Strategic interactions with the Assistant of an Algorithm

Last registered on May 23, 2022
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Pre-Trial

Trial Information

General Information

Title
Strategic interactions with the Assistant of an Algorithm
RCT ID
AEARCTR-0009469
Initial registration date
May 19, 2022

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
May 23, 2022, 5:19 PM EDT

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

Locations

Country
China
Region
Wuhan

Primary Investigator

Name
Lian XUE
Affiliation
Wuhan University
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Other Primary Investigator(s)

PI Name
Lijia Wei
PI Affiliation
Wuhan University
Contact Investigator
PI Name
Zhengqing Gui
PI Affiliation
Wuhan University
Contact Investigator
PI Name
Lu Bai
PI Affiliation
Wuhan University
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Additional Trial Information

Status
In development
Start date
2022-05-01
End date
2022-08-31
Keywords
Behavior
Additional Keywords
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
In real life, people face strategic situations when there are trade-offs between payoff maximization and sequential reasoning. The decision we made in these situations depends on our own strategic reasoning, and the advice we received from others. In this project, we will examine the situation when this advice is generated by an algorithm. Furthermore, we will explore the mechanism behind algorithm trust in strategic interactions.
External Link(s)

Registration Citation

Citation
Bai, Lu et al. 2022. "Strategic interactions with the Assistant of an Algorithm." AEA RCT Registry. May 23. https://doi.org/10.1257/rct.9469-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
We have baseline treatment where subjects play a strategic game without the assistance of an algorithm;
And Unilateral treatments and Bilateral treatments where subjects play the same strategic game however with the assistance of an algorithm.
Intervention Start Date
2022-05-22
Intervention End Date
2022-07-21

Primary Outcomes

Primary Outcomes (end points)
The degree of algorithm trust across treatments.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Participants matched in pairs will play the repeated centipede game in two forms, the exponential sum game, and the constant sum game.
In benchmark treatment, participants play games without the assistance of an algorithm.
In the treated group, either one or both players will receive a piece of advice from an algorithm.
Experimental Design Details
In the additional treatments where we explore the mechanisms behind the algorithm trust, subjects will be provided with additional information and the mechanism to process the information during their decision-making process.
In the expert treatment, subjects will receive the same piece of advice but will be told the advice is based on the experimenters' calculation.
Randomization Method
Randomization was done in the lab by computer programs.
Randomization Unit
Individual
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
600 subjects
Sample size: planned number of observations
600 subjects
Sample size (or number of clusters) by treatment arms
100 per treatments
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Center of Behavior and Economic Research
IRB Approval Date
2022-05-20
IRB Approval Number
IRB202200139
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