Decision making following advice from humans and AI

Last registered on August 20, 2026
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Pre-Trial

Trial Information

General Information

Title
Decision making following advice from humans and AI
RCT ID
AEARCTR-0019360
Initial registration date
August 11, 2026

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 20, 2026, 8:29 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
Linda Thunstrom
Affiliation
University of Wyoming
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Other Primary Investigator(s)

PI Name
Collin DePaemelere
PI Affiliation
University of Wyoming
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PI Name
Johanna Mollerstrom
PI Affiliation
George Mason University
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Additional Trial Information

Status
In development
Start date
2026-08-20
End date
2026-11-13
Keywords
Behavior
Additional Keywords
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This study examines how advice affects decision-making in an allocation task and how people evaluate the social appropriateness of different allocation choices. It also examines whether these effects differ depending on whether the advice comes from another person or from AI.
External Link(s)

Registration Citation

Citation
DePaemelere, Collin, Johanna Mollerstrom and Linda Thunstrom. 2026. "Decision making following advice from humans and AI." AEA RCT Registry. August 20. https://doi.org/10.1257/rct.19360-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
The project consists of 3 related studies.
Study 1 (N=950): Participants are asked to rate the social appropriateness of dictator game (DG) allocations (splits of $10 between the proposer and recipient, in $1-increments). Participants are randomized into one of 5 conditions:
- control (no advice): the participant is asked to rate the social appropriateness of the possible splits in the DG
- selfish-human: the participant needs to rate the social appropriateness in the scenario where the proposer in the DG gets advice from a human to choose a $8/$2 split between themselves/recipient
- fair-human: the participant is asked to rate the social appropriateness in the scenario where the proposer in the DG gets advice from a human to choose a $5/$5 split between themselves/recipient
- selfish-AI: the participant is asked to rate the social appropriateness in the scenario where the proposer in the DG gets advice from an AI to choose a $8/$2 split between themselves/recipient
- fair-AI: the participant is asked to rate the social appropriateness in the scenario where the proposer in the DG gets advice from an AI to choose a $5/$5 split between themselves/recipient

Study 2 (N=1200): Participants are asked to choose dictator game (DG) allocations (splits of $10 between themselves and a recipient, in $1-increments). Participants are randomized into one of 5 conditions:
- control (no advice): the participant gets no advice before choosing their allocation
- selfish-human: before choosing their allocation, the participant gets advice from a human to choose a $8/$2 split between themselves/recipient
- fair-human: before choosing their allocation, the participant gets advice from a human to choose a $5/$5 split between themselves/recipient
- selfish-AI: before choosing their allocation, the participant gets advice from an AI to choose a $8/$2 split between themselves/recipient
- fair-AI: before choosing their allocation, the participant gets advice from an AI to choose a $5/$5 split between themselves/recipient

Study 3 (N=120): All participants are asked to predict the allocation most commonly chosen by participants in the selfish-AI-treatment in Study 2.
Intervention Start Date
2026-08-20
Intervention End Date
2026-11-13

Primary Outcomes

Primary Outcomes (end points)
Study 1: social appropriateness ratings
Study 2: amount kept by proposers in the DG
Study 3: predicted amount kept by proposers in Study 2
Primary Outcomes (explanation)
N/A

Secondary Outcomes

Secondary Outcomes (end points)
N/A
Secondary Outcomes (explanation)
N/A

Experimental Design

Experimental Design
The project consists of 3 related studies.
Study 1 (N=950): Participants are asked to rate the social appropriateness of dictator game (DG) allocations (splits of $10 between the proposer and recipient, in $1-increments). Participants are randomized into one of 5 conditions:
- control (no advice): the participant is asked to rate the social appropriateness of the possible splits in the DG
- selfish-human: the participant needs to rate the social appropriateness in the scenario where the proposer in the DG gets advice from a human to choose a $8/$2 split between themselves/recipient
- fair-human: the participant is asked to rate the social appropriateness in the scenario where the proposer in the DG gets advice from a human to choose a $5/$5 split between themselves/recipient
- selfish-AI: the participant is asked to rate the social appropriateness in the scenario where the proposer in the DG gets advice from an AI to choose a $8/$2 split between themselves/recipient
- fair-AI: the participant is asked to rate the social appropriateness in the scenario where the proposer in the DG gets advice from an AI to choose a $5/$5 split between themselves/recipient

Study 2 (N=1200): Participants are asked to choose dictator game (DG) allocations (splits of $10 between themselves and a recipient, in $1-increments). Participants are randomized into one of 5 conditions:
- control (no advice): the participant gets no advice before choosing their allocation
- selfish-human: before choosing their allocation, the participant gets advice from a human to choose a $8/$2 split between themselves/recipient
- fair-human: before choosing their allocation, the participant gets advice from a human to choose a $5/$5 split between themselves/recipient
- selfish-AI: before choosing their allocation, the participant gets advice from an AI to choose a $8/$2 split between themselves/recipient
- fair-AI: before choosing their allocation, the participant gets advice from an AI to choose a $5/$5 split between themselves/recipient

Study 3 (N=120): All participants are asked to predict the allocation most commonly chosen by participants in the selfish-AI-treatment in Study 2.
Experimental Design Details
Not available
Randomization Method
By computer.
Randomization Unit
Individual.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
None.
Sample size: planned number of observations
Study 1: 950 individuals Study 2: 1200 individuals Study 3: 120 individuals
Sample size (or number of clusters) by treatment arms
Study 1: 950/5=190
Study 2: 1200/5=240
Study 3: 120
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 Wyoming IRB
IRB Approval Date
2026-07-02
IRB Approval Number
2026-205
Analysis Plan