, 2020 5:07 PM EDT
Primary Investigator
University of Zurich
Other Primary Investigator(s)
PI Affiliation
University of Zurich
Additional Trial Information
In development
Start date
End date
Secondary IDs
The preference reversal phenomenon refers to a pattern of decisions under risk where decision makers explicitly value long-shot lotteries above more moderate ones but then choose the latter, in contradiction with Expected Utility Theory. The phenomenon is typically demonstrated in paradigms involving pairs of lotteries consisting of a relatively safe lottery, called the P-bet (for "probability"), and a riskier lottery offering a larger prize (a long shot), called the $-bet. Decision makers often choose the P-bet in the direct choice task, but explicitly value the $-bet above the P-bet, in contradiction with the most basic tenets of decision theories under risk, and specifically with the indifference between a lottery and its certainty equivalent. This phenomenon reveals an inconsistency between elicitation methods which should be equivalent.

Salience theory postulates that reversals occur because certain states are overweighted due to their salience. To test this hypothesis we conduct an online experiment analyzing decisions under risk while implementing two treatments which manipulate salience. See the hypothesis in the attached pdf.
External Link(s)
Registration Citation
Alós-Ferrer, Carlos and Alexander Ritschel. 2020. "Salience and Preference Reversals (Online Study)." AEA RCT Registry. July 08. https://doi.org/10.1257/rct.5988-1.0.
Experimental Details
Intervention Start Date
Intervention End Date
Primary Outcomes
Primary Outcomes (end points)
Proportion of predicted preference reversals.
Primary Outcomes (explanation)
Proportion of predicted reversals: Proportion of the $-bets being evaluated higher than the P-bets, among all pairs such that the P-bet was chosen.
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
Classical preference reversal experiment conducted online where subjects choose and evaluate lottery pairs. There will be two between-subject treatments for evaluating lotteries.
Experimental Design Details
Each subject faces 24 decisions constructed using 8 lottery pairs out of a pool of 32 lottery pairs (4 predetermined sets of 8 lotteries each, with each subject randomly assigned to one of the four sets of lotteries). Each of the 32 lottery pairs consists of a $-bet and a P-bet. Each bet will have two possible outcomes, expressed in ECU (experimental currency units). Payments will be exchanged to GBP at the end of the experiment (exchange rate 1 ECU = 0.4 GBP). The $-bet consists of a high outcome (>11 ECU) with a low probability (<45%) and a second, low outcome, while the P-bet consists of a moderate outcome (<9 ECU) with high probability (>60%) and a second, low outcome. Each subject faces three decisions for each fixed lottery pair: (i) choose between the two lotteries (Choice), (ii) evaluate the $-bet, or (iii) evaluate the P-bet. Choices and evaluations are interspersed according to a pseudo-randomized sequence. Subjects are randomly assigned to one of two (between-subject) treatments for the evaluation of lotteries: Isolation and Joint evaluation. In the Isolation Treatment, the $-bet and P-bet are evaluated without the other lottery (the one not being evaluated) being present. Black circles (as placeholders) are shown where the probabilities and outcomes of the alternative lottery would have been presented. In the Joint Treatment, the $-bet will be evaluated while a perturbed version of the P-bet is present, and vice versa (see below for the perturbed versions). For payment, one decision (a choice or an evaluation) will be chosen randomly for each subject and either the chosen lottery will be played out (if a choice decision is sampled) or the Becker-DeGroot-Marshak mechanism will be applied (if an evaluation decision is sampled), that is, either the lottery will be played out or a monetary value will be paid. Perturbed lotteries are designed in such a way that the higher outcome of each lottery is changed by less than 3% and the lower outcome is changed accordingly to keep a similar expected value of the lottery. The perturbation does not change the salience ranking of the states. Salience states were calculated using the same salience function as in Bordalo et al. (2012). See the attached pdf for the equation. The Lotteries and the different outcomes are separated by two axes. Lotteries are presented on the left and right side of the screen, visually separated by a vertical line. The two possible states of a lottery (outcome with probability) are visually separated by a horizontal line. For each subject, the position of the $-bets and P-bets and the position of the higher outcome (HO) for each lottery is counterbalanced: {$ left/P right; $ right/P left}×{HO($) top; HO($) bottom}×{HO(P) top; HO(P) bottom}.
Randomization Method
Randomization by computer will determine the individual's evaluation treatment.
Randomization Unit
On the individual.
Was the treatment clustered?
Experiment Characteristics
Sample size: planned number of clusters
128 individuals.
Sample size: planned number of observations
256 individuals (proportions of predicted preference reversals for each individual).
Sample size (or number of clusters) by treatment arms
128 individuals.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
We aim to collect data that could detect a small-to-medium-sized effect (Cohen's d=.35), which requires a sample size of N=102 in each treatment for a one-tailed Mann-Whitney-Wilcoxon test with alpha=.05 and a power of .80. We increase this number to 128 participants to be a multiple of 32 (=4 Sets of lotteries x 8 counterbalancing measures). A total of 128 subjects per treatment yields an actual power of .87 (with d=.35 and alpha=.05). A sample of N=128 in each treatment is able to detect a minimal effect of d=.31 (with power of .80 and alpha=.05). We will, therefore, collect data from 256 participants in total (128 in each treatment).
Supporting Documents and Materials

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IRB Name
Human Subjects Committee of the Faculty of Economics, Business Administration, and Information Technology, University of Zurich
IRB Approval Date
IRB Approval Number
OEC IRB # 2020-040
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)