Disentangle the mechanisms underlying correlation sensitivity in decision making

Last registered on April 26, 2024
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Pre-Trial

Trial Information

General Information

Title
Disentangle the mechanisms underlying correlation sensitivity in decision making
RCT ID
AEARCTR-0013395
Initial registration date
April 24, 2024

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
April 26, 2024, 12:38 PM 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
Jiakun Zheng
Affiliation
Aix-Marseille School of Economics
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Other Primary Investigator(s)

PI Name
Moritz Loewenfeld
PI Affiliation
Toulouse School of Economics
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Additional Trial Information

Status
In development
Start date
2024-04-29
End date
2024-05-12
Keywords
Behavior
Additional Keywords
correlation-sensitivity, probability dominance, regret
JEL code(s)
C91; D81; D91
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
A recent study by Loewenfeld and Zheng (2023) documented that individuals are correlation-sensitive and exhibit behavior patterns compatible with decreasing sensitivity to payoff differences (DSPD). These behavioral regularities can be explained by two competing theories: correlation-sensitive preferences, as characterized by Lanzani (2022), with concavity, or probability dominance, as advocated by Diecidue et al. (2020). In this study, we conduct a controlled lab experiment with students to examine the mechanisms underlying correlation sensitivity in decision making.
External Link(s)

Registration Citation

Citation
Loewenfeld, Moritz and Jiakun Zheng. 2024. "Disentangle the mechanisms underlying correlation sensitivity in decision making." AEA RCT Registry. April 26. https://doi.org/10.1257/rct.13395-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
Participants are given the task of making a series of binary decisions, where they must choose between Lotteries A and B. The outcomes of each option are uncertain and determined by computer-generated random draws. The experiment utilizes both between-group and within-group designs. Within-group comparisons involve participants selecting between the same Lotteries A and B, but with varying correlations between them, allowing for a distinction between two competing theories. Additionally, one lottery is adjusted to be first-order stochastically dominant by adding a fixed premium to all its outcomes, enabling the assessment of preference strength. Between-group comparisons expose participants to lotteries with either a high (7/8) or low (3/4) number of states of nature, allowing for the investigation of whether correlation preferences are influenced by the complexity of the decision tasks. Following the main experiment, participants are asked if they wish to learn the specific correlation between Lotteries A and B while keeping it unknown, and then proceed to make binary decisions again. Here, we adopt a within-subject design where subjects experimental three experimental conditions: revealing correlation structure is free, costs them 2% of the lotteries' expected payoffs, or helps them win that amount. Subsequently, participants are required to complete an anonymous questionnaire, providing basic individual information such as age and gender.
Intervention Start Date
2024-04-29
Intervention End Date
2024-05-12

Primary Outcomes

Primary Outcomes (end points)
Choice_{DSPD}: subjects make binary choices as predicted by correlation-sensitive preferences with concavity.
Choice_{PD}: subjects make binary choices as predicted by probability dominance.
Choice_{Reveal}: Subjects want to reveal correlation structure when it is hidden.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Participants are given the task of making a series of binary decisions, where they must choose between Lotteries A and B. The outcomes of each option are uncertain and determined by computer-generated random draws. The experiment utilizes both between-group and within-group designs. Within-group comparisons involve participants selecting between the same Lotteries A and B, but with varying correlations between them, allowing for a distinction between two competing theories. Additionally, one lottery is adjusted to be first-order stochastically dominant by adding a fixed premium to all its outcomes, enabling the assessment of preference strength. Between-group comparisons expose participants to lotteries with either a high (7/8) or low (3/4) number of states of nature, allowing for the investigation of whether correlation preferences are influenced by the complexity of the decision tasks. Following the main experiment, participants are asked if they wish to learn the specific correlation between Lotteries A and B while keeping it unknown, and then proceed to make binary decisions again. Here, we adopt a within-subject design where subjects experimental three experimental conditions: revealing correlation structure is free, costs them 2% of the lotteries' expected payoffs, or help them win that amount. Subsequently, participants are required to complete an anonymous questionnaire, providing basic individual information such as age and gender.
Experimental Design Details
Not available
Randomization Method
randomization done by a computer.
Randomization Unit
individual.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
200 full-time students from Renmin University of China
Sample size: planned number of observations
200 full-time students from Renmin University of China
Sample size (or number of clusters) by treatment arms
100 students per treatment.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Lab of National Governance and Development, Renmin University of China
IRB Approval Date
2024-04-17
IRB Approval Number
RUCecon-202404-2
Analysis Plan

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