Decision-Making under Algorithmic Recommendation

Last registered on September 30, 2023
View Trial History

Pre-Trial

Trial Information

General Information

Title
Decision-Making under Algorithmic Recommendation
RCT ID
AEARCTR-0011532
Initial registration date
August 07, 2023

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
August 10, 2023, 1:34 PM EDT

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

Last updated
September 30, 2023, 12:09 AM EDT

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

Locations

Country
United States of America
Region

Primary Investigator

Name
Ariel Listo
Affiliation
University of Maryland, College Park
Contact Primary Investigator

Other Primary Investigator(s)

Additional Trial Information

Status
On going
Start date
2023-08-07
End date
2024-03-31
Keywords
Behavior
Additional Keywords
Ambiguity, Risk, Algorithmic Recommendation
JEL code(s)
D81, D90
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
I study the role of ambiguity preferences in predicting take-up of algorithmic (i.e., personalized) recommendations. In a two-part experiment, I first elicit individuals's preferences towards risk and ambiguity. In Part 2, I ask participants to make choices over a menu of risky prospects after I provide a recommendation (or not) based on their treatment assignment. I assign individuals to one of two control groups (no recommendation or randomly created recommendation) or to one of two treatment groups (personalized recommendation with no information or personalized recommendation with information). The difference between the two treatments is only the information provided to individuals on how the recommendation is constructed; both recommendations are of similar quality (i.e., based on the individual's risk preferences elicited in Part I).
External Link(s)

Registration Citation

Citation
Listo, Ariel. 2023. "Decision-Making under Algorithmic Recommendation." AEA RCT Registry. September 30. https://doi.org/10.1257/rct.11532-1.2
Sponsors & Partners

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Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2023-08-07
Intervention End Date
2023-09-30

Primary Outcomes

Primary Outcomes (end points)
Take-up of personalized recommendation
Primary Outcomes (explanation)
Take-up of personalized recommendation refers to whether individuals choose one of the alternatives listed as "Recommended" at the top of the menu of all alternatives.

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
In a two-part experiment, I first elicit individuals's preferences towards risk and ambiguity. In Part 2, I ask participants to make choices over a menu of risky prospects after provide a recommendation (or not) based on their treatment assignment. I assign individuals to one of two control groups (no recommendation or randomly created recommendation) or to one of two treatment groups (personalized recommendation with no information or personalized recommendation with information). The difference between the two treatments is simply the information provided to individuals on how the recommendation is constructed, while both recommendations are of similar quality (i.e., based on the individual's risk preferences elicited in Part I).
Experimental Design Details
Randomization Method
Randomization done by Qualtrics
Randomization Unit
Randomization conducted at the individual level
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
600 Prolific participants
Sample size: planned number of observations
600 Prolific participants
Sample size (or number of clusters) by treatment arms
150 participants in Control I, 150 participants in Control II, 150 participants in Treatment I, and 150 participants in Treatment II
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
University of Maryland College Park (UMCP) IRB
IRB Approval Date
2023-09-20
IRB Approval Number
2050491-3
Analysis Plan

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