Algorithmic advice

Last registered on August 02, 2019


Trial Information

General Information

Algorithmic advice
Initial registration date
August 02, 2019

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 02, 2019, 3:38 PM EDT

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



Primary Investigator

WHU - Otto Beisheim School of Management

Other Primary Investigator(s)

PI Affiliation
University of Cologne

Additional Trial Information

In development
Start date
End date
Secondary IDs
Algorithms based on enormous amounts of data enable new ways of supporting decision making by integrating and improving data-driven predictions. The present project investigates whether and how algorithmic assistance influences advice-giving in ethical dilemmas. The current project is based on outcomes of a prior RCT pre-registered under the file AEARCTR-0003529.
External Link(s)

Registration Citation

Irlenbusch, Bernd and Rainer Michael Rilke. 2019. "Algorithmic advice." AEA RCT Registry. August 02.
Former Citation
Irlenbusch, Bernd and Rainer Michael Rilke. 2019. "Algorithmic advice." AEA RCT Registry. August 02.
Experimental Details


Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
The fraction of subjects who overreport the outcome of a fair die roll.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We aim to utilize the infrastructure of an online labor market place to run a conflict of interest game where participants receive assistance from algorithms (Cain et al., 2005). Participants are randomly assigned the role of the sender or the role of the receiver. The sender observes the outcome of a random computerized six-sided die roll and has to send a message about the outcome of the die roll to the receiver. The receiver does not know the true outcome of the dice roll; he gets the message and has to choose a number between one and six. The sender’s payment is increasing in the number chosen by the receiver, whereas the receiver only receives a payment when he chooses the number that matched the actual die roll. This game gives the sender the opportunity to over-report the actual die roll outcome in order to gain an additional payoff.
Experimental Design Details
In our experiment senders will get a prediction about the behavior of the receivers. This prediction is generated based on real data from a different pre-registered project (AEARCTR-0003529). Our four treatments vary the source (human x machine) and quality (honest x selfish) of advice senders in our game receive.
Randomization Method
Randomization is done by online survey software (Questback).
Randomization Unit
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
Sample size: planned number of observations
We plan to gather 900 senders and 900 receivers.
Sample size (or number of clusters) by treatment arms
~230 per treatment
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Institutional Review Boards (IRBs)

IRB Name
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