Strategic interactions with the Assistant of an Algorithm

Last registered on May 23, 2022


Trial Information

General Information

Strategic interactions with the Assistant of an Algorithm
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.



Primary Investigator

Wuhan University

Other Primary Investigator(s)

PI Affiliation
Wuhan University
PI Affiliation
Wuhan University
PI Affiliation
Wuhan University

Additional Trial Information

In development
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
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

Bai, Lu et al. 2022. "Strategic interactions with the Assistant of an Algorithm." AEA RCT Registry. May 23.
Experimental Details


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
Intervention End Date

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
Was the treatment clustered?

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)

Institutional Review Boards (IRBs)

IRB Name
Center of Behavior and Economic Research
IRB Approval Date
IRB Approval Number


Post Trial Information

Study Withdrawal

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Is the intervention completed?
Data Collection Complete
Data Publication

Data Publication

Is public data available?

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials