Predictive algorithms and decision-making

Last registered on July 14, 2021
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Pre-Trial

Trial Information

General Information

Title
Predictive algorithms and decision-making
RCT ID
AEARCTR-0007859
Initial registration date
July 14, 2021

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
July 14, 2021, 10:15 AM 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
Hyunjin Kim
Affiliation
Harvard University
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Other Primary Investigator(s)

PI Name
Xi Kang
PI Affiliation
INSEAD
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Additional Trial Information

Status
In development
Start date
2021-07-19
End date
2025-12-31
Keywords
Behavior, Firms & Productivity
Additional Keywords
algorithms, digitization, decision-making
JEL code(s)
Secondary IDs
Abstract
We explore the impact of predictive algorithms as decision aids.
External Link(s)

Registration Citation

Citation
Kang, Xi and Hyunjin Kim. 2021. "Predictive algorithms and decision-making." AEA RCT Registry. July 14. https://doi.org/10.1257/rct.7859-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
We provide analysts with algorithmic recommendations.
Intervention Start Date
2021-07-19
Intervention End Date
2022-01-21

Primary Outcomes

Primary Outcomes (end points)
Reasoning quality and confidence in decision
Primary Outcomes (explanation)
Please see analysis plan attached.

Secondary Outcomes

Secondary Outcomes (end points)
Please see analysis plan attached.
Secondary Outcomes (explanation)
Please see analysis plan attached.

Experimental Design

Experimental Design
Decision-makers are randomly assigned to one of three experimental groups.
Experimental Design Details
Not available
Randomization Method
Randomization done in office by a computer
Randomization Unit
Individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
99
Sample size: planned number of observations
99
Sample size (or number of clusters) by treatment arms
49 analysts control, 50 analysts treatment (25 in treatment 1, 25 in treatment 2)
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
INSEAD Institutional Review Board
IRB Approval Date
2021-06-14
IRB Approval Number
2020-111A
Analysis Plan

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